标题： 材料科学中的贝叶斯优化：综述 显示英文标题

标题： Bayesian Optimization in Materials Science: A Survey

摘要： 贝叶斯优化被广泛应用于人工智能的许多领域，用于优化黑箱过程，并在许多应用中实现了最先进的显著改进。 它智能地探索大型和复杂的设计空间，同时最小化对昂贵的底层过程进行评估的次数。 材料科学考虑了在给定一个定义如何合成或加工材料的大设计空间的情况下优化材料性能的问题，评估需要昂贵的实验或模拟——这是一个非常相似的环境。 虽然贝叶斯优化也是解决这些问题的流行方法，但研究相同概念的两个社区之间几乎没有重叠。 我们对材料科学中的贝叶斯优化方法进行了综述，以促进跨学科交流并避免工作重复。 我们强调了共同的挑战和联合研究的机会。 显示英文摘要

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.