The Potential of Neural Network Potentials.
| Autor: | Duignan TT; Queensland Micro- and Nanotechnology Centre, Australia, Griffith University, Nathan, Queensland 4111, Australia.; School of Chemical Engineering, University of Queensland, Brisbane, Queensland 4072, Australia. |
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| Jazyk: | angličtina |
| Zdroj: | ACS physical chemistry Au [ACS Phys Chem Au] 2024 Mar 21; Vol. 4 (3), pp. 232-241. Date of Electronic Publication: 2024 Mar 21 (Print Publication: 2024). |
| DOI: | 10.1021/acsphyschemau.4c00004 |
| Abstrakt: | In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are a breakthrough new tool that are already enabling us to simulate systems at the molecular scale with unprecedented accuracy and speed, relying on nothing but fundamental physical laws. The continued development of this approach will realize Paul Dirac's 80-year-old vision of using quantum mechanics to unify physics with chemistry and providing invaluable tools for understanding materials science, biology, earth sciences, and beyond. The era of highly accurate and efficient first-principles molecular simulations will provide a wealth of training data that can be used to build automated computational methodologies, using tools such as diffusion models, for the design and optimization of systems at the molecular scale. Large language models (LLMs) will also evolve into increasingly indispensable tools for literature review, coding, idea generation, and scientific writing. Competing Interests: The author declares no competing financial interest. (© 2024 The Author. Published by American Chemical Society.) |
| Databáze: | MEDLINE |
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