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arXiv 2211.16619physics.chem-phcond-mat.dis-nn
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来自周期相关电子结构方法的迁移学习的机器学习潜力:应用于具有AFQMC、CCSD和CCSD的液态水(T)

Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)

Michael S. Chen, Joonho Lee, Hong-Zhou Ye, Timothy C. Berkelbach, David R. Reichman, Thomas E. Markland

摘要:

从第一性原理获得无序凝聚相系统的原子结构和动力学仍然是化学理论的前沿挑战之一。在这里,我们利用周期性电子结构的最新进展来表明,通过利用从较低层电子结构方法开始的迁移学习,可以使用$\le$ 200能量从较高层AFQMC、CCSD和CCSD(T)方法获得液态水的机器学习势能面。通过在这些机器学习的势能面上进行经典和路径积分分子动力学模拟,我们揭示了动态电子相关和核量子效应在整个水的液体范围内的相互作用,同时提供了一种有效利用周期相关电子结构方法来探索无序凝聚相系统的一般策略。

英文摘要:

Obtaining the atomistic structure and dynamics of disordered condensed phase systems from first principles remains one of the forefront challenges of chemical theory. Here we exploit recent advances in periodic electronic structure to show that, by leveraging transfer learning starting from lower tier electronic structure methods, one can obtain machine learned potential energy surfaces for liquid water from the higher tier AFQMC, CCSD, and CCSD(T) approaches using $\le$200 energies. By performing both classical and path integral molecular dynamics simulations on these machine learned potential energy surfaces we uncover the interplay of dynamical electron correlation and nuclear quantum effects across the entire liquid range of water while providing a general strategy for efficiently utilizing periodic correlated electronic structure methods to explore disordered condensed phase systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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