Chemically-informed data-driven optimization (ChIDDO): leveraging physical models and Bayesian learning to accelerate chemical research
Chemically-informed data-driven optimization (ChIDDO): leveraging physical models and Bayesian learning to accelerate chemical research
复制标题
化学信息数据驱动优化 (ChIDDO)：利用物理模型和贝叶斯学习加速化学研究
DOI：
10.1039/d2re00005a
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
Modestino, Miguel A.
中科院分区：
文献类型：
--
作者：
Frey, Daniel;Shin, Ju Hee;Musco, Christopher;Modestino, Miguel A.
目前寻找最佳实验条件的方法，爱迪生系统搜索，往往效率低下，评估次优设计点，并需要精细的分辨率来确定接近最佳的条件。对于昂贵的试验性活动或拥有较大设计空间的活动，现状方法的缺点更为明显。在这里，我们扩展了贝叶斯优化(BO)，并引入了一种化学信息数据驱动优化(CHIDDO)方法。这种方法利用从化学过程的物理模型中获得的廉价和低保真的信息，然后将其与昂贵和高保真的实验数据相结合来优化共同的目标函数。使用常见的优化基准目标函数，描述了Chiddo算法优于传统BO方法的场景，并在一个模拟的电化学工程优化问题上实现了该算法。
Current methods of finding optimal experimental conditions, Edisonian systematic searches, often inefficiently evaluate suboptimal design points and require fine resolution to identify near optimal conditions. For expensive experimental campaigns or those with large design spaces, the shortcomings of the status quo approaches are more significant. Here, we extend Bayesian optimization (BO) and introduce a chemically-informed data-driven optimization (ChIDDO) approach. This approach uses inexpensive and low-fidelity information obtained from physical models of chemical processes and subsequently combines it with expensive and high-fidelity experimental data to optimize a common objective function. Using common optimization benchmark objective functions, we describe scenarios in which the ChIDDO algorithm outperforms the traditional BO approach, and then implement the algorithm on a simulated electrochemical engineering optimization problem.