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基于第一性原理模拟的贝叶斯搜索实现自改进光敏剂发现系统
作者:小柯机器人 发布时间:2021/11/20 21:13:45

新加坡国立大学Xiaonan Wang团队报道了基于第一原理模拟的贝叶斯搜索实现自改进光敏剂发现系统。相关研究成果发表在2021年11月17日出版的《美国化学会杂志》。

基于人工智能(AI)的自学习或自改进材料发现系统将实现下一代材料的发现成为可能。

该文中,研究人员演示了如何通过第一性原理计算和基于贝叶斯优化的主动学习来准确预测材料性能,从而实现高性能光敏剂(PSs)的自我改进发现系统。通过自我改进循环,该系统可以提高模型预测精度(单重态-三重态输出的最佳平均绝对误差为0.090 eV)和高性能PS搜索能力,实现PSs的高效发现。从700多万个分子的分子空间中,发现了5357个潜在的高性能PSs。进一步合成了四种PSs,其性能与商用PSs相当或优于商用PSs。

该研究工作突出了主动学习在基于第一原理的材料设计中的潜力,发现的结构可以促进光敏相关应用的发展。

附:英文原文

Title: Self-Improving Photosensitizer Discovery System via Bayesian Search with First-Principle Simulations

Author: Shidang Xu, Jiali Li, Pengfei Cai, Xiaoli Liu, Bin Liu, Xiaonan Wang

Issue&Volume: November 17, 2021

Abstract: Artificial intelligence (AI) based self-learning or self-improving material discovery system will enable next-generation material discovery. Herein, we demonstrate how to combine accurate prediction of material performance via first-principle calculations and Bayesian optimization-based active learning to realize a self-improving discovery system for high-performance photosensitizers (PSs). Through self-improving cycles, such a system can improve the model prediction accuracy (best mean absolute error of 0.090 eV for singlet–triplet spitting) and high-performance PS search ability, realizing efficient discovery of PSs. From a molecular space with more than 7 million molecules, 5357 potential high-performance PSs were discovered. Four PSs were further synthesized to show performance comparable with or superior to commercial ones. This work highlights the potential of active learning in first-principle-based materials design, and the discovered structures could boost the development of photosensitization related applications.

DOI: 10.1021/jacs.1c08211

Source: https://pubs.acs.org/doi/10.1021/jacs.1c08211

 

期刊信息

JACS:《美国化学会志》,创刊于1879年。隶属于美国化学会,最新IF:14.612
官方网址:https://pubs.acs.org/journal/jacsat
投稿链接:https://acsparagonplus.acs.org/psweb/loginForm?code=1000