{"total":35,"rows":[{"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":"2304.06174","title":"Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model","created":"2023-04-12","cited_by_count":30,"area":null},{"id":"2403.00485","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","created":"2024-03-01","cited_by_count":28,"area":null},{"id":"2509.21624","title":"HIP: Hessian Interatomic Potentials without derivatives","created":"2025-09-25","cited_by_count":12,"area":null},{"id":"2305.16325","title":"Graph Neural Network Interatomic Potential Ensembles with Calibrated Aleatoric and Epistemic Uncertainty on Energy and Forces","created":"2023-05-10","cited_by_count":10,"area":null},{"id":"2506.14665","title":"Accurate and scalable exchange-correlation with deep learning","created":"2025-06-17","cited_by_count":8,"area":null},{"id":"2408.08006","title":"Hessian QM9: A quantum chemistry database of molecular Hessians in implicit solvents","created":"2024-08-15","cited_by_count":7,"area":null},{"id":"2409.01931","title":"On the design space between molecular mechanics and machine learning force fields","created":"2024-09-03","cited_by_count":4,"area":null},{"id":"2404.13430","title":"React-OT: Optimal Transport for Generating Transition State in Chemical Reactions","created":"2024-04-20","cited_by_count":3,"area":null},{"id":"2502.15843","title":"Implicit Neural Representations for Chemical Reaction Paths","created":"2025-02-20","cited_by_count":3,"area":null},{"id":"2503.08674","title":"Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields","created":"2025-03-11","cited_by_count":3,"area":null},{"id":"2510.06030","title":"Adaptive Pruning for Increased Robustness and Reduced Computational Overhead in Gaussian Process Accelerated Saddle Point Searches","created":"2025-10-07","cited_by_count":3,"area":null},{"id":"2605.03964","title":"Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs","created":"2026-05-05","cited_by_count":3,"area":null},{"id":"2406.14347","title":"$\\nabla^2$DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials","created":"2024-06-20","cited_by_count":2,"area":null},{"id":"2504.15370","title":"Transferable Learning of Reaction Pathways from Geometric Priors","created":"2025-04-21","cited_by_count":2,"area":null},{"id":"2602.02310","title":"FragmentFlow: Scalable Transition State Generation for Large Molecules","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2605.13788","title":"Force-Aware Neural Tangent Kernels for Scalable and Robust Active Learning of MLIPs","created":"2026-05-13","cited_by_count":2,"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":"2503.18975","title":"Machine Learning - Driven Materials Discovery: Unlocking Next-Generation Functional Materials - A review","created":"2025-03-22","cited_by_count":1,"area":null},{"id":"2504.19538","title":"Towards Faster and More Compact Foundation Models for Molecular Property Prediction","created":"2025-04-28","cited_by_count":1,"area":null},{"id":"2506.21788","title":"Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data","created":"2025-06-26","cited_by_count":1,"area":null},{"id":"2312.04174","title":"Coherent energy and force uncertainty in deep learning force fields","created":"2023-12-07","cited_by_count":0,"area":null},{"id":"2411.19629","title":"OpenQDC: Open Quantum Data Commons","created":"2024-11-29","cited_by_count":0,"area":null},{"id":"2502.11085","title":"On the Importance of Pretraining Data Alignment for Atomic Property Prediction","created":"2025-02-16","cited_by_count":0,"area":null},{"id":"2505.22208","title":"LaMM: Semi-Supervised Pre-Training of Large-Scale Materials Models","created":"2025-05-28","cited_by_count":0,"area":null},{"id":"2603.10992","title":"A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2603.11924","title":"Chem4DLLM: 4D Multimodal LLMs for Chemical Dynamics Understanding","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2603.25373","title":"Hessian-informed machine learning interatomic potential towards bridging theory and experiments","created":"2026-03-26","cited_by_count":0,"area":null},{"id":"2604.15380","title":"Exascale Multi-Task Graph Foundation Models for Imbalanced, Multi-Fidelity Atomistic Data","created":"2026-04-15","cited_by_count":0,"area":null},{"id":"2605.08988","title":"Benchmarking Compositional Generalisation for Machine Learning Interatomic Potentials","created":"2026-05-09","cited_by_count":0,"area":null},{"id":"2608.14076","title":"Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generation","created":"2026-08-14","cited_by_count":0,"area":null},{"id":"2608.21605","title":"Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANs","created":"2026-08-21","cited_by_count":0,"area":null},{"id":"2609.00488","title":"AdaptNTK: Adaptive Uncertainty Quantification and Active Learning for Neural Network Potentials","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.05233","title":"Hessian-Informed Conformation Augmentation for Potential Energy Surface Curvature in Machine Learning Interatomic Potentials","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2609.28013","title":"SoLiD26: A First Principles Solid-Liquid Interface Dataset for Machine-learned Interatomic Potentials","created":"2026-09-23","cited_by_count":0,"area":null}],"next_cursor":null}