The Potential of Neural Network Potentials

IF 3.7 Q2 CHEMISTRY, PHYSICAL ACS Physical Chemistry Au Pub Date : 2024-03-21 DOI:10.1021/acsphyschemau.4c00004
Timothy T. Duignan*, 
{"title":"The Potential of Neural Network Potentials","authors":"Timothy T. Duignan*,&nbsp;","doi":"10.1021/acsphyschemau.4c00004","DOIUrl":null,"url":null,"abstract":"<p >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.</p>","PeriodicalId":29796,"journal":{"name":"ACS Physical Chemistry Au","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsphyschemau.4c00004","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Physical Chemistry Au","FirstCategoryId":"1085","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acsphyschemau.4c00004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0

Abstract

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.

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
神经网络的潜力
在未来的半个世纪里,物理化学很可能会经历一场深刻的变革,其主要驱动力是量子化学和机器学习(ML)的最新进展。具体来说,等变神经网络势(NNPs)是一种突破性的新工具,它已经使我们能够在分子尺度上以前所未有的精度和速度模拟系统,而这一切只依赖于基本物理定律。这种方法的不断发展将实现保罗-狄拉克(Paul Dirac)80 年前的愿景,即利用量子力学将物理学与化学统一起来,并为理解材料科学、生物学、地球科学及其他领域提供宝贵的工具。高精度、高效率的第一原理分子模拟时代将提供丰富的训练数据,可用于建立自动化计算方法,使用扩散模型等工具,在分子尺度上设计和优化系统。大型语言模型(LLM)也将逐渐发展成为文献查阅、编码、创意生成和科学写作不可或缺的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
3.70
自引率
0.00%
发文量
0
期刊介绍: ACS Physical Chemistry Au is an open access journal which publishes original fundamental and applied research on all aspects of physical chemistry. The journal publishes new and original experimental computational and theoretical research of interest to physical chemists biophysical chemists chemical physicists physicists material scientists and engineers. An essential criterion for acceptance is that the manuscript provides new physical insight or develops new tools and methods of general interest. Some major topical areas include:Molecules Clusters and Aerosols; Biophysics Biomaterials Liquids and Soft Matter; Energy Materials and Catalysis
期刊最新文献
Issue Publication Information Issue Editorial Masthead Roundabout Mechanism of Ion–Molecule Nucleophilic Substitution Reactions Ultrafast Spin Relaxation of Charge Carriers in Strongly Quantum Confined Methylammonium Lead Bromide Perovskite Magic-Sized Clusters Direct Detection of Bound Water in Hydrated Powders of Lysozyme by Differential Scanning Calorimetry
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1