{"total":21,"rows":[{"id":"2311.07361","title":"The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4","created":"2023-11-13","cited_by_count":112,"area":null},{"id":"2312.03687","title":"MatterGen: a generative model for inorganic materials design","created":"2023-12-06","cited_by_count":98,"area":null},{"id":"2310.04562","title":"Towards Foundation Models for Knowledge Graph Reasoning","created":"2023-10-06","cited_by_count":86,"area":null},{"id":"2402.02216","title":"Position: Graph Foundation Models are Already Here","created":"2024-02-03","cited_by_count":86,"area":null},{"id":"2411.06070","title":"GFT: Graph Foundation Model with Transferable Tree Vocabulary","created":"2024-11-09","cited_by_count":85,"area":null},{"id":"2312.07511","title":"A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems","created":"2023-12-12","cited_by_count":68,"area":null},{"id":"2409.17808","title":"Generative Modeling of Molecular Dynamics Trajectories","created":"2024-09-26","cited_by_count":51,"area":null},{"id":"2403.15734","title":"Space Group Informed Transformer for Crystalline Materials Generation","created":"2024-03-23","cited_by_count":41,"area":null},{"id":"2310.16802","title":"From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction","created":"2023-10-25","cited_by_count":39,"area":null},{"id":"2310.04925","title":"Crystal-GFN: sampling crystals with desirable properties and constraints","created":"2023-10-07","cited_by_count":31,"area":null},{"id":"2410.18403","title":"Structure Language Models for Protein Conformation Generation","created":"2024-10-24","cited_by_count":17,"area":null},{"id":"2310.14782","title":"Towards equilibrium molecular conformation generation with GFlowNets","created":"2023-10-20","cited_by_count":14,"area":null},{"id":"2309.16578","title":"Overcoming the Barrier of Orbital-Free Density Functional Theory for Molecular Systems Using Deep Learning","created":"2023-09-28","cited_by_count":13,"area":null},{"id":"2409.15166","title":"Harmonic Path Integral Diffusion","created":"2024-09-23","cited_by_count":11,"area":null},{"id":"2312.05472","title":"Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models","created":"2023-12-09","cited_by_count":8,"area":null},{"id":"2405.03962","title":"AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion","created":"2024-05-07","cited_by_count":8,"area":null},{"id":"2401.02080","title":"Energy based diffusion generator for efficient sampling of Boltzmann distributions","created":"2024-01-04","cited_by_count":7,"area":null},{"id":"2401.10721","title":"Generative Model for Constructing Reaction Path from Initial to Final States","created":"2024-01-19","cited_by_count":1,"area":null},{"id":"2402.10433","title":"Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations","created":"2024-02-16","cited_by_count":1,"area":null},{"id":"2512.11858","title":"Adaptive Path Integral Diffusion: AdaPID","created":"2025-12-05","cited_by_count":1,"area":null},{"id":"2308.15116","title":"Mixup-Augmented Meta-Learning for Sample-Efficient Fine-Tuning of Protein Simulators","created":"2023-08-29","cited_by_count":0,"area":null}],"next_cursor":null}