Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909717751660544 |
|---|---|
| author | Weber, John L. Guha, Rishabh D. Agarwal, Garvit Wei, Yujing Fike, Aidan A. Xie, Xiaowei Stevenson, James Santra, Biswajit Friesner, Richard A. Leswing, Karl Halls, Mathew D. Abel, Robert Jacobson, Leif D. |
| author_facet | Weber, John L. Guha, Rishabh D. Agarwal, Garvit Wei, Yujing Fike, Aidan A. Xie, Xiaowei Stevenson, James Santra, Biswajit Friesner, Richard A. Leswing, Karl Halls, Mathew D. Abel, Robert Jacobson, Leif D. |
| contents | Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant message passing MLFF architecture (MPNICE) which iteratively predicts atomic partial charges, including long-range interactions, enabling the prediction of charge-dependent properties while achieving 5-20x faster inference versus models with comparable accuracy. We train direct and delta-learned MPNICE models for organic systems, and benchmark against experimental properties of liquid and solid systems. We also benchmark the energetics of finite systems, contributing a new set of torsion scans with charged species and a new set of DLPNO-CCSD(T) references for the TorsionNet500 benchmark. We additionally train and benchmark MPNICE models for bulk inorganic crystals, focusing on structural ranking and mechanical properties. Finally, we explore multi-task models for both inorganic and organic systems, which exhibit slightly decreased performance on domain-specific tasks but surprising generalization, stably predicting the gas phase structure of $\simeq500$ Pt/Ir organometallic complexes despite never training to organometallic complexes of any kind. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_06462 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties Weber, John L. Guha, Rishabh D. Agarwal, Garvit Wei, Yujing Fike, Aidan A. Xie, Xiaowei Stevenson, James Santra, Biswajit Friesner, Richard A. Leswing, Karl Halls, Mathew D. Abel, Robert Jacobson, Leif D. Chemical Physics Materials Science Machine learning force fields (MLFFs) have emerged as a sophisticated tool for cost-efficient atomistic simulations approaching DFT accuracy, with recent message passing MLFFs able to cover the entire periodic table. We present an invariant message passing MLFF architecture (MPNICE) which iteratively predicts atomic partial charges, including long-range interactions, enabling the prediction of charge-dependent properties while achieving 5-20x faster inference versus models with comparable accuracy. We train direct and delta-learned MPNICE models for organic systems, and benchmark against experimental properties of liquid and solid systems. We also benchmark the energetics of finite systems, contributing a new set of torsion scans with charged species and a new set of DLPNO-CCSD(T) references for the TorsionNet500 benchmark. We additionally train and benchmark MPNICE models for bulk inorganic crystals, focusing on structural ranking and mechanical properties. Finally, we explore multi-task models for both inorganic and organic systems, which exhibit slightly decreased performance on domain-specific tasks but surprising generalization, stably predicting the gas phase structure of $\simeq500$ Pt/Ir organometallic complexes despite never training to organometallic complexes of any kind. |
| title | Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties |
| topic | Chemical Physics Materials Science |
| url | https://arxiv.org/abs/2505.06462 |