Kawagoe Rinta, Ando Tatsuhito, Matsuzawa Nobuyuki N, Maeshima Hiroyuki, Kaneko Hiromasa
Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa 214-8571, Japan.
Engineering Division, Panasonic Industry Co., Ltd., Kadoma, Osaka 571-8506, Japan.
ACS Omega. 2024 Nov 27;9(49):48844-48854. doi: 10.1021/acsomega.4c09124. eCollection 2024 Dec 10.
Organic semiconductors have been widely studied owing to their potential applications in various devices, such as field-effect transistors, light-emitting diodes, solar cells, and image sensors. However, they have a limitation of significantly lower carrier mobility compared to silicon, which is a widely used inorganic semiconductor. Therefore, to address such limitations, these molecules should be further explored. Hole reorganization energy has been known to influence carrier mobility; that is, lower energy results in higher mobility. This study uses Bayesian optimization (BO) to identify molecules with low hole reorganization energies. While several acquisition functions (AFs), including probability of improvement, expected improvement, and mutual information, have been proposed for use in BO, it is well established that the performance of AFs can vary depending on the data set. We evaluate the performance of AFs applied to a data set of organic semiconductor molecules and propose a novel approach that alternates the use of AFs in the BO process. Our findings conclude that alternating AFs in BO enhance the stability of the search for molecules with low reorganization energy.
有机半导体因其在各种器件中的潜在应用而受到广泛研究，这些器件包括场效应晶体管、发光二极管、太阳能电池和图像传感器等。然而，与广泛使用的无机半导体硅相比，它们存在载流子迁移率显著较低的局限性。因此，为了解决这些局限性，需要对这些分子进行进一步探索。已知空穴重组能会影响载流子迁移率；也就是说，能量越低，迁移率越高。本研究使用贝叶斯优化（BO）来识别具有低空穴重组能的分子。虽然已经提出了几种用于贝叶斯优化的采集函数（AFs），包括改进概率、预期改进和互信息，但众所周知，采集函数的性能可能会因数据集而异。我们评估了应用于有机半导体分子数据集的采集函数的性能，并提出了一种在贝叶斯优化过程中交替使用采集函数的新方法。我们的研究结果表明，在贝叶斯优化中交替使用采集函数可提高寻找低重组能分子搜索的稳定性。
