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.

中文翻译：

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通过第一原理模拟的贝叶斯搜索自我改进的光敏剂发现系统

基于人工智能 (AI) 的自我学习或自我改进材料发现系统将使下一代材料发现成为可能。在此，我们展示了如何将通过第一性原理计算准确预测材料性能和基于贝叶斯优化的主动学习相结合，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，这样的系统可以提高模型预测精度（单线态-三线态喷射的最佳平均绝对误差为 0.090 eV）和高性能的 PS 搜索能力，实现 PS 的高效发现。从超过 700 万个分子的分子空间中，发现了 5357 个潜在的高性能 PS。进一步合成了四种 PS，以显示与商业 PS 相当或更好的性能。