Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916182218506240 |
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| author | Lin, Arthur Y. Huguenin-Dumittan, Kevin K. Cho, Yong-Cheol Nigam, Jigyasa Cersonsky, Rose K. |
| author_facet | Lin, Arthur Y. Huguenin-Dumittan, Kevin K. Cho, Yong-Cheol Nigam, Jigyasa Cersonsky, Rose K. |
| contents | Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotropic, interactions. In many communities, there is often a need to represent groups of atoms, either to increase the computational efficiency of simulation via coarse-graining or to understand molecular influences on system behavior. In such cases, atom-centered representations will have limited utility, as groups of atoms may not be well-approximated as spheres. In this work, we extend the popular Smooth Overlap of Atomic Positions (SOAP) ML representation for systems consisting of non-spherical anisotropic particles or clusters of atoms. We show the power of this anisotropic extension of SOAP, which we deem \AniSOAP, in accurately characterizing liquid crystal systems and predicting the energetics of Gay-Berne ellipsoids and coarse-grained benzene crystals. With our study of these prototypical anisotropic systems, we derive fundamental insights into how molecular shape influences mesoscale behavior and explain how to reincorporate important atom-atom interactions typically not captured by coarse-grained models. Moving forward, we propose \AniSOAP as a flexible, unified framework for coarse-graining in complex, multiscale simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19039 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles Lin, Arthur Y. Huguenin-Dumittan, Kevin K. Cho, Yong-Cheol Nigam, Jigyasa Cersonsky, Rose K. Computational Physics Statistical Mechanics Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of these representations build off the idea of atoms as having spherical, or isotropic, interactions. In many communities, there is often a need to represent groups of atoms, either to increase the computational efficiency of simulation via coarse-graining or to understand molecular influences on system behavior. In such cases, atom-centered representations will have limited utility, as groups of atoms may not be well-approximated as spheres. In this work, we extend the popular Smooth Overlap of Atomic Positions (SOAP) ML representation for systems consisting of non-spherical anisotropic particles or clusters of atoms. We show the power of this anisotropic extension of SOAP, which we deem \AniSOAP, in accurately characterizing liquid crystal systems and predicting the energetics of Gay-Berne ellipsoids and coarse-grained benzene crystals. With our study of these prototypical anisotropic systems, we derive fundamental insights into how molecular shape influences mesoscale behavior and explain how to reincorporate important atom-atom interactions typically not captured by coarse-grained models. Moving forward, we propose \AniSOAP as a flexible, unified framework for coarse-graining in complex, multiscale simulation. |
| title | Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles |
| topic | Computational Physics Statistical Mechanics |
| url | https://arxiv.org/abs/2403.19039 |