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*, 
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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.

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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。
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3.70
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0.00%
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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
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