材料科学中的贝叶斯优化
Bayesian Optimization in Materials Science: A Survey
摘要：
贝叶斯优化被用于人工智能的许多领域，用于优化黑盒过程，并在许多应用中取得了令人印象深刻的进步。它可以智能地探索大型复杂的设计空间，同时最大限度地减少要优化的昂贵底层流程的评估数量。材料科学考虑优化材料性能的问题，给定一个大的设计空间，定义如何合成或处理它们，评估需要昂贵的实验或模拟-一个非常相似的设置。虽然贝叶斯优化也是解决此类问题的一种流行方法，但在研究相同概念的两个社区之间几乎没有重叠。我们提出了一个调查贝叶斯优化方法在材料科学，以增加交叉施肥，避免重复工作。我们强调共同的挑战和共同研究工作的机会。
英文摘要：
Bayesian optimization is used in many areas of AI for the optimization of black-box processes and has achieved impressive improvements of the state of the art for a lot of applications. It intelligently explores large and complex design spaces while minimizing the number of evaluations of the expensive underlying process to be optimized. Materials science considers the problem of optimizing materials' properties given a large design space that defines how to synthesize or process them, with evaluations requiring expensive experiments or simulations -- a very similar setting. While Bayesian optimization is also a popular approach to tackle such problems, there is almost no overlap between the two communities that are investigating the same concepts. We present a survey of Bayesian optimization approaches in materials science to increase cross-fertilization and avoid duplication of work. We highlight common challenges and opportunities for joint research efforts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
