Simulating the microscopic world：from the Schrödinger equation to the large atomic model

ZHANG Lin-Feng; WANG Han

doi:10.7693/wl20240701

PHYSICS > 2024 > 53(7): 431-441. > DOI: 10.7693/wl20240701

ZHANG Lin-Feng, WANG Han. Simulating the microscopic world：from the Schrödinger equation to the large atomic model[J]. PHYSICS, 2024, 53(7): 431-441. DOI: 10.7693/wl20240701

Citation:

ZHANG Lin-Feng, WANG Han. Simulating the microscopic world：from the Schrödinger equation to the large atomic model[J]. PHYSICS, 2024, 53(7): 431-441. DOI: 10.7693/wl20240701

Citation:

ZHANG Lin-Feng, WANG Han. Simulating the microscopic world：from the Schrödinger equation to the large atomic model[J]. PHYSICS, 2024, 53(7): 431-441. DOI: 10.7693/wl20240701

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Simulating the microscopic world：from the Schrödinger equation to the large atomic model

ZHANG Lin-Feng^1,2, ,,
WANG Han^3,4, ,

1 AI for Science Institute, Beijing 100080, China;
2 DP Technology, Beijing 100080, China;
3 National Laboratory of Computational Physics, Institute of Applied Physics and Computational Mathematics, Beijing 100094, China;
4 Center for Applied Physics and Technology, College of Engineering, Peking University, Beijing 100871, China

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Received Date: May 12, 2024
Available Online: July 12, 2024

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Abstract

With the rapid advance of artificial intelligence, its integration with physical modeling has introduced revolutionary tools for scientific research at the microscopic scale. This paper delineates the development from the approximate solutions of quantum mechanics based on the Schrödinger equation to the emergence of the large atomic model (LAM), with particular emphasis on the application of machine learning in atomic-scale simulations. The theoretical foundations underlying the synergy between artificial intelligence and physical modeling are first discussed. This is followed by a comprehensive analysis of the implementation methodologies for this integration in atomic-scale simulations, including the construction and training strategies of machine learning models. Next, the critical roles of data accumulation, software tools, and engineering infrastructure in propelling advancements in this domain are examined. The potential impact of LAM on future scientific research and industrial applications is also envisioned. Through sustained technological innovation and interdisciplinary collaboration, it is anticipated that LAM will significantly contribute to many fields, including materials science, chemical engineering, and biotechnology, thereby ushering in a new era of development in basic research and applications.
- artificial intelligence,
- physical modeling,
- quantum mechanics,
- large atomic model,
- atomic-scale simulation

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References

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