Conditional Generative Modeling

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Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction
Sep 15, 2026Kishalay DasGraph SSL Materials Property Prediction
Representing Clinical Conditions on Vital Signs from Healthy Individuals using Latent Modeling
Sep 14, 2026Rafael Pina, Varuna De Silva, Mindula IlleperumaSynthetic Data Augmentation Time Series Generation
Institute for Digital Technologies Loughborough University London London, United Kingdom
Ensemble-Conditioned Molecular Design
Sep 14, 2026Ross Irwin, Alessandro Tibo, Jon Paul Janet +1Molecular Optimization Structure-Based Drug Design
Molecular AI Discovery Sciences, R&D AstraZeneca Gothenburg, Sweden · Department of Computer Science and Engineering Chalmers University of Technology and University of Gothenburg Gothenburg, Sweden
uFlowCSP: Crystal Structure Prediction using Mean flow generative models
Sep 9, 2026Sourin Dey, Dipannoy Das Gupta, Lai Wei +2Crystal Structure Prediction One-Step Generative Modeling
Department of Computer Science and Engineering University of South Carolina Columbia, SC 29201 · Department of Computer Science and Cybersecurity University of North Georgia Columbia, SC 29201
NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
Sep 8, 2026Tobias Susetzky, Raphael Rehms, Dmitrii Seletkov +5Irregular Time-Series Modeling Clinical Prediction
Chair for AI in Healthcare and Medicine, TUM University Hospital and Technical University of Munich (TUM), Munich, Germany · Institute for Diagnostic and Interventional Radiology, TUM University Hospital, Munich, Germany · Munich Center for Machine Learning (MCML), Munich, Germany +3
MedFlow: Class-Aware Multi-Scale Generation for Medical Time-Series Synthesis
Sep 7, 2026Yanhao Huang, Shibo Feng, Wanjin Feng +2Flow Matching Synthetic Data Generation
Shanghai Jiao Tong University · Nanyang Technological University · Tsinghua University
OutageDiT: A Generative Foundation Model for Power Outage Forecasting and Scenario Simulation
Sep 1, 2026Yunqin Zhu, Feng Qiu, Yao XieConditional Generative Modeling Time-Series Foundation Models
H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology · Argonne National Laboratory
FrOGS: Discrete Neural Sampler for Independent Alloy Configurations Across Chemical Conditions
Sep 1, 2026Kyucheol Min, Elyssa Hofgard, Tess SmidtFree Energy Calculation Materials Science
Massachusetts Institute of Technology Cambridge, MA 02139
On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study
Sep 1, 2026Chathurika S Abeykoon, Mathias Nthiani Muia, Mallory GoldsteinClass-Imbalanced Learning Synthetic Data Augmentation
When Metropolis and Hastings Meet Bradley and Terry: Exact MCMC From Preference Voting
Sep 1, 2026Ariel Smogorghevski, Nir Rosenfeld, Yaniv RomanoMarkov Chain Monte Carlo Markov Models
Department of Data and Decision Sciences, Technion – Israel Institute of Technology · Department of Computer Science, Technion – Israel Institute of Technology · Department of Electrical and Computer Engineering, Technion – Israel Institute of Technology
Conditional Flow Matching for ML-Based Inverse Design Problems
Sep 1, 2026Juliana Felder, Milad Habibi, Soheyl Massoudi +1Conditional Flow Matching Constrained Optimization
ETH Zürich · University of Maryland, College Park
Language-Informed Flow Matching for Trend-Guided Structure-Based 3D Molecular Generation
Aug 31, 2026Tianyu Gao, Zhikai Su, Jiashu Li +5Flow Matching Structure-Based Drug Design
1City University of Hong Kong · 2The University of Hong Kong · 3Stanford University
Decoupled Latent Flow Matching for Few-Step Joint Vocal-Accompaniment Separation
Aug 31, 2026Lishi Zuo, Youzhi Tu, Lu Yi +4Flow Matching Flow-Based Generative Modeling
Dept. of Electrical and Electronic Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China
Diffusion-Based Inverse Design of Dielectric Resonator Metasurfaces for Shaping Smart Electromagnetic Environments
Aug 30, 2026M. Tsukerman, K. Grotov, D. Vovchuk +1Conditional Generative Modeling Inverse Design
FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching
Aug 13, 2026Zhuohan Wang, Andreea Bacalum, Ollie Olby +2Flow Matching Flow-Based Generative Modeling
Joint affiliation with King’s College London · Simudyne United Kingdom · King’s College London United Kingdom
Fine-tuned Normalizing Flows for ALICE Zero Degree Calorimeter Fast Simulation
Aug 13, 2026Emilia Majerz, Jacek Otwinowski, Witold Dzwinel +1Neural Surrogate Modeling Normalizing Flows
AGH University of Krakow, Poland · The Henryk Niewodniczański Institute of Nuclear Physics, Polish Academy of Sciences
Flow-based conditional cardiac anatomy generation for virtual cohorts
Aug 10, 2026Konstantinos Kevopoulos, Beatrice Moscoloni, Benjamin Alheit +4Normalizing Flows 3D Medical Imaging
Dept. BioMechanical Engineering, Delft University of Technology, Delft, The Netherlands · Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA, USA · BioMMeda – Institute for Biomedical Engineering and Technology, Ghent University, Ghent, Belgium +2
ControlRadio: Prompt-Driven Controllable Diffusion for Cross-Modal Radio Map Generation
Aug 10, 2026Kangjun Liu, Xiying Pan, Shuhang Zhang +3Wireless Communications Generative Modeling
Pengcheng Laboratory, China · South China University of Technology and the Pengcheng Laboratory, China · Peking University, China +2
Tropical Cyclone Forecasting via Latent Rectified Flow using Satellite Imagery and Atmospheric Fields
Aug 8, 2026Meheru Zannat, Sk. Md. Masudul AhsanRectified Flow Spatiotemporal Forecasting
Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna 9203, Bangladesh
CosmosAlign: Adapting a World Foundation Model for Generative Traffic Video Forecasting
Aug 7, 2026Quang Minh Dinh, Tuan Kiet DoanLong-Horizon Video Generation Vision Foundation Model Adaptation
Simon Fraser University, Burnaby, BC, Canada · Institut Polytechnique de Paris, Palaiseau, France
Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy
Aug 7, 2026Francesca Pia Panaccione, Eugenio Lomurno, Matteo Matteucci3D Medical Imaging Computed Tomography
Department of Electronics, Information, and Bioengineering Politecnico di Milano
ED-CSP: Crystal Structure Prediction from Electron Diffraction
Aug 6, 2026Germain Poloudenny, Yaël Frégier, Arnaud DemortièreCrystal Structure Prediction Crystal Structure Generation
Laboratoire de Mathématiques de Lens (LML), UR 2462, Université d’Artois, Lens, France · Laboratoire de Réactivité et de Chimie des Solides (LRCS), CNRS UMR 7314, UPJV, Amiens, France · Réseau sur le Stockage Electrochimique de l’Energie (RS2E), CNRS FR 3459, Amiens, France +1
Energy-Guided Flow Matching
Aug 6, 2026Haoyang Tong, Yu He, Fang Li +6Flow Matching Conditional Image Generation
MAIS & NLPR, CASIA · JD.com · Xi'an Jiaotong University +1
FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
Aug 6, 2026Yann Fabel, Bijan Nouri, Milon Miah +4Time Series Forecasting Solar Power Forecasting
1DLR, Institute of Solar Research · 2RWTH Aachen University · 3CIEMAT, Energy Department, Renewable Energy Division
Marginal Matching Does Not License Factorized Sampling: Auditing Conditional Style Leakage in Factorized Generative Models
Aug 5, 2026Duong Bach, Hai Nguyen Hong, Cuong DoDisentangled Representation Learning Latent Variable Models
Smart Health Center (VISHC), VinUniversity, Ha Noi, Viet Nam
CSGen: A Multi-Domain Curvilinear Structure Generation Model via Hierarchical Multimodal Diffusion
Aug 5, 2026Zhe Shan, Ziming Yang, Lei Zhou +3Image Generation Conditional Generative Modeling
Hainan University Haikou, China · Guangdong Ocean University Zhanjiang, China
Helping Music Co-Creation Agents 'Listen' Well: Hierarchical Self-Supervised World Models for Understanding and Generation
Aug 5, 2026Scott H. HawleyRepresentation Learning Self-Supervised Pre-Training
Department of Chemistry & Physics Belmont University Nashville, TN, USA
AI-Based Sound Effect Generation: A Narrative Review of Generative Models Across Input Modalities
Aug 4, 2026Sandy Abdo, Bill Kapralos, Priyamvada Tripathi +2Text-to-Audio Generation Sound Effects Generation
Ontario Tech University, Oshawa, ON, Canada · Durham College, Oshawa, ON, Canada · Carleton University, Ottawa, ON, Canada
GSRAIN: Physically Calibrated High-/Low-Frequency Rainfall Synthesis for 3D Gaussian Driving Scenes
Aug 3, 2026Fanyu Wang, Longgao Zhang, Junyi Chen3D Gaussian Splatting Conditional Generative Modeling
Fanyu Wang, Longgao Zhang, and Junyi Chen are with the College of Automotive and Energy Engineering, Tongji University, Shanghai, China.
GeoCore-9B: Towards Geo-Aware Generative Foundation Models in Earth Observation
Aug 3, 2026Jeonghyeok Do, Munchurl KimGeospatial Foundation Models Diffusion-Based Image Generation
Information & Electronics Research Institute KAIST · School of Electrical Engineering KAIST

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Robust and Efficient AI Frameworks for Scalable Material Design and Property Prediction

Kishalay Das

Graph SSL Materials Property Prediction Crystal Structure Generation Multimodal Graph Learning Conditional Generative Modeling

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This thesis develops robust and efficient AI frameworks for accelerating crystalline materials discovery by addressing both major stages of the materials-design pipeline: crystal property prediction and crystal structure generation. Motivated by the high computational cost of Density Functional Theory (DFT) and the limited availability of labeled materials data, the thesis explores graph representation learning, pretraining, multimodal learning, and generative modeling for scalable materials design. For property prediction, the thesis first introduces CrysXPP, which learns transferable crystal representations through unsupervised graph autoencoding, reducing dependence on large property-labeled datasets. It then proposes CrysGNN, a large-scale self-supervised graph pretraining framework that captures atomic connectivity, chemical attributes, and global structural information and transfers this knowledge to downstream property predictors through knowledge distillation. CrysMMNet further enriches crystal representations by jointly modeling graph structure and textual descriptions, thereby incorporating both local chemical and global structural knowledge. For crystal generation, the thesis introduces TGDMat, a text-guided joint diffusion framework that jointly models lattice parameters, atomic types, and atomic coordinates while incorporating textual structural knowledge during denoising. This enables the generation of more valid and stable periodic materials while also supporting conditional generation from natural-language descriptions. Overall, the thesis establishes a unified AI-based framework for data-efficient property prediction and controllable crystal generation, demonstrating how graph learning, multimodal representations, and generative models can reduce computational cost and improve the scalability of materials

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Latest papers 258