通过第一原则模拟进行贝叶斯搜索的自我改进的光敏感器发现系统
Shidang Xu1, Jiali Li1, Pengfei Cai2
1Department of Chemical and Biomolecular Engineering, National University of Singapore, 4 Engineering Drive 4, Singapore 117585, Singapore.
Journal of the American Chemical Society
|November 17, 2021
概括
我们开发了一个人工智能系统, 利用主动学习和初步计算来发现高性能光敏剂. 这种自我改进的系统有效地识别了成千上万的潜在候选物, 加快了材料的发现.
科学领域:
- 材料科学
- 计算化学
- 人工智能
背景情况:
- 下一代材料的发现需要先进的计算工具.
- 开发高性能光敏剂对于各种应用至关重要.
- 目前的发现方法可能耗时且低效.
研究的目的:
- 使用人工智能创建一个自我改进的材料发现系统.
- 为了加快高性能光敏剂的识别.
- 展示主动学习和第一原则计算之间的协同作用.
主要方法:
- 实施了基于贝叶斯优化的积极学习框架.
- 综合精确的材料性能预测使用第一原则计算.
- 使用自我改进周期来提高模型的准确性和搜索能力.
主要成果:
- 最好的平均绝对误差为0.090eV.
- 从700多万个分子中发现了5357种潜在的高性能光敏感剂.
- 合成了四种新的光敏感剂,其性能与商业标准相美.
结论:
- 积极学习显著提高了基于第一原则的材料设计.
- 开发的人工智能系统有效地发现了高性能光敏剂.
- 发现的光敏剂显示出对光敏应用的前景.
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