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Momentum

15 papers in the last four weeks, up 400% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 86

Jun 27, 2026cs.LG

MALOQ: Massively Accelerated Learning of Operators for Quantum Transport

Machine-learned (ML) operator models can be trained to predict density functional theory (DFT) Hamiltonian/density matrices at significantly reduced computational cost, thus extending electronic-structure calculations to previously unfeasible scales. Here, we introduce MALOQ (Massively Accelerated Learning of Operators for Quantum Transport), an application built to train on and predict electronic-structure matrices for systems made of few to 100k atoms, described by large basis sets, and covering a wide range of atomic elements. Based on a state-of-the-art, SO(2)-equivariant backbone architecture, MALOQ provides (i) custom data-processing kernels to handle high-rank Hamiltonian matrix data and (ii) a scalable edge-wise distribution of atomic graph(s). Trained on the largest molecular Hamiltonian datasets available today, it reduces time-per-epoch by over 30% compared to a molecule-wise-distributed framework, and enables inference on material graphs of arbitrary size. We demonstrate scalable training and inference for 3,000-12,000 atoms on the Alps supercomputer, up to 192 GPUs and 256 GPUs, respectively.

Manasa Kaniselvan, Alexander Maeder, Denghui Lu +2

D-ITET, ETH Zurich, Zurich, Switzerland

Quantum Chemistry High-Performance Computing Distributed Training Computational Materials Science

Jun 22, 2026cs.RO

A Reduced Order Model for Emergent Mechanics in Woven Systems

Woven structures exhibit rich mechanical behaviors including anisotropic stiffness, shear-induced locking, and crimp interchange that emerge purely from the geometric arrangement of individual weavers rather than from constituent material properties. Existing models either homogenize these interactions or resolve them at prohibitive computational cost. We introduce a reduced-order model that bridges this gap by representing individual weaver interactions through a system of nodes and four physically interpretable stiffness elements capturing axial deformation, in-plane uncrimping, inter-weaver shear, and frictional slip. Eigenvalue analysis of the unit cell confirms that the lowest-energy deformation modes correspond directly to known weave-specific phenomena, and that each element is necessary for a complete kinematic and mechanistic description. Element stiffness parameters are calibrated against empirical three-point bending and shear data, achieving agreement within 5% across varied weaver widths and spacings. The validated model is then applied to demonstrate capabilities beyond the reach of continuum approaches including: the emergent Poisson's response arising from crimp interchange, stepwise force reduction during progressive weaver pullout, stress localization under three distinct tearing configurations, and programmable mechanical anisotropy through spatially graded weaver stiffness. The physical transparency and computational efficiency of the framework position it as a practical tool for the analysis and design of woven architected materials with programmable mechanical response.

Anvay A. Pradhan, Evgueni T. Filipov, Talia Y. Moore

Department of Mechanical Engineering, University of Michigan, Ann Arbor, Michigan, 48109, USA · Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, Michigan, 48109, USA · Department of Robotics, University of Michigan, Ann Arbor, Michigan, 48109, USA +1

Structural Mechanics Materials Science Reduced-Order Modeling Computational Materials Science

Jun 21, 2026cs.CE

Deep material network for homogenization of piezoelectric composites

Piezoelectric composites are widely used in sensors, actuators, transducers, and energy-harvesting devices because their effective electromechanical performance can be tailored by combining constituent phases and microstructural architecture. However, conventional computational homogenization based on direct numerical simulation (DNS) is computationally expensive, particularly for multiscale simulations and material design tasks that require repeated homogenization analyses. To address this limitation, this work proposes a piezoelectric deep material network (PDMN) to efficiently homogenize two-phase piezoelectric composites. The proposed framework embeds the governing electromechanical homogenization relations directly into the network architecture, yielding a physics-informed, semi-analytical surrogate that explicitly captures the two-way coupling between the mechanical and electrical fields across constituent phases. The network is trained offline on linear electroelastic datasets and, through a fully coupled Newton--Raphson solution with a consistent electromechanical tangent, subsequently used for efficient online prediction under broader constitutive settings, including nonlinear electroelasticity and history-dependent responses. The framework is validated on two-phase composites of polyvinylidene fluoride (PVDF) and lithium niobate (LiNbO

_3

) with reversed phase arrangements under nonlinear electroelastic loading, and on a viscoelastic--piezoelectric composite exhibiting coupled stress relaxation. Numerical examples show that the proposed PDMN achieves high predictive accuracy while reducing the computational cost by more than three orders of magnitude compared with DNS. The proposed framework, therefore, provides an efficient and reliable surrogate for the multiscale analysis and design of piezoelectric composites.

Ting-Ju Wei, Yen-Ming Lu, Chuin-Shan Chen

Department of Civil Engineering National Taiwan University Taipei, Taiwan · Department of Civil Engineering Department of Materials Science and Engineering National Taiwan University Taipei, Taiwan

Neural Surrogate Modeling Materials Science Physics-Informed ML Computational Materials Science

Jun 20, 2026cs.LG

Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials

Reinforcement learning from verifiable rewards (RLVR) has driven rapid progress in mathematical and code reasoning, but when extended to science, existing benchmarks do not decompose what generalizes: do gains reflect structural transfer, property transfer, or memorization? We introduce Mat-Pref, a benchmark of 10,837 ionic-substitution questions across 11 inorganic structure families, grounded in density functional theory calculations from the Materials Project, with three evaluation splits that isolate in-distribution performance, generalization to entirely held-out structure families, and cross-property transfer: applying band-gap reasoning to hosts seen during training only through formation-energy supervision. Four zero-shot frontier models (70-671B parameters) remain in the 33-54% range on every split, confirming that scale alone does not resolve the compositional chemical reasoning this task demands. A two-stage pipeline of supervised fine-tuning followed by Group Relative Policy Optimization (GRPO) lifts Qwen3-8B to 65.2% in-distribution and 71.6% on held-out families, exceeding zero-shot Qwen3-235B by over 20 percentage points on both structural-generalization splits. Self-consistency sampling shows that the SFT policy can already produce correct answers but cannot reliably surface them as the modal response; GRPO reshapes the distribution so that correct answers become modal rather than merely reachable, and this sharper commitment is visible mechanistically: logit lens analysis reveals a

{\sim}

20pp advantage in answer crystallization at the critical decision layer. We formalize this observation as a distractor-permutation consistency metric under which GRPO narrows the gap between lenient scoring (at least one permutation correct) and strict scoring (all permutations correct) from 24.0 to 14.3 percentage points.

Sarrah R. Mikhail Leung, Taehan Kim, Jeongbin Park

University of California, Berkeley · 2Machine Learning at Berkeley · University of Michigan

Compositional Reasoning RL for Language Model Reasoning Materials Property Prediction Reinforcement Learning with Verifiable Rewards Computational Materials Science

Jun 19, 2026cond-mat.mtrl-sci

Computational references are not experiments: pre-registered validation of machine-learned sodium-cathode voltages

Machine-learning screens for battery materials are trained and judged almost entirely against computed reference voltages, and those references carry their own systematic errors. We report a case in which this matters quantitatively: our own screening stack (a graph-network voltage screen, a prior-art triage layer, and a local PBE+U bench) fails pre-registered validation against experiment-anchored literature values. Verdict thresholds, failure modes, and the primary metric were committed before analysis. On an operator-audited set of known Na-ion cathodes (n = 6 after one documented exclusion; verdict unchanged at n = 7), the raw held-out mean absolute error was 0.67 V, the pre-registered conservative metric, the upper 95% confidence bound of the cross-validated bias-corrected error, was 1.09 V, and the residual was strongly voltage-dependent (r = -0.94), so no additive calibration is valid. On the two compounds where prediction, database reference, and experiment could all be compared, the Materials Project PBE+U reference sat about 0.54 V below measurement: the reference, not the model, dominated the error. A prior-art screen found at least 70% of the targeted Na substitution space already published. We retire the screen, bound what "verified" means for our DFT ledger, and pre-register a calibration audit of it against four benchmark Li couples.

Krishna Teja Vepa

Independent Researcher†

Materials Property Prediction Virtual Screening Model Calibration Benchmark Validity Computational Materials Science

Jun 17, 2026physics.optics

Equivariant Graph Neural Networks Improve Optical Spectra Prediction for Materials Screening

Scalable prediction of optical spectra is a critical component of high-throughput materials screening for optoelectronic applications such as solar cells. Existing surrogate models are trained on spectra computed from lower levels of theory or rely on rotation-invariant scalar features, limiting their geometric expressiveness. We explore the use of equivariant graph neural networks for optical spectra prediction, adapting GotenNet to this task and evaluating it on multiple datasets including a recently published collection of 10,533 structures with spectra computed at the level of the random phase approximation (RPA). The proposed model outperforms the current state of the art, with the largest gains in the 0-8 eV range and on predicting the static real permittivity, both of particular relevance for thin-film optics.

Kasper Helverskov Petersen, François R J Cornet, Martin Ovesen +3

Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark · Department of Physics, Technical University of Denmark, Kongens Lyngby, Denmark

Materials Property Prediction Equivariant GNNs Computational Materials Science

Jun 15, 2026cond-mat.mtrl-sci

InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery

Inverse materials design starts from target functionality and searches for structures that can realize it. Its value in closed-loop discovery depends not only on prediction performance, but also on whether expensive first-principles results are independently validated, provenance-recorded, and admitted as feedback only when evidence is sufficient. This is especially important for composite properties such as carrier mobility, where a final scalar value hides intermediate quantities, fit quality, convergence history, and workflow assumptions. Here we present InvDesMobility, a reliability-gated first-principles feedback framework that integrates multi-agent automated DFT, evidence stratification, generative structure proposal, acquisition ranking, and auditable release. Using 516 2DMatPedia-derived candidates, the workflow produced 280 QC-passed materials and 573 retained carrier-direction seed channels after channel-level reliability gating. These records were split into two feedback objects: relaxed structures updated the generative model, while retained mobility channels trained the acquisition model and set validation priority. Over multiple iterations, InvDesMobility screened 2.4 x 10^6 structures, submitted 102 candidates for DFT validation, and retained 86 reliability-gated generated channels across 41 formulas. Overall, the main contribution is not a fixed list of high-mobility materials, but a transferable feedback contract that makes closed-loop inverse design both useful and auditable when learning from expensive calculated properties. All source data, retained feedback records, and workflows are available at https://github.com/DreamLufei/invDesMobility, with an accompanying evidence website at https://dreamlufei.github.io/invDesMobility/.

Wen-Kao Li, Ze-Feng Gao, Peng-Jie Guo +2

School of Physics and Key Laboratory of Quantum State Construction (Ministry of Education), Renmin University of China, Beijing 100872, China

Materials Property Prediction Autonomous Scientific Discovery Inverse Design Computational Materials Science

Jun 14, 2026cs.LG

Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials

Accurate interatomic potentials enable molecular dynamics of materials, molecules, and interfaces beyond density-functional-theory length and time scales. Equivariant neural network potentials have improved the representation of local geometry. However, their deployable energy surfaces ultimately manifest through invariant scalar channels, whose aggregation and spectral resolution remain comparatively underexamined. Here we use Physics-Aware Neighborhood (PAN) pooling and Physics-Guided Spectral (PGS) mixers as controlled scalar-pathway probes: lightweight, symmetry-preserving modifications that act only on

\ell=0

channels while leaving the equivariant tensor backbone unchanged. Using MACE as a high-body-order mechanistic scaffold, PAN adds coordination-sensitive amplitude modulation, whereas PGS augments edge and readout scalar features with radial and tapered spectral bases. Across metallic Ag, covalent Si, a short-range ionic LiF/Li--F subset, and MD17/rMD17 molecules, this scalar-pathway correction reduces MACE force errors by 22--27% and energy errors by 19--22%; on systems with stress labels, stress errors decrease by 27--28%, at approximately 5% additional inference-FLOPs cost. Directionally consistent gains in Allegro and NequIP further indicate that the correction is portable across distinct short-range equivariant backbones, although effect sizes remain architecture-dependent. These results identify scalar-pathway fidelity as a practical design dimension for short-range equivariant interatomic potentials.

Jia Bi, Alin Marin Elena, Samuel Pinilla

Science and Technology Facilities Council, Harwell Campus, Didcot, OX11 0QX, Oxfordshire, United Kingdom. · Science and Technology Facilities Council, Keckwick Lane, Daresbury, WA4 4AD, Cheshire, United Kingdom. · Diamond Light Source, Harwell Science and Innovation Campus,2026 Didcot, OX11 0QX, Oxfordshire, United Kingdom.

Machine Learning Interatomic Potentials Equivariant GNNs Computational Materials Science

Jun 12, 2026cs.LG

A Hybrid GNN-FEM Framework for Phase-Field Fracture Simulation. Physics-Preserving Hybridization for Generalizable Surrogate Modeling

Scientific machine learning (SciML) has emerged as a promising approach for accelerating simulations of complex physical systems, yet achieving physically consistent and generalizable predictions for nonlinear, history-dependent problems remains a central challenge. In this study, we propose a hybrid GNN--FEM framework for efficient and generalizable phase-field fracture modeling. While phase-field approaches provide a robust variational framework for simulating complex crack evolution, their high computational cost limits practical applications because they require solving coupled, nonlinear, and history-dependent systems within an incremental finite element procedure. To address this challenge, a graph neural network surrogate is integrated into the conventional staggered scheme, replacing the phase-field update at each load increment while retaining the FEM-based displacement solver to enforce mechanical equilibrium and boundary conditions. By preserving the incremental solution structure, the framework remains consistent with history-dependent fracture evolution without requiring the surrogate to approximate the full solution trajectory. This selective surrogate strategy emphasizes the identification of a physically meaningful and incrementally structured learning target, rather than relying on brute-force data generation to learn the full fracture process. The proposed framework achieves strong generalization across varying geometries, loading conditions, material properties, and discretizations through dimensionless feature design, a graph-based formulation on mesh-based domains, and a physics-informed loss derived from the governing phase-field equation. Numerical experiments demonstrate that the hybrid approach reduces computational cost while maintaining accuracy compared with conventional FEM, and exhibits robust predictive performance across diverse problem settings.

Hyeonbin Moon, Yongjin Choi, Seunghwa Ryu

Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · KAIST InnoCORE PRISM-AI Center, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea · Department of AX, College of AI, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea

Structural Mechanics PDE Surrogate Modeling Physics-Informed ML Computational Materials Science

Jun 12, 2026cond-mat.mtrl-sci

XRDiff: Crystal Structure Prediction from Powder X-Ray Diffraction Data Using Diffusion Models

Determining the crystal structure of a material from its powder X-ray diffraction (PXRD) pattern is a central challenge in materials science. PXRD is an accessible and widely used characterization technique, yet recovering the atomic structure from diffraction data requires solving an underdetermined inverse problem due to the loss of phase information. Generative modeling can provide a prior over atomic structure and learn the mapping from PXRD patterns to crystal structures via simulated structure-spectrum pairs. We present XRDiff, a diffusion model that recovers crystal structures from PXRD given either the stoichiometry or, in a more challenging setting, the elemental constituents and total number of atoms in the unit cell. We evaluate on datasets where each stoichiometry has multiple polymorphs and all polymorphs of a given composition are held out together, ensuring that high performance reflects genuine use of the diffraction signal. XRDiff achieves strong structure recovery rates on simulated benchmarks, indicating that the model learns a spectrum-to-structure mapping precise enough to differentiate between polymorphs. To address generalization to experimental data, we compare a full-spectrum encoding against an encoding based on peak descriptors. The peak-based encoding generalizes substantially better, outperforming even a model trained on full spectra with augmentations fitted to the experimental noise distribution. These results demonstrate that representations robust to the noise and artifacts present in real-world PXRD offer a practical and scalable path toward closing the simulation-to-experiment gap, enabling zero-shot crystal structure solution from experimental PXRD with full or partial chemical composition input.

Nofit Segal, Mingda Li, Benjamin Kurt Miller +1

Department of Materials Science and Engineering, MIT, Cambridge, MA, USA. · Department of Nuclear Science and Engineering, MIT, Cambridge, MA, USA. · FAIR, Meta, San Francisco, CA, USA.

Crystal Structure Prediction Diffusion Models Computational Materials Science

Jun 9, 2026cond-mat.dis-nn

Magnetic HIP-NN for spin dynamics in disordered itinerant magnets

We present a magnetic extension of the Hierarchically Interacting Particle Neural Network (HIP-NN) that enables large-scale simulations of electron-mediated spin dynamics in disordered itinerant magnets. The resulting magnetic HIP-NN (mHIP-NN) incorporates rotationally invariant spin correlations directly into hierarchical message-passing layers, enabling the network to learn emergent magnetic energy landscapes and effective local fields from coupled geometric-spin environments while preserving spin-rotation symmetry. As a benchmark application, we consider structurally disordered itinerant

s

-

d

exchange models in which the effective magnetic forces arise dynamically from the instantaneous electronic structure and are computationally prohibitive to evaluate using conventional exact-diagonalization-based approaches. We show that mHIP-NN accurately reproduces the local torques governing Landau-Lifshitz-Gilbert dynamics and faithfully captures the nonequilibrium evolution of spatial spin correlations following thermal quenches. Our results establish symmetry-aware hierarchical message-passing networks as an efficient and scalable framework for large-scale simulations of frustrated itinerant spin systems and nonequilibrium magnetic dynamics. More broadly, because the learned energy functional remains fully differentiable with respect to both atomic coordinates and spin variables, the framework also provides a natural foundation for spin-dependent interatomic potentials and coupled atom-spin dynamics.

Supriyo Ghosh, Yunhao Fan, Sheng Zhang +2

Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA · Department of Chemistry, University of Chicago, Chicago, IL 60637, USA · Theoretical Division and CNLS, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

Equivariant Neural Networks Message Passing Neural Networks Materials Science Computational Materials Science

Jun 5, 2026cond-mat.mtrl-sci

Agentic multi-fidelity learning of quasiparticle and excitonic properties

Many-body GW-Bethe-Salpeter equation calculations are essential for accurate simulations of electronic structure and optical properties in modern low-dimensional nanomaterials. However, these methods are computationally demanding and can exhibit localized numerical instabilities or convergence failures that are difficult to detect within high-throughput workflows. We introduce an agent-guided multi-fidelity framework for correcting GW-Bethe-Salpeter excited-state landscapes in strained MoS2-WS2 bilayers. Across stacking registries, strain branches and reciprocal-space samplings, the workflow identifies spike-like excursions, near-zero-gap collapse and cross-fidelity inconsistencies associated with fragile long-wavelength dielectric screening. A structural agent evaluates calculations by assigning confidence weights and selectively using a small number of high-accuracy reference points. Machine learning models then transfer information across related systems and apply Gaussian process corrections to recover improved quasiparticle gaps and exciton binding energies, with calibrated uncertainty estimates. The approach corrects numerically induced artifacts without erasing physical strain dependence and substantially improves agreement with higher-fidelity references relative to a no-agent baseline. These results show that reliable surrogate learning for excited-state materials requires explicit diagnosis of numerical fragility, not direct interpolation of raw first-principles data points. The proposed framework is readily transferable to other optoelectronic nanomaterials characterized by strong quantum confinement, such as quantum dots, nanoribbons, layered two-dimensional semiconductors, and hybrid perovskite nanostructures.

Arnab Neogi, Aaron Forde, Christopher A. Lane +2

Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA · Center for Integrated Nanotechnologies, Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

AI Agents for Scientific Discovery Materials Property Prediction Quantum Many-Body Physics Computational Materials Science

Jun 5, 2026cs.LG

Decision-Aware Evaluation of Physics-Informed Surrogates

Physics-informed machine learning is often assessed by curve error, although engineering use depends on downstream decisions: ranking candidates, avoiding infeasible designs and limiting regret. We introduce pinn-gym, an open benchmark for material-conditioned lattice design that couples a transparent reduced-order crush-and-impact oracle with five printable polymer cards, dimensionless force-response targets and a protocol spanning curve fidelity, physical admissibility, top-k retrieval and mass regret. Across per-material, pooled and cross-material settings, low nRMSE is frequently insufficient to identify useful design selections. Physics-informed losses alter trade-offs rather than monotonically improving all metrics, and dimensionless conditioning improves comparability without making transfer symmetric. The benchmark is not a certified material model; within the released oracle, candidate generator and material cards, pinn-gym provides a reproducible testbed for evaluating PIML surrogates as decision systems rather than curve predictors alone.

Daniel Cieślak, Andrzej Czyżewski

Gdańsk University of Technology, Gdańsk, Poland

Surrogate Modeling Benchmark Design Physics-Informed ML Computational Materials Science

Jun 2, 2026cs.LG

Derivative Informed Learning of Exchange-Correlation Functionals

Machine-learned (ML) exchange-correlation (XC) functionals aim to replace human-designed density functional approximations by learning directly from reference data, but they still do not consistently outperform traditional

\mathcal{O}(N^4)

-scaling hybrid functionals. We study a hybrid-distillation setting in which

\mathcal{O}(N^3)

-scaling ML-XC functionals are trained to reproduce B3LYP/def2-SVP targets. We introduce Derivative Informed XC-Loss (DI-Loss), a loss that incorporates additional information from the reference hybrid functional by supervising first and second derivatives of the energy on the Grassmannian of admissible density matrices. Rather than only matching the self-consistent fixed point, DI-Loss aligns the local first- and second-order response of the learned functional with that of the target functional. Across four evaluated architectures, DI-Loss consistently improves the main energy metrics. Averaged uniformly across architectures, the total-energy MAE decreases by 66% relative to energy and density supervision alone. The density-sensitive mean-field energy metric

E_ρ

improves from

1.2

to

0.8

mEh on average, while dipole and

\mathcal{L}_2

density errors do not improve uniformly. We further show that densities from the distilled functionals reduce hybrid-functional SCF iterations by up to 50%. In downstream TDDFT calculations, Hessian supervision improves excited-state predictions, with XCdiff reducing the mean excitation-energy MAE by 19 - 35%.

Eike S. Eberhard, Luca A. Thiede, Abdul Aldossary +5

Technical University of Munich, Munich, Germany · Munich Data Science Institute, Munich, Germany · Munich Center for Machine Learning, Munich, Germany +4

Quantum Chemistry Knowledge Distillation Computational Materials Science

May 31, 2026cs.CE

Machine Learning Surrogate Modeling for Homogenization of Hyperelastic Materials with Boolean Microstructures

Data-driven surrogate models are an alternative to numerical homogenization of heterogeneous materials. In this contribution, a supervised learning approach is presented for predicting effective Lamé parameters of hyperelastic composites from low-dimensional microstructural descriptors. The data set is based on previously published numerical homogenization results for ensembles of two-phase stochastic microstructures generated by planar Boolean models, covering variations of inclusion shape, phase contrast, and area fraction; see Brändel, Brands, Maike, Rheinbach, Schröder, Schwarz and Stoyan (2022). A neural network is trained on combinations of scalar and curve-valued statistical descriptors, including the area fraction, a derived scalar shape descriptor

τ

, the two-point correlation function

S_2(r)

, and the lineal-path function

\ell(z)

. Additional data representing limiting cases of the parameter space are incorporated to stabilize training and improve extrapolation behavior. The surrogate is evaluated by leave-one-grain-type-out cross-validation in order to assess generalization to unseen grain geometries. Numerical results demonstrate that additional descriptors can reduce relative errors. A predictor trained with

τ

and

S_2(r)

provides a compact representation with good quantitative accuracy and regular dense response behavior. Adding the lineal-path function

\ell(z)

further reduces the error at the available data points, indicating that it is a promising additional descriptor; however, dense post-training response evaluations show that improved pointwise accuracy does not automatically guarantee physically admissible behavior between sampled parameter values. This motivates future work on physically constrained surrogate models, loss formulations, bounded output parametrizations, and a more systematic representation of curve-valued geometric descriptors.

Matthias Brändel, Oliver Rheinbach

Technische Universität Bergakademie Freiberg, Institut für Numerische Mathematik und Optimierung, Fakultät für Mathematik und Informatik, 09596 Freiberg, Germany.

Neural Surrogate Modeling Hyperelasticity Materials Property Prediction Materials Science Computational Materials Science

May 30, 2026cond-mat.mtrl-sci

Benchmark Dataset for Catalysis on 2D MXenes

Merging first-principles calculations with machine learning (ML), we aim to accelerate the exploration of catalytic behaviour in novel materials. We focus on two-dimensional (2D) Ti

_2

CT

_y

MXenes, whose versatile surface chemistry makes them particularly compelling candidates for catalysis. Resolving their composition and structure under realistic conditions exceeds the reach of standard density functional theory (DFT) due to computational cost. To address this challenge, we generate a comprehensive dataset of 50,000 DFT calculations for training and 10,000 for testing, encompassing both Ti

_2

CT

_y

MXene configurations and molecular systems, along with an additional test dataset with 1000 genuinely new, larger systems to investigate how well models generalise. We train and validate widely used and competitive machine learning interatomic potential (MLIP) models, including EquiformerV2, MACE, MatRIS, and UPET, that accurately predict atomic forces and formation energies -- quantities that DFT must repeatedly compute for structural and catalytic investigations -- for these 2D materials. This combined DFT-ML framework achieves computational acceleration on the order of approximately

1-4 \cdot 10^3

(on a CPU) while maintaining desired-level accuracy (approximately +/-

10

meV/A for forces and approximately +/-

1

meV for per-atom energies), paving the way for more efficient investigations of MXene catalytic behaviour. Moreover, we perform an extensive qualitative evaluation of the trained models, showcasing the importance of comprehensive simulation-based comparison beyond benchmark metrics. The dataset and the trained models with the code are available at https://huggingface.co/datasets/CatalystAnonymous/catalyst_mxenes.

Pavlo Melnyk, Anmar Karmush, Mårten Wadenbäck +4

Computer Vision and Learning Systems, Department of Electrical Engineering (ISY) & AI4X, Linköping University (LiU), 58183 Linköping, Sweden. · Materials Design Division, Department of Physics, Chemistry and Biology (IFM), and Wallenberg Initiative Materials Science for Sustainability (WISE); Linköping University (LiU), 58183 Linköping, Sweden. · Wallenberg Initiative Materials Science for Sustainability (WISE); Linköping University (LiU), 58183 Linköping, Sweden.

Machine Learning Interatomic Potentials Benchmark Design Materials Property Prediction Materials Science Computational Materials Science

May 29, 2026cond-mat.soft

Discovering Thermodynamically Admissible Dissipation Potentials via Grammar-Based Symbolic Regression

Constitutive laws for inelastic materials must satisfy strict thermodynamic admissibility requirements, yet current data-driven approaches sacrifice interpretability, even when formal guarantees are provided by physics-encoded architectures. We propose a symbolic regression framework for the data-driven discovery of dissipation potentials governing the evolution of internal variables within the Generalized Standard Materials (GSM) formalism. Starting from the Clausius--Duhem inequality, we enforce the thermodynamic requirements, convexity and non-negativity, that the dual dissipation potential must satisfy to guarantee non-negative mechanical dissipation. These requirements are formulated in the general subdifferential setting, encompassing rate-dependent (viscoelastic) and viscoplastic dissipative mechanisms, including potentials with genuine elastic domains, within a unified framework. Candidate potentials are generated by a composition-extended convexity-preserving grammar that guarantees thermodynamic admissibility \emph{by construction}. The framework is validated on synthetic datasets spanning Newtonian, power-law, and Bingham viscoplastic ground truths under process and measurement noise, and on experimental oscillatory shear measurements of a synthetic elastomer across multiple strain amplitudes and frequencies, where the discovered potentials reproduce the amplitude-dependent softening of the dynamic moduli and outperform a calibrated linear Zener baseline.

Federico Califano, Jacopo Ciambella

Materials Science Computational Materials Science Symbolic Regression

May 27, 2026cs.LG

LEIA: Learned Environment for Interactive Architected Materials

World models have enabled interactive exploration of game environments and robotic manipulation, but physical engineering remains beyond their reach: real materials exhibit nonlinear constitutive laws, carry history-dependent internal state, undergo inertial dynamics, and may possess hierarchical structures spanning multiple length scales. We present LEIA (Learned Environment for Interactive Architected materials), a world model that lets engineers apply boundary conditions step by step and observe the resulting deformation and stress fields in real time. LEIA handles large three-dimensional unstructured meshes and generates autoregressive responses to user-specified loading. We introduce MicroPlate, a benchmark of architected plates spanning two regimes of microstructure modeling: architected lattices that resolve microstructure explicitly through three-dimensional geometry, and a homogeneous plate where microstructural change is modeled implicitly through internal degrees of freedom. MicroPlate is used to assess LEIA alongside four baseline methods across both regimes. Finally, we demonstrate that LEIA enables efficient candidate generation and ranking for fast surrogate-guided search for de novo designs of architected materials, with stress-accurate candidate ranking validated by finite element ground truth.

Haiqian Yang, Yuan Cao, Markus J. Buehler

Unreasonable Labs · Mountain View, CA 94043, USA

Neural Surrogate Modeling World Model Learning Structural Mechanics Materials Property Prediction Interactive World Models Computational Materials Science

May 25, 2026cond-mat.mtrl-sci

AutoDFT: A Closed-Loop Multi-Agent Framework for Autonomous DFT Calculations

Density functional theory (DFT) serves as the basis for computational discovery in materials science and chemistry, yet each calculation demands extensive human effort: adjusting algorithms when convergence stalls, revising plans when unexpected physics emerges, and inserting steps as intermediate results reshape the problem. Existing LLM-based agents automate only the initial planning stage, producing a full execution plan upfront and leaving all subsequent adaptation to hand-crafted rules. As a result, these workflows remain fragile, do not generalize well beyond pre-planned scenarios, and often require expert intervention when failures or unexpected intermediate results require changes to the calculation path. Here, we introduce AutoDFT, a closed-loop multi-agent framework that embeds LLM reasoning into every stage of the DFT lifecycle, where a strategic planner produces a skeletal plan of step objectives; a step planner generates numerical parameters just in time from preceding results; and a monitor-recover-reflect cycle diagnoses failures, repairs them, and revises the plan when the evidence justifies it. We demonstrate both breadth and depth: breadth on VASPBench, a purpose-built benchmark spanning 34 tasks and 9 DFT calculation types, where AutoDFT achieves 94.1% task-level success with GPT-5.2; and depth on established materials databases, where AutoDFT produces quantitatively reliable property predictions across electronic, magnetic, and energetic properties. By closing the loop between planning and execution, AutoDFT enables experimentalists without deep computational expertise to obtain reliable first-principles results.

Penghui Yang, Zhonghan Zhang, Yue Li +6

Nanyang Technological University, Singapore · Singapore Management University

Multi-Agent LLM Systems Scientific Workflow Automation LLM Agent Planning Computational Materials Science

May 23, 2026cond-mat.mtrl-sci

SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling

We present a probabilistic modeling framework for incorporating small-scale spatial heterogeneity into macroscopic descriptions of material behavior for polycrystalline metallic materials. Spatially heterogeneous material state fields are represented using probability density functions (PDFs), providing a principled statistical description of microstructural variability and state evolution across different computational polycrystalline realizations. The framework is built on the inverse identification of a probabilistic transport model, formulated as a Liouville equation with an unknown drift term. To enable accurate, stable, and interpretable inference of this drift field in high-dimensional, transport-dominated settings, we develop a Separable Probability Learning Technique via Physics-Informed Neural Networks (SPLIT-PINN). This method incorporates a marginal-correction drift decomposition, orthogonality constraints, and residual-based adaptive training to enhance well-posedness, numerical stability, and physical consistency without imposing restrictive parametric assumptions. Using SPLIT-PINN, the drift field governing the temporal evolution of joint state PDFs is inferred directly from data. After benchmark validation, the framework is applied to physical computational datasets describing the evolution of polycrystalline microstructural states, including von Mises stress, dislocation density, and equivalent plastic strain rate. The learned Liouville model, trained on a single dataset, is subsequently used in forward predictions of the temporal evolution of joint and marginal PDFs for multiple unseen polycrystal realizations. Quantitative comparisons with reference PDFs demonstrate that the proposed framework yields accurate and robust probabilistic predictions and generalizes effectively across datasets.

Pouria Behnoudfar, Deekshith Naidu Ponnana, Noah J. Schmelzer +6

Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA · Department of Civil Engineering, Johns Hopkins University, Baltimore, MD, USA · Materials Physics and Applications Division, Los Alamos National Laboratory, Los Alamos, NM, USA +2

Physics-Informed ML Computational Materials Science

May 19, 2026cond-mat.str-el

Representability-Aware Neural Networks for Reduced Density Matrices: Application to Fractional Chern Insulators

We develop a representability-aware and interpolable neural network (NN) framework for predicting two-particle reduced density matrices (2-RDMs). The NN incorporates a subset of representability conditions through its architecture and loss function, and can operate on different momentum meshes, enabling evaluating the representability conditions across multiple meshes, which we call interpolated representability condition. The framework can be used either to predict 2-RDMs on large momentum meshes by interpolating exact results from small meshes, or as a variational 2-RDM ansatz optimized by energy minimization on arbitrary meshes. We apply this approach to the fractional Chern insulator in the one-band projected model of twisted bilayer MoTe

_2

at twist angle

3.89^\circ

and hole filling

2/3

. Trained on exact-diagonalization (ED) 2-RDMs from meshes with

12

or

18

momentum points using six different NN architectures, the best NN is the residual multilayer perceptron, which predicts the

6\times6

2-RDM with

97.07\%-98.18\%

accuracy relative to the ED 2-RDM but predicts an energy

77.353

meV above ED ground-state energy. We then variationally optimize the NN on several meshes including

6\times6

, predicting a

6\times 6

energy of just

0.104

meV below ED while maintaining

98.94\%-98.96\%

accuracy. Compared with the conventional boundary-point semidefinite programming, which gives an energy

5.560

meV below ED with

96.40\%-98.94\%

accuracy, the NN achieves a more accurate energy and similar accuracy while using only less than 1/20 as many parameters. Eventually, we add a symmetric mesh of

48

momentum points to the variational optimization of the NN, and provide a prediction of the many-body ground-state energy and the many-body quantum metric on that mesh.

Justin B. Hart, Awwab A. Azam, Thomas Li +4

Department of Physics, University of Florida, Gainesville, FL, USA · Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, USA · Palo Alto High School, Palo Alto, CA, USA +4

Quantum Machine Learning Quantum Many-Body Physics Computational Materials Science

May 18, 2026physics.chem-ph

Harnessing AtomisticSkills for Agentic Atomistic Research

Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabilities, scaling monolithic agents to manage the rigor and complexity of atomistic research remains a challenge. Here, we introduce AtomisticSkills, an open-source harness framework that empowers general-purpose AI coding agents to conduct atomistic research across materials science, chemistry, and drug discovery. By hierarchically decomposing scientific workflows into agent skills and tools, AtomisticSkills provides agents with modular, extensible, and plug-and-play research capabilities. The framework integrates more than 100 human-curated multidisciplinary skills, including database access, thermodynamics and kinetics modeling, and diverse simulation engines employing machine learning interatomic potentials (MLIPs) and density functional theory (DFT). We validate its functional coverage against scientific literature and demonstrate robust orchestration capabilities across diverse scientific campaigns: generative design of Li-ion solid-state electrolytes, high-throughput screening of metal-organic frameworks for CO2 capture, autonomous MLIP benchmarking and fine-tuning, multi-stage structure-based virtual screening for drug design, multimodal X-ray diffraction pattern analysis, and screening of Fe-oxide catalysts for oxygen evolution reaction. AtomisticSkills provides a critical agent infrastructure towards building fully autonomous AI scientists.

Bowen Deng, Bohan Li, Matthew Cox +20

Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemistry, Kookmin University, Seoul 02707, Republic of Korea +5

AI Agents for Scientific Discovery AI Coding Agents LLM Agent Orchestration Computational Materials Science

May 18, 2026cond-mat.mtrl-sci

Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches

Chemical disorder, originating from the mixed occupation of crystallographic sites by multiple elements, is widespread in alloys, ceramics, and compositionally complex materials, where short- and long-range orderings can strongly influence properties. A central obstacle is the representation gap between experiments and simulations: experiments often report disorder as partial occupancies and ensemble-averaged behaviors, whereas atomistic simulations and AI workflows usually require fully specified configurations. Tackling this gap requires computational methods that convert averaged disorder descriptions into representative configurational ensembles while balancing cost, bias, and fidelity. This challenge has become more urgent in AI-driven computational discovery, where ignoring disorder may cause AI workflows to misrank stability, misjudge novelty, and misdirect experiments with too-idealized representations. This Review highlights how classical and AI-driven methods can bridge this representation gap. We assess the strengths and limitations of approaches spanning mean-field theories, cluster expansion, quasi-random approximations, Monte Carlo, and emerging schemes powered by universal interatomic potentials and generative models. We further highlight how AI can accelerate classical computational schemes by lowering the cost of microstate evaluation, configurational exploration, and atomistic-to-thermodynamic closure. We also emphasize how AI can enable disorder-native capabilities, including workflow triage, ordering-sensitive and alchemical representations, generative models of disordered structures and distributions, and kinetics-aware disorder prediction. Together, this framework outlines a practical roadmap toward disorder-native AI, which can transform chemical disorder from a representational obstacle into a controllable variable for realistic AI-accelerated materials discovery.

Jiayu Peng, Peichen Zhong

Department of Materials Design and Innovation, University at Buffalo, Buffalo, NY 14260, USA · Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore

Crystal Structure Generation Computational Materials Science

May 15, 2026physics.chem-ph

Reweighting free energy profiles between universal machine learning interatomic potentials for fast consensus building

Free energy profiles serve as a fundamental bridge between microscopic atomic fluctuations and macroscopic thermodynamic observables. Estimating the free energy profile along a reaction coordinate, referred to as the potential of mean force (PMF), with density functional theory (DFT) accuracy is computationally expensive. Universal machine learning interatomic potentials (MLIPs) drastically reduce this cost, but their accuracy is strongly determined by their training data and hence can be uncertain for a given system. In this work, we present a systematic and scalable framework for reweighting PMFs, initially sampled with a single 'source' MLIP, across a representative suite of target MLIPs. Because traditional direct exponential reweighting fails for large system sizes due to low phase-space overlap between potentials, we deploy robust analytical corrections. Applying this to a complex 601-atom system of Li

^+

transport in a nanoconfined electrolyte, we demonstrate that a mean energy-gap approximation effectively bypasses statistical collapse, producing a highly stable PMF matching the target PMF. Using this approach, we recover high-fidelity target thermodynamics across multiple DFT reference levels (PBE+D3, PBE-sol, r

^2

SCAN,r

^2

SCAN-D4) at a fraction of the computational cost of full simulations. Furthermore, thermodynamic analysis reveals that the studied MLIPs partition into two distinct clusters driven by their training data. Our reweighting framework successfully recovers target thermodynamic properties--specifically, reaction and activation free energies--even when the phase-space overlap between potentials is critically low. Ultimately, this approach establishes a vital diagnostic protocol to achieve affordable cross-model consensus on materials chemistry properties without redundant, resource-intensive simulations.

Sauradeep Majumdar, Miguel Steiner, Johannes C. B. Dietschreit +4

Department of Material Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, 02139, United States. · Institute of Theoretical Chemistry, University of Vienna, Währinger Str. 17, 1090, Vienna, Austria. · Department of Physical and Macromolecular Chemistry, Charles University, Hlavova 8, Praha 2, 12800, Czech Republic.

Free Energy Calculation Machine Learning Interatomic Potentials Importance Sampling Computational Materials Science

May 14, 2026cs.LG

Crys-JEPA: Accelerating Crystal Discovery via Embedding Screening and Generative Refinement

De novo crystal generation seeks to discover materials that are not merely realistic, but also stable and novel. However, most existing generative models are trained to maximize the likelihood of observed crystals, which encourages samples to stay close to known materials yet not necessarily align with the criteria that matter in discovery. Our empirical analysis shows that current crystal generative models exhibit a clear conflict between stability and novelty: samples near the observed distribution tend to retain stability but offer limited novelty, whereas samples farther from it often lose stability rapidly. This suggests that the useful region for discovering crystals that are both stable and novel is extremely narrow. To move beyond this limitation, we introduce Crys-JEPA, a joint embedding predictive architecture for crystals that learns an energy-aware latent space preserving formation-energy differences. In this space, stability assessment can be reformulated as an embedding-based comparison against accessible training crystals, reducing the reliance on expensive energy evaluation and task-specific external references. Building on Crys-JEPA, we further develop a screening-and-refinement pipeline that identifies promising generated crystals and reintroduces them to refine the generative model. On MP-20 and Alex-MP-20 datasets, we achieve improvements over baselines up to 53.8% and 72.7% on V.S.U.N. metric, respectively.

Nian Liu, Nikita Kazeev, Stephen Gregory Dale +8

1National University of Singapore · 2Loyola Marymount University · 3New York University +1

Joint-Embedding Predictive Architecture Generative Modeling Crystal Structure Generation Computational Materials Science

May 14, 2026cond-mat.mtrl-sci

Agentic Design of Compositional Descriptors via Autoresearch for Materials Science Applications

Autoresearch offers a flexible paradigm for automating scientific tasks, in which an AI agent proposes, implements, evaluates, and refines candidate solutions against a quantitative objective. Here, we use composition-based materials-property prediction to test whether such agents can perform a task beyond model selection and hyperparameter optimization: the design of input descriptors. We introduce Automat, an autoresearch framework where a coding agent based on a large language model generates composition-only descriptors for chemical compounds and evaluates them using a random forest workflow. The agent is restricted to information derivable from chemical formulas and iteratively proposes, implements, and tests chemically motivated descriptor strategies. We apply Automat, with OpenAI Codex using GPT-5.5 as the coding agent, to the prediction of experimental band gaps in inorganic materials and Curie temperatures in ferromagnetic compounds. In both tasks, Automat improves over fractional-composition, Magpie, and combined fractional-composition/Magpie baselines, while producing descriptor families that are chemically interpretable. These results provide a demonstration that autoresearch agents can generate competitive, task-specific materials descriptors without manual feature engineering during the run. They also reveal current limitations, including descriptor redundancy, sensitivity to greedy feature expansion, and the need for explicit complexity control, descriptor pruning, and more sophisticated search strategies.

Matteo Cobelli, Stefano Sanvito

School of Physics and CRANN Institute, Trinity College, Dublin 2, Dublin, Ireland.

Materials Property Prediction Automated Feature Engineering Autonomous Research Agents Computational Materials Science

May 11, 2026cs.LG

Generating Symmetric Materials using Latent Flow Matching

Tackling the task of materials generation, we aim to enhance the previously proposed All-atom Diffusion Transformer (ADiT) by introducing SymADiT, a symmetry-aware variant. To do so, we use a representation of materials based on Wyckoff positions. We follow ADiT and perform generative modelling in latent space, adapted to our symmetry-aware representation. By forcing the output of the generative model to adhere to the symmetry restrictions imposed by the generated crystal's space group and each atom's Wyckoff-position, the generated materials exhibit more realistic symmetry properties. We benchmark our method against both symmetry-aware and symmetry-agnostic models for materials generation and show competitive performance, generating stable, symmetric materials with a simple Transformer architecture.

Anmar Karmush, Cedric Mathieu Brandenburg, Soheil Ershadrad +3

Department of Electrical Engineering (ISY) & AI4x, Linköping University · Department of Physics, Chemistry and Biology (IFM), Linköping University · Wallenberg Initiative Materials Science for Sustainability (WISE), Linköping University +1

Crystal Structure Generation Computational Materials Science

May 10, 2026physics.chem-ph

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators

Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retaining differentiability, a key property for materials design. Graph neural network (GNN)-based simulators have shown strong performance across a range of physical domains, including molecular dynamics. However, their reliance on temporal context for accurate prediction limits their use in inverse design settings, where simulations must be initialized from a single static configuration. Moreover, inverse design requires robust out-of-distribution (OOD) generalization, as candidate structures typically lie outside the training domain. Here, we address both challenges by introducing two complementary strategies that enable stable and accurate structure-only initialization of GNN-based simulations. To directly target OOD generalization, we propose an inference-time physics-based optimization framework that constrains model predictions to remain physically consistent during rollout. In addition, we introduce a differentiable, GNN-based barostat that enables accurate tracking of system dimensions and pressure, critical for capturing macroscopic responses and supporting OOD generalization. We evaluate these approaches in the context of uniaxial compression of disordered elastic networks spanning a broad range of geometries, Poisson ratios, and microscopic behaviors. We find that, together, these methods substantially improve rollout stability and enable reliable OOD generalization, including regimes with distinct, more complex dynamics than those in the training data. These results show that, when properly initialized and constrained, GNN-based simulators can serve as efficient and generalizable tools for materials discovery and structural optimization, advancing their use in materials, molecular, and dynamical system design.

S. A. Shteingolts, Salman N. Salman, Dan Mendels

The Wolfson Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, 32000, Israel

Graph Neural Networks OOD Generalization Physics-Informed ML Molecular Dynamics Simulation Physics-Based Simulation Computational Materials Science

May 8, 2026physics.comp-ph

Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics

Physics-informed operator learning is an attractive candidate for surrogate modeling of microstructures, especially in multiscale finite-element simulations. Its practical use, however, is often limited by the high cost of loss evaluation. We address this bottleneck by combining the Equilibrium Neural Operator (EquiNO) with the QR-based discrete empirical interpolation method (Q-DEIM). EquiNO learns only the modal coefficients of reduced displacement-fluctuation and first Piola-Kirchhoff stress representations built from periodic and divergence-free bases, thereby enforcing periodicity and mechanical equilibrium by construction. Q-DEIM then identifies a small set of spatial points through a column-pivoted QR factorization of the stress basis and restricts constitutive evaluations during training to these points alone. This makes full-batch second-order optimization practical for three-dimensional representative volume elements (RVEs). Homogenized first Piola-Kirchhoff stresses are recovered directly from the offline-averaged reduced stress modes, without the need to reconstruct the full stress field at inference time. We validate the framework on two three-dimensional finite-strain hyperelastic RVEs. Q-DEIM reduces the per-step training cost by roughly three orders of magnitude relative to full-field loss evaluation, while reduced homogenization achieves speed-up factors of order

10^3

to

10^4

over direct full-field computations. Despite relying on only a small number of offline snapshot loading paths for basis construction, the method accurately interpolates and extrapolates both microscopic stress fields and homogenized stresses, with prediction quality improving systematically as more snapshots are added.

Hamidreza Eivazi, Henning Wessels

Division Data-Driven Modeling of Mechanical Systems, Institute of Applied Mechanics, Technische Universität Braunschweig, Pockelsstr. 3, 38106 Braunschweig, Germany

Structural Mechanics Hyperelasticity Materials Science Reduced-Order Modeling Physics-Informed Neural Operators Computational Materials Science

May 7, 2026cond-mat.mtrl-sci

SLayerGen: a Crystal Generative Model for all Space and Layer Groups

Crystal generative models have shown rapid progress for accelerating the discovery of bulk, periodic materials. However, many material systems such as 2D superconductors, thin film semiconductors, and catalytic surfaces are diperiodic, i.e., aperiodic along one of the lattice directions. These systems are invariant under the layer groups, which are known to influence materials properties yet not considered by existing models. In this paper, we propose SLayerGen, a generative model that produces crystals constrained to be invariant to any space or layer group. SLayerGen consists of coarse-to-fine discrete autoregressive lattice generation; transformer-based autoregressive sampling of Wyckoff positions, elements, and numbers of symmetrically unique atoms; and space or layer group equivariant diffusion of atomic coordinates. For the diffusion component, we corrected an inconsistency in the loss from prior work arising from hexagonal groups being non-orthogonal in fractional coordinates. To facilitate progress in generative modeling of diperiodic materials, we assembled and filtered datasets of monolayers and bilayers, propose relevant evaluation metrics, and developed novel representations for layer group symmetries. For de novo generation of diperiodic materials, SLayerGen achieves consistent performance gains over bulk crystal generative models and is competitive when training jointly on bulk and diperiodic materials.

Rees Chang, Andrew Novick, Ryan P Adams +1

Department of Materials Science & Engineering, University of Illinois at Urbana-Champaign · Department of Computer Science, Princeton University · Materials Research Laboratory, University of Illinois at Urbana-Champaign

Crystal Structure Generation Diffusion Models Computational Materials Science