Autonomous efficient experiment design for materials discovery with Bayesian model averaging
Autonomous efficient experiment design for materials discovery with Bayesian model averaging
复制标题
DOI：
10.1103/physrevmaterials.2.113803
复制
发表时间：
2018-03
影响因子：
3.4
通讯作者：
A. Talapatra;Shahin Boluki;T. Duong;Xiaoning Qian;E. Dougherty;Raymundo Arr'oyave
中科院分区：
文献类型：
--
作者：
A. Talapatra;Shahin Boluki;T. Duong;Xiaoning Qian;E. Dougherty;Raymundo Arr'oyave
加速探索材料空间，以确定具有最佳性能的配置是一个持续的挑战。当前的范例通常围绕着通过高吞吐量实验/计算来执行这种探索的想法。然而，这种办法没有考虑到现有资源始终存在的限制。最近，这个问题已经解决了框架材料发现作为一个最佳的实验设计。这项工作增强了早期的努力，提出了一个框架，有效地探索材料的设计空间，不仅占资源的限制，但也纳入模型的不确定性的概念。由此产生的方法结合贝叶斯模型平均贝叶斯优化，以实现一个系统，能够自主和自适应地学习，不仅在材料空间中的最有前途的地区，而且最有效地引导这种探索的模型。通过密度泛函理论（DFT）计算有效地探索MAX三元碳化物/氮化物空间的框架证明。
The accelerated exploration of the materials space in order to identify configurations with optimal properties is an ongoing challenge. Current paradigms are typically centered around the idea of performing this exploration through high-throughput experimentation/computation. Such approaches, however, do not account fo the always present constraints in resources available. Recently, this problem has been addressed by framing materials discovery as an optimal experiment design. This work augments earlier efforts by putting forward a framework that efficiently explores the materials design space not only accounting for resource constraints but also incorporating the notion of model uncertainty. The resulting approach combines Bayesian Model Averaging within Bayesian Optimization in order to realize a system capable of autonomously and adaptively learning not only the most promising regions in the materials space but also the models that most efficiently guide such exploration. The framework is demonstrated by efficiently exploring the MAX ternary carbide/nitride space through Density Functional Theory (DFT) calculations.