{"total":24,"rows":[{"id":"2506.23971","title":"UMA: A Family of Universal Models for Atoms","created":"2025-06-30","cited_by_count":105,"area":null},{"id":"2502.12147","title":"Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction","created":"2025-02-17","cited_by_count":84,"area":null},{"id":"2405.03962","title":"AdsorbDiff: Adsorbate Placement via Conditional Denoising Diffusion","created":"2024-05-07","cited_by_count":8,"area":null},{"id":"2510.22938","title":"AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis","created":"2025-10-27","cited_by_count":4,"area":null},{"id":"2407.10844","title":"Improved Uncertainty Estimation of Graph Neural Network Potentials Using Engineered Latent Space Distances","created":"2024-07-15","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":"2409.16204","title":"AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites","created":"2024-09-24","cited_by_count":2,"area":null},{"id":"2606.05050","title":"Autonomous heterogeneous catalyst discovery with a self-evolving multi-agent digital twin","created":"2026-06-03","cited_by_count":2,"area":null},{"id":"2403.09811","title":"Adapting OC20-trained EquiformerV2 Models for High-Entropy Materials","created":"2024-03-14","cited_by_count":1,"area":null},{"id":"2504.05565","title":"Cross-functional transferability in universal machine learning interatomic potentials","created":"2025-04-07","cited_by_count":1,"area":null},{"id":"2510.06632","title":"Chem-NMF: Multi-layer $\\alpha$-divergence Non-Negative Matrix Factorization for Cardiorespiratory Disease Clustering, with Improved Convergence Inspired by Chemical Catalysts and Rigorous Asymptotic Analysis","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2606.19152","title":"AdsMind: A Physics-Grounded Multi-Agent System for Self-Correcting Discovery of Adsorption Configurations on Heterogeneous Catalyst Surfaces","created":"2026-06-17","cited_by_count":1,"area":null},{"id":"2404.12445","title":"Adaptive Catalyst Discovery Using Multicriteria Bayesian Optimization with Representation Learning","created":"2024-04-18","cited_by_count":0,"area":null},{"id":"2502.07293","title":"Global Universal Scaling and Ultra-Small Parameterization in Machine Learning Interatomic Potentials with Super-Linearity","created":"2025-02-11","cited_by_count":0,"area":null},{"id":"2502.15483","title":"MoMa: A Modular Deep Learning Framework for Material Property Prediction","created":"2025-02-21","cited_by_count":0,"area":null},{"id":"2504.02848","title":"Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation","created":"2025-03-20","cited_by_count":0,"area":null},{"id":"2507.15303","title":"Universal crystal material property prediction via multi-view geometric fusion in graph transformers","created":"2025-07-21","cited_by_count":0,"area":null},{"id":"2510.22123","title":"Learning 3D Anisotropic Noise Distributions Improves Molecular Force Field Modeling","created":"2025-10-25","cited_by_count":0,"area":null},{"id":"2512.15228","title":"Accelerating High-Throughput Catalyst Screening by Direct Generation of Equilibrium Adsorption Structures","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2605.04102","title":"Meta-LegNet: A Transferable and Interpretable Framework for Surface Adsorption Prediction via Self-Defined Adsorption-Environment Learning","created":"2026-05-03","cited_by_count":0,"area":null},{"id":"2605.13297","title":"PaMM: Periodic Motif Memory for Atomistic Models with an Explicit Local-Structure Interface","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2609.00528","title":"Why Multi-Layer Message Passing Works: Completeness Theory for Graph Neural Network Interatomic Potentials","created":"2026-09-01","cited_by_count":0,"area":null},{"id":"2609.05643","title":"The convergent laboratory: when AI reasoning, autonomous experiments, high performance and quantum computing reshape chemistry","created":"2026-09-04","cited_by_count":0,"area":null},{"id":"2610.19630","title":"Risk-Controlled DFT Selection for Machine-Learned Interatomic Potentials","created":"2026-10-14","cited_by_count":0,"area":null}],"next_cursor":null}