Molecular Dynamics

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Period ending 2026-09-14

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Period ending 2026-09-07

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A weekly snapshot of new work published in Molecular Dynamics.

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  1. Machine learning kinetics from molecular dynamics data

    Sep 15, 2026Jonathan Weare, Aaron R. DinnerIncomplete KineticsMolecular Dynamics

  2. Autonomous Chemical Mechanistic Discovery through Agentic Reasoning and Validation

    Sep 12, 2026Dong Li, Sixuan Mi, Zihao Ye +10Agentic DiscoveryComputational Chemistry

  3. MLIP Detective: Active Failure Mode Discovery Beyond Benchmark Scores for Machine-Learning Interatomic Potentials

    Sep 8, 2026Ryuhei Okuno, Nontawat Charoenphakdee, Kaoru Hisama +1Interatomic PotentialsAdsorption Energies

  4. Equivariance Breaks the Learning Rate

    Sep 8, 2026Andrei Manolache, Mathias NiepertAdamEquivariance

  5. Hessian-based molecular conformation augmentation for a scalable and efficient strategy of machine learning interatomic potentials

    Sep 4, 2026Bumju Kwak, Jeonghee JoInteratomic PotentialsHessian Matrix

  6. Generative Nested Sampling of Atomistic Thermodynamic Landscapes

    Sep 2, 2026Alessandro Coretti, Nico Unglert, Sebastian Falkner +2Molecular DynamicsLandscapes

  7. TRACE: Spatiotemporal Contact Memory Graph Network Simulator for Granular Dynamics

    Sep 2, 2026Changjian Zhou, Negin Yousefpour, Jie Qi +3Edge-Sdf ContactsPhysics Simulation

  8. Physics-Informed Neural Network Surrogate for Oxygen Vacancy Dynamics in epitaxial \mathrm{SrTiO_3} on Si memristors via Dynamic Spectral Optimization

    Sep 2, 2026Rodion Podorozhny, Nikoleta Theodoropoulou, Jelena TešićTernary Memristive LogicPorous Media

  9. Latent unified smooth Hamiltonians for excited state chemistry

    Sep 1, 2026David Juergens, Martin Stöhr, Andreas E. Hillers-Bendtsen +2Molecular DynamicsNeural Architectures

  10. Agentic programs: an emerging form of scientific software in computational materials science

    Sep 1, 2026Yunsung Lim, Haekwan Jeon, Jaesun Kim +2Molecular DynamicsScientific Discovery

  11. Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials

    Sep 1, 2026Pingbing Ming, Han WangInteratomic PotentialsMessage Passing Neural Networks

  12. AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials

    Aug 31, 2026Prajwal Ananth, Shuwen YueInteratomic PotentialsActive Learning

  13. Toward a First-Principles Update Geometry for the Language-Model Head

    Aug 23, 2026Aditya Somasundaram, Charles Guille-Escuret, Alexander Moreno +2Spectral NormMulti-Head Attention

  14. Equivariant learning of a transferable three-dimensional classical density functional

    Aug 13, 2026Bingqing ChengDensity Functional TheoryFree Energy Principle

  15. An Agentic Workflow for Legacy HPC Modernization: Converting the Two-Electron-Integral Core of GAMESS

    Aug 12, 2026Yuzhong Shen, Masha Sosonkina, Peng Xu +1Agentic WorkflowsComputational Chemistry

  16. DynaPPI: A Large-scale Dynamic Protein Dataset for AI-driven Advances in Protein Interactomics

    Aug 11, 2026Jiabao Wei, Zilong Geng, Yuze Wang +5ProteinMolecular Dynamics

  17. Agent-MD: Selective LLM Intervention with Event-Driven Escalation for Stateful GCMC--MD Campaigns

    Aug 7, 2026Yijie Wang, Zhen-Yu Yin, Zhenheng Tang +1Molecular DynamicsAgent2Agent

  18. Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers

    Aug 7, 2026Xiangyu Meng, Peng Chen, Mingzhen Li +7High-Performance ComputingMolecular Dynamics

  19. A Multi-Agent Framework for Automated Coarse-Grained Molecular Dynamics of Polymers

    Aug 7, 2026Joohee Choi, Junhyeong Lee, Seunghwa RyuPolymerMolecular Dynamics

  20. ED-DiT: Physics-Guided Diffusion Pretraining for Transferable Molecular Representations from Electron Density

    Aug 4, 2026Liang Shuang, Haocheng Wang, Jiayi Song +2Density Functional TheoryModel Pretraining

  21. When May a Model Replace the Experiment? Audits, Licenses, and the Price of Trust in Surrogate-Driven Design

    Aug 2, 2026Shuangxiu, Ma, Wenhe +1Surrogate ModelingAlgorithm Auditing

  22. A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverage Prediction and Reliable Kinetics Inversion

    Jul 31, 2026Ning Hu, Chang Liu, Yunlei Jiang +1Incomplete KineticsConventional Physics-Informed Neural Networks

  23. MDArena: Evaluating Coding Agents on Realistic Molecular Dynamics Workflows

    Jul 31, 2026Nithishwer Mouroug Anand, Wei-Tse Hsu, Kyle Vaccaro +6Coding AgentsMolecular Dynamics

  24. Implicit Machine Learning Force Fields Accelerate Molecular Dynamics Simulations

    Jul 31, 2026Johannes Maeß, Leon Werner, J. Thorben Frank +5Molecular DynamicsMachine Learning

  25. DFSC: Error-Controlled Differentiable Mittag-Leffler Propagation for Fractional Scientific Machine Learning

    Jul 31, 2026Ning Hu, Haitao Duan, Shuqun Li +1Density Functional TheoryPropagation

  26. Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation

    Jul 30, 2026Paul Hagemann, Katharina Ueltzen, Simon Müller +2Crystal Structure PredictionInteratomic Potentials

  27. Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction

    Jul 30, 2026Tianyou Bai, Huan Wang, Mingchen Gao +4Molecular Property PredictionComputational Chemistry

  28. Using large language models to probe the limits of atom-centered structural descriptors

    Jul 29, 2026Michelangelo Domina, Michele CeriottiMolecular DescriptorsAtoms

  29. Recursive transformers for semiconductor thermo-mechanical reliability

    Jul 28, 2026Kart-leong LimUnified TransformerSurrogate Modeling

  30. Accurate structural modeling of chemically diverse molecular interfaces with Vilya-2

    Jul 28, 2026Vilya Research, :, Pascal Sturmfels +10Protein DesignDrug Design

  31. An Ontology for Machine Learning Interatomic Potentials

    Jul 25, 2026Daniel Hernández, Jong Hyun Jung, Yuji Ikeda +11Interatomic PotentialsDensity Functional Theory

  32. Learning to Prepare Molecular Ground States with Transformer Models

    Jul 24, 2026Alex Koziell-Pipe, Jasmine Brewer, Jem Guhit +14Quantum CircuitsQuantum

  33. Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems

    Jul 23, 2026Xiao Zhu, Srinivasan S. IyengarMolecular DynamicsComputational Chemistry

  34. Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework

    Jul 22, 2026Ashish Kumar Patra, Anurag K. S. V., Ruchika Bhat +3Computational Chemistry

  35. ATLAS: A Foundation Neural Sampler for Amorphous Materials

    Jul 21, 2026Mouyang Cheng, Denis Blessing, Botao Yu +4Molecular DynamicsMaterials Science

  36. DeepCormack: Fermi surface tomography using model-based data-driven algorithms

    Jul 14, 2026Georg F. B. Lovric, Bryn Drury, Carola-Bibiane Schönlieb +2Cryo-Electron MicroscopyComputed Tomography

  37. Transferable Implicit Solvent Machine Learning Potential for Drugs and Proteins Approaching Ab Initio Accuracy

    Jul 12, 2026Jan Eckwert, Julija ZavadlavPrevious Machine-Learning-Based Implicit SolventInteratomic Potentials

  38. Edge Cluster Expansion with Radial Rotary Attention for Interatomic Potentials

    Jul 12, 2026Zemin Xu, Wenbo Xie, P. HuInteratomic Potentials

  39. Active rejection enables reliable generalization of universal machine-learning interatomic potentials

    Jul 10, 2026Mingxiang Luo, Xinnan Mao, Lu Wang +3Interatomic PotentialsMolecular Dynamics

  40. A First-Principles Theory of Slow Thinking and Active Perception

    Jul 9, 2026Hongkang Yang, Zhi-Qin John Xu, Feiyu Xiong +1Cognitive SciencePerception

  41. FirstResearch: Auditable Question Formation for LLM Scientific Discovery Agents

    Jul 6, 2026Yufeng WangLlm-DrivenFalsification

  42. EquiFiLM: Charge-Conditioned Equivariant Force Fields via Feature-wise Linear Modulation

    Jul 6, 2026Samuel Sahel-Schackis, Ken-ichi Nomura, Aiichiro Nakano +2Computational ChemistryWireless Foundation Models

  43. Physically-Relevant Information Learning in High-Dimensional Time-Derivatives Spaces

    Jul 6, 2026Domiziano Doria, Matteo Becchi, Giovanni M. PavanHigh-DimensionalTime-Varying Systems

  44. AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms

    Jul 3, 2026Emmanuel Bengio, Sanjeev Raja, Yui Tik Pang +5Molecular DynamicsPrevious Machine-Learning-Based Implicit Solvent

  45. Microcosmos: Reimagining Artificial Life for the GPU Era

    Jul 3, 2026Mark Tensen, Ciaran Regan, Bert Wang-Chak Chan +3Physics SimulationBio-Inspired Framework

  46. Beyond Adam: SOAP and Muon for Faster, Label-Efficient Training of Machine Learning Interatomic Potentials

    Jul 2, 2026Gil Harari, Yoel Zimmermann, Ola Tangen Kulseng +4Interatomic PotentialsAdam

  47. Q-Score: A Quantum-Native Scoring Function for Molecular Docking

    Jul 2, 2026Kangyu Zheng, Yidong Zhou, Ruihao Li +3Drug DesignQuantum Approximate Optimization Algorithm

  48. Spin-Weighted Spherical Harmonics Enable Complete and Scalable \mathrm{E}(3)-Equivariant Networks

    Jul 1, 2026Chenxing Liang, Yuchao Lin, Andrii Kryvenko +5Molecular Dynamics

  49. Revisiting the Volume Hypothesis

    Jun 30, 2026Ari Pakman, Lior Kreimer, Yakir BerchenkoStochastic Gradient DescentLow-Data Regimes

  50. Diffusion-warm sampling of the XY model enables fast thermalization at scale

    Jun 29, 2026Sehmimul Hoque, Roger Melko, Pooya RonaghTwo-Dimensional Ising ModelDiffusion Sampling

  51. Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations

    Jun 29, 2026Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee +4Drug DesignMolecular Dynamics

  52. Atompack: A Storage and Distribution Layer for Read-Heavy Atomistic ML Training Datasets

    Jun 29, 2026Ali Ramlaoui, Daniel T. Speckhard, Sagar Pal +3Molecular DynamicsTraining Models

  53. Geometric Algebra Meets Cartesian Tensors: Higher-Order Equivariance for Interatomic Potentials

    Jun 28, 2026Can Polat, Erchin Serpedin, Mustafa Kurban +1Interatomic PotentialsMolecular Structure Elucidation

  54. Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents

    Jun 28, 2026Kyungmin Nam, Seunghee Han, Jihan KimMolecular DesignCrystal Structure Prediction

  55. MALOQ: Massively Accelerated Learning of Operators for Quantum Transport

    Jun 27, 2026Manasa Kaniselvan, Alexander Maeder, Denghui Lu +2Computational ChemistryProbabilistic Operator Learning

  56. Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design

    Jun 26, 2026Sk Md Ahnaf Akif Alvi, Jan Janssen, Danny Perez +2Materials DiscoveryCrystal

  57. Autoregressive Boltzmann Generators

    Jun 25, 2026Danyal Rehman, Charlie B. Tan, Yoshua Bengio +2Autoregressive GenerationMolecular Generation

  58. Weight-Space Geometry of Offline Reasoning Training

    Jun 21, 2026Aleksandr Nikolich, Igor Kiselev, Vladimir Platonov +1Weight-SpaceDensity Functional Theory

  59. SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery

    Jun 21, 2026Bin CaoCrystalMaterials Discovery

  60. Atomistic Language Models Understand and Generate Materials

    Jun 19, 2026Sathya Edamadaka, Krithik Ramesh, Ju Li +1Crystal Structure PredictionMolecular Dynamics