论文标题
从转移学习的机器学习潜力周期性相关电子结构方法：使用AFQMC，CCSD和CCSD应用于液态水（T）
Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)
论文作者
论文摘要
从第一原理中获得无序的冷凝相系统的原子结构和动力学仍然是化学理论的最前沿挑战之一。在这里，我们利用了周期性电子结构的最新进展，以表明，通过利用从较低的电子结构方法开始的转移学习，可以从较高的afqmc，ccsd，ccsd和ccsd（t）使用$ \ le \ le le \ le $ $ $ $ $ $ $ $ $ 200能量获得机器学习的液态水的势能表面。通过在这些机器上学习的经典和路径积分分子动力学模拟，我们在整个水的整个液体范围内揭示了动态电子相关和核量子效应的相互作用，同时提供了有效利用定期相关电子结构方法的一般策略来探索无序无序的相位系统。
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.
