一个多目标主动学习平台和响应优化的Web应用程序
Jose Antonio Garrido Torres1, Sii Hong Lau1,2, Pranay Anchuri3
1Department of Chemistry, Princeton University, Princeton, New Jersey08544, United States.
Journal of the American Chemical Society
|October 19, 2022
概括
我们开发了一个开源的贝叶斯式优化平台, 这种工具通过超越人为驱动的运动来加速发现, 通过用户友好的网络应用程序使复杂的化学物质可访问.
科学领域:
- 化学学
- 化学工程
- 计算化学
背景情况:
- 多目标反应优化对于化学合成至关重要.
- 传统的优化方法可能耗时且劳动密集.
- 高通量实验 (HTE) 产生大量数据集,但需要有效的分析.
研究的目的:
- 开发一个开源的贝叶斯优化平台,用于多目标的反应优化.
- 为化学家创建一个用户友好的界面,将优化程序整合到实验室实践中.
- 证明该平台在优化复杂化学反应方面的有效性.
主要方法:
- 开发一个开源的贝叶斯优化平台.
- 使用高通量实验 (HTE) 和虚拟选数据.
- 微调算法组件包括反应编码和替代模型.
- 用于Ni/光电催化酶选择性交叉电友合反应.
主要成果:
- 与人类驱动的运动相比,贝叶斯优化器在较少的实验中 (15-24) 确定了优异的反应条件.
- 该平台成功优化了同时反应产量和反选择性.
- 取得的结果超过了以往人类驱动的优化工作.
结论:
- 开发的开源平台为多目标反应优化提供了高效和可访问的解决方案.
- 图形用户界面 (GUI) 降低了化学家使用高级优化技术的障碍.
- 这种方法加速了化学发现,并将计算工具整合到常规实验室工作中.
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