机器学习加速氧化还原电位和酸度常数计算OA北大核心CSTPCD

Redox potentials and acidity constants are key properties for evaluating the performance of energy materials. To achieve computational design of new generation of energy materials with higher performances, computing redox potentials and acidity constants with computational chemistry have attracted lots of attention. However, many works are done by using implicit solvation models, which is difficult to be applied to complex solvation environments due to hard parameterization. Recently, ab initio molecular dynamics (AIMD) has been applied to investigate real electrolytes with complex solvation. Furthermore, AIMD based free energy calculation methods have been established to calculate these physical chemical properties accurately. However, due to the low efficiency of ab initio calculations and the high computational costs, AIMD based free energy calculations are limited to systems with less than 1000 atoms. To solve the dilemma, machine learning molecular dynamics (MLMD) is introduced to accelerate the calculations. By using machine learning method to construct one-to-one mapping from structures to computed potential energies and atomic forces, molecular dynamics can be carried out with much low costs under ab initio accuracy. In order to achieve the MLMD based free energy calculation, a new scheme for machine learning potential (MLP) should be introduced to collect training datasets. By combining the free energy perturbation sampling method and concurrent learning scheme, the training datasets can be collected along the reaction's pathway (insertion of an electron or a proton) with high efficiency and the free energy calculations based on MLMD show good accuracy in comparison with AIMD simulation. This paper describes how to constructing machine learning potential for free energy calculation through the automated workflow, and how to use MLMD to compute accurate free energy differences and corresponding physical chemical properties.