一种基于人工神经网络的过渡金属配合物绝热能量差密度泛函方法
An Artificial Neural Network-based Density Functional Approach for Adiabatic Energy Differences in Transition Metal Complexes
AI总结：
针对近似Kohn-Sham密度泛函在过渡金属配合物自旋态能量预测上的不足，研究基于R2SCAN泛函结合粒子群优化训练的神经网络校正，构建增强型meta-GGA泛函，其绝热能量差预测表现优于多数已知泛函。
摘要：
近几十年来，近似Kohn-Sham密度泛函理论方案在计算化学和物理学领域取得了诸多成功，但其在自旋态能量学预测方面的表现往往不尽如人意。本研究借助机器学习方法，开发了一种增强型交换关联泛函，用于描述过渡金属配合物的绝热能量差。该泛函基于计算高效的正则化强约束且适当范数修正版（R2SCAN）泛函构建，并通过在电子密度、原子化能和/或自旋态能量学的小型数据集上训练的人工神经网络校正项进行改进。本文详细讨论了训练过程，该过程采用了一种受生物启发的非梯度方法，是由粒子群优化（Particle Swarm Optimization）方法适配本研究改造而来。最终结果表明，这种meta-GGA泛函在验证测试和通用性测试的绝热能量差预测中，表现均优于大多数已知的密度泛函。
英文摘要：
During the past decades, approximate Kohn-Sham density-functional theory schemes garnered many successes in computational chemistry and physics; yet the performance in the prediction of spin state energetics is often unsatisfactory. By means of a machine-learning approach, an enhanced exchange and correlation functional is developed to describe adiabatic energy differences in transition metal complexes. The functional is based on the computationally efficient revision of the regularized strongly constrained and appropriately normed (R2SCAN) functional and improved by an artificial neural-network correction trained over a small dataset of electronic densities, atomization energies and/or spin state energetics. The training process, performed using a bio-inspired non gradient-based approach adapted for this work from Particle Swarm Optimization, is discussed in detail. The meta-GGA functional is finally shown to outperform most known density functionals in the prediction of adiabatic energy differences for both the validation test and the generality test.
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