Li Wei, Ma Haibo, Li Shuhua, Ma Jing
Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University Nanjing 210023 China
Jiangsu Key Laboratory of Advanced Organic Materials, Jiangsu Key Laboratory of Vehicle Emissions Control, Nanjing University Nanjing 210023 China.
Chem Sci. 2021 Nov 8;12(45):14987-15006. doi: 10.1039/d1sc02574k. eCollection 2021 Nov 24.
Electronic structure methods based on quantum mechanics (QM) are widely employed in the computational predictions of the molecular properties and optoelectronic properties of molecular materials. The computational costs of these QM methods, ranging from density functional theory (DFT) or time-dependent DFT (TDDFT) to wave-function theory (WFT), usually increase sharply with the system size, causing the curse of dimensionality and hindering the QM calculations for large sized systems such as long polymer oligomers and complex molecular aggregates. In such cases, in recent years low scaling QM methods and machine learning (ML) techniques have been adopted to reduce the computational costs and thus assist computational and data driven molecular material design. In this review, we illustrated low scaling ground-state and excited-state QM approaches and their applications to long oligomers, self-assembled supramolecular complexes, stimuli-responsive materials, mechanically interlocked molecules, and excited state processes in molecular aggregates. Variable electrostatic parameters were also introduced in the modified force fields with the polarization model. On the basis of QM computational or experimental datasets, several ML algorithms, including explainable models, deep learning, and on-line learning methods, have been employed to predict the molecular energies, forces, electronic structure properties, and optical or electrical properties of materials. It can be conceived that low scaling algorithms with periodic boundary conditions are expected to be further applicable to functional materials, perhaps in combination with machine learning to fast predict the lattice energy, crystal structures, and spectroscopic properties of periodic functional materials.
基于量子力学（QM）的电子结构方法被广泛应用于分子材料的分子性质和光电性质的计算预测。这些量子力学方法的计算成本，从密度泛函理论（DFT）或含时密度泛函理论（TDDFT）到波函数理论（WFT），通常会随着系统规模的增大而急剧增加，从而导致维度灾难，并阻碍对诸如长聚合物低聚物和复杂分子聚集体等大型系统的量子力学计算。在这种情况下，近年来人们采用了低标度量子力学方法和机器学习（ML）技术来降低计算成本，从而辅助计算和数据驱动的分子材料设计。在这篇综述中，我们阐述了低标度基态和激发态量子力学方法及其在长低聚物、自组装超分子复合物、刺激响应材料、机械互锁分子以及分子聚集体中的激发态过程中的应用。在修正的力场中还通过极化模型引入了可变静电参数。基于量子力学计算或实验数据集，已经采用了几种机器学习算法，包括可解释模型、深度学习和在线学习方法，来预测材料的分子能量、力、电子结构性质以及光学或电学性质。可以设想，具有周期性边界条件的低标度算法有望进一步应用于功能材料，或许与机器学习相结合以快速预测周期性功能材料的晶格能、晶体结构和光谱性质。
