Single-Point Extrapolation to the Complete Basis Set Limit through Deep Learning
Single-Point Extrapolation to the Complete Basis Set Limit through Deep Learning
复制标题
通过深度学习单点外推至完整基集极限
DOI：
10.1021/acs.jctc.2c01298
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发表时间：
2023
影响因子：
5.5
通讯作者：
Martínez, Todd J.
中科院分区：
文献类型：
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作者：
Holm, Soren;Unzueta, Pablo A.;Thompson, Keiran;Martínez, Todd J.
机器学习 (ML) 提供了一种有吸引力的方法来预测分子系统，同时避免运行昂贵的电子结构计算。一旦接受了从头开始的数据训练，机器学习的前景就是能够准确预测分子特性，而这在以前通过计算是无法实现的。在这项工作中，我们开发并训练了一个图神经网络模型，以纠正 RHF 和 B3LYP 理论水平上小基组和大基组之间的基组不完整性误差 (BSIE)。我们的结果表明，与拟合总潜力相比，用于校正 BSIE 的 ML 模型更能泛化到训练期间未见过的系统。我们通过在评估分子复合物的同时对单分子进行训练来测试这种能力。我们还表明，在训练数据不足的情况下，集成模型可以产生更好的表现潜力。然而，即使仅拟合 BSIE，只有当训练数据与想要进行预测的系统足够相似时才能实现可接受的性能。对于 B3LYP 密度泛函，经过训练以预测 cc-pVDZ 和 cc-pV5Z 电势之间差异的最终模型的测试误差为 0.184 kcal/mol，并且集成模型准确地再现了 S66x8 数据集上的大基组相互作用能量曲线。
Machine learning (ML) offers an attractive method for making predictions about molecular systems while circumventing the need to run expensive electronic structure calculations. Once trained on ab initio data, the promise of ML is to deliver accurate predictions of molecular properties that were previously computationally infeasible. In this work, we develop and train a graph neural network model to correct the basis set incompleteness error (BSIE) between a small and large basis set at the RHF and B3LYP levels of theory. Our results show that, when compared to fitting to the total potential, an ML model fitted to correct the BSIE is better at generalizing to systems not seen during training. We test this ability by training on single molecules while evaluating on molecular complexes. We also show that ensemble models yield better behaved potentials in situations where the training data is insufficient. However, even when only fitting to the BSIE, acceptable performance is only achieved when the training data sufficiently resemble the systems one wants to make predictions on. The test error of the final model trained to predict the difference between the cc-pVDZ and cc-pV5Z potential is 0.184 kcal/mol for the B3LYP density functional, and the ensemble model accurately reproduces the large basis set interaction energy curves on the S66x8 dataset.