Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization
Design of Molecules with Low Hole and Electron Reorganization Energy Using DFT Calculations and Bayesian Optimization
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DOI：
10.1021/acs.jpca.2c05229
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发表时间：
2022-09-02
影响因子：
2.9
通讯作者：
Kaneko, Hiromasa
中科院分区：
文献类型：
--
作者：
Ando, Tatsuhito;Shimizu, Naoto;Kaneko, Hiromasa
具有比传统有机半导体材料更高迁移率的材料，如富勒烯和熔融噻吩，在印刷电子领域的应用需求很大。为了发现可能表现出更高电荷迁移率的新分子，将密度泛函理论（DFT）方法与机器学习技术相结合，进行了实验自适应设计（DoE）来设计具有低重组能的分子。将165个分子的DFT计算值作为高斯过程回归（GPR）模型的初始训练数据集，应用GPR模型进行5轮分子设计，并通过DFT计算进行验证。结果，成功发现了重组能小于初始训练数据集中最低值的新分子。
Materials exhibiting higher mobility than conven-tional organic semiconducting materials, such as fullerenes and fused thiophenes, are in high demand for applications in printed electronics. To discover new molecules that might show improved charge mobility, the adaptive design of experiments (DoE) to design molecules with low reorganization energy was performed by combining density functional theory (DFT) methods and machine learning techniques. DFT-calculated values of 165 molecules were used as an initial training dataset for a Gaussian process regression (GPR) model, and five rounds of molecular designs applying the GPR model and validation via DFT calculations were executed. As a result, new molecules whose reorganization energy is smaller than the lowest value in the initial training dataset were successfully discovered.