Integrated computational materials engineering (ICME) calls for the integration of simulation tools and experiments to accelerate the development of materials. ICME approaches tend to be computationally costly, and recently, Bayesian optimization (BO) has been proposed as a way to make ICME more resource efficient. Conventional BO, however, is sequential (i.e., one-at-a-time) in nature, which makes it very time-consuming when the evaluation of a materials design choice is costly. While conventional high-throughput approaches enable the synthesis and characterization (or simulation) of materials in a parallel manner, they tend to be “open loop” and are unable to provide recommendations of what to try next once the parallel experiment/simulation has been carried out and analyzed. Here, we address this problem by introducing a batch BO framework that enables the exploration of the material’s design space in a parallel fashion. We augment this approach by incorporating information fusion frameworks capable of integrating multiple information sources. Demonstrating the proposed approach in the computational design of dual-phase steel, we show that batch BO can result in a significant reduction in the time and resources needed to carry out the design task. The proposed approach has wider applicability, well beyond the ICME example used to demonstrate it.

集成计算材料工程（ICME）要求集成仿真工具和实验以加速材料的开发。ICME方法趋向于在计算上昂贵，并且最近，贝叶斯优化（BO）已被提出作为一种使ICME更有效利用资源的方法。然而，传统的BO本质上是连续的（即一次一次），这使得在评估材料设计选择的成本很高时非常耗时。尽管常规的高通量方法可以并行方式进行材料的合成和表征（或模拟），但它们往往是“开环”的，一旦进行了并行实验/模拟，就无法提供下一步的建议。进行分析。这里，我们通过引入批处理BO框架来解决此问题，该框架使您能够以并行方式探索材料的设计空间。我们通过合并能够集成多个信息源的信息融合框架来增强这种方法。在双相钢的计算设计中证明了所提出的方法，我们表明，批处理BO可以显着减少执行设计任务所需的时间和资源。所提议的方法具有更广泛的适用性，远远超出了用于演示它的ICME示例。在双相钢的计算设计中证明了所提出的方法，我们表明，批处理BO可以显着减少执行设计任务所需的时间和资源。所提议的方法具有更广泛的适用性，远远超出了用于演示它的ICME示例。在双相钢的计算设计中证明了所提出的方法，我们表明，批处理BO可以显着减少执行设计任务所需的时间和资源。所提议的方法具有更广泛的适用性，远远超出了用于演示它的ICME示例。