Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations
Fuente:
arXiv
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| Main Author: | |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910574658453504 |
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| author | De Fabritiis, Gianni |
| author_facet | De Fabritiis, Gianni |
| contents | Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simulating complex molecular systems. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_12625 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations De Fabritiis, Gianni Chemical Physics Machine Learning Machine learning potentials offer a revolutionary, unifying framework for molecular simulations across scales, from quantum chemistry to coarse-grained models. Here, I explore their potential to dramatically improve accuracy and scalability in simulating complex molecular systems. I discuss key challenges that must be addressed to fully realize their transformative potential in chemical biology and related fields. |
| title | Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations |
| topic | Chemical Physics Machine Learning |
| url | https://arxiv.org/abs/2408.12625 |