{"total":16,"rows":[{"id":"2305.19302","title":"Smooth, exact rotational symmetrization for deep learning on point clouds","created":"2023-05-30","cited_by_count":39,"area":null},{"id":"2503.09814","title":"A practical guide to machine learning interatomic potentials -- Status and future","created":"2025-03-12","cited_by_count":26,"area":null},{"id":"2305.14247","title":"Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science","created":"2023-05-23","cited_by_count":25,"area":null},{"id":"2302.14231","title":"CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling","created":"2023-02-28","cited_by_count":13,"area":null},{"id":"2210.01705","title":"Tensor-reduced atomic density representations","created":"2022-10-02","cited_by_count":10,"area":null},{"id":"2405.01491","title":"FeNNol: an Efficient and Flexible Library for Building Force-field-enhanced Neural Network Potentials","created":"2024-05-02","cited_by_count":6,"area":null},{"id":"2101.02930","title":"Symmetry-adapted graph neural networks for constructing molecular dynamics force fields","created":"2021-01-08","cited_by_count":5,"area":null},{"id":"2309.06710","title":"Crystal structure prediction using neural network potential and age-fitness Pareto genetic algorithm","created":"2023-09-13","cited_by_count":4,"area":null},{"id":"2405.09052","title":"Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred Potential","created":"2024-05-15","cited_by_count":3,"area":null},{"id":"2406.13265","title":"Molecule Graph Networks with Many-body Equivariant Interactions","created":"2024-06-19","cited_by_count":3,"area":null},{"id":"2502.13522","title":"Enhancing Machine Learning Potentials through Transfer Learning across Chemical Elements","created":"2025-02-19","cited_by_count":2,"area":null},{"id":"2405.12229","title":"Multi-task learning for molecular electronic structure approaching coupled-cluster accuracy","created":"2024-05-09","cited_by_count":1,"area":null},{"id":"2410.23858","title":"Neural Network Matrix Product Operator: A Multi-Dimensionally Integrable Machine Learning Potential","created":"2024-10-31","cited_by_count":0,"area":null},{"id":"2503.08207","title":"Are Foundational Atomistic Models Reliable for Finite-Temperature Molecular Dynamics?","created":"2025-03-11","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":"2509.07417","title":"Benchmarking Universal Interatomic Potentials on Zeolite Structures","created":"2025-09-09","cited_by_count":0,"area":null}],"next_cursor":null}