Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908368053993472 |
|---|---|
| author | Yu, Qi Ma, Ruitao Qu, Chen Conte, Riccardo Nandi, Apurba Pandey, Priyanka Houston, Paul L. Zhang, Dong H. Bowman, Joel M. |
| author_facet | Yu, Qi Ma, Ruitao Qu, Chen Conte, Riccardo Nandi, Apurba Pandey, Priyanka Houston, Paul L. Zhang, Dong H. Bowman, Joel M. |
| contents | Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully achieved. Here, we present a novel method that combines aspects of both approaches, and achieves state-of-the-art balance of accuracy and force field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. Without sophisticated neural network design, the structural descriptors of monomers are described by 1-body and 2-body effective interactions, enforced by appropriate sets of PIPs as inputs to the feed forward NN. We demonstrate the performance of this method through systematic assessments of models for gas-phase water trimer, liquid water, and also liquid CO2. The high accuracy, fast speed, and flexibility of this method provide a new route for constructing accurate ML potentials and enabling large-scale quantum and classical simulations for complex molecular systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00522 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials Yu, Qi Ma, Ruitao Qu, Chen Conte, Riccardo Nandi, Apurba Pandey, Priyanka Houston, Paul L. Zhang, Dong H. Bowman, Joel M. Chemical Physics Disordered Systems and Neural Networks Atomic and Molecular Clusters Computational Physics Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully achieved. Here, we present a novel method that combines aspects of both approaches, and achieves state-of-the-art balance of accuracy and force field-level speed. This method utilizes a monomer-centered representation, where the potential energy is decomposed into the sum of chemically meaningful monomeric energies. Without sophisticated neural network design, the structural descriptors of monomers are described by 1-body and 2-body effective interactions, enforced by appropriate sets of PIPs as inputs to the feed forward NN. We demonstrate the performance of this method through systematic assessments of models for gas-phase water trimer, liquid water, and also liquid CO2. The high accuracy, fast speed, and flexibility of this method provide a new route for constructing accurate ML potentials and enabling large-scale quantum and classical simulations for complex molecular systems. |
| title | Extending the atomic decomposition and many-body representation, a chemistry-motivated monomer-centered approach for machine learning potentials |
| topic | Chemical Physics Disordered Systems and Neural Networks Atomic and Molecular Clusters Computational Physics |
| url | https://arxiv.org/abs/2412.00522 |