Machine-Learning Guided Quantum Chemical and Molecular Dynamics Calculations to Design Novel Hole-Conducting Organic Materials

Materials exhibiting
higher mobilities than conventional organic
semiconducting materials such as fullerenes and fused thiophenes are
in high demand for applications such as printed electronics, organic
solar cells, and image sensors. In order to discover new molecules
that might show improved charge mobility, combined density functional
theory (DFT) and molecular dynamics (MD) calculations were performed,
guided by predictions from machine learning (ML). A ML model was constructed
based on 32 values of theoretically calculated hole mobilities for
thiophene derivatives, benzodifuran derivatives, a carbazole derivative
and a perylene diimide derivative with the maximum value of 10–1.96 cm2/(V s). Sequential learning, also
known as active learning, was applied to select compounds on which
to perform DFT/MD calculation of hole mobility to simultaneously improve
the mobility surrogate model and identify high mobility compounds.
By performing 60 cycles of sequential learning with 165 DFT/MD calculations,
a molecule having a fused thioacene structure with its calculated
hole mobility of 10–1.86 cm2/(V s) was
identified. This values is higher than the maximum value of mobility
in the initial training data set, showing that an extrapolative discovery
could be made with the sequential learning.

相较于传统有机半导体材料（如富勒烯与稠合噻吩类材料），具备更高电荷迁移率的材料在印刷电子、有机太阳能电池以及图像传感器等应用场景中需求旺盛。为发掘可实现更优电荷迁移率的新型分子，本研究依托机器学习（Machine Learning, ML）模型的预测指导，开展了密度泛函理论（Density Functional Theory, DFT）与分子动力学（Molecular Dynamics, MD）的联合计算。本研究基于32组理论计算得到的空穴迁移率数据构建了机器学习模型，数据集涵盖噻吩衍生物、苯并二呋喃衍生物、咔唑衍生物以及苝二酰亚胺衍生物，其最大空穴迁移率为10^-1.96 cm²/(V·s)。本研究采用序列学习（又称主动学习）策略筛选化合物，开展其空穴迁移率的DFT/MD计算，以同时优化迁移率替代模型并发掘高迁移率化合物。通过完成60轮序列学习迭代与165组DFT/MD计算，本研究成功鉴定出一种具备稠合噻蒽结构的分子，其理论计算得到的空穴迁移率为10^-1.86 cm²/(V·s)。该迁移率数值高于初始训练数据集的最大迁移率，证明序列学习可实现外推式的材料发掘。