SMT-Aided Combinatorial Materials Discovery

In combinatorial materials discovery, one searches for new materials with desirable properties by obtaining measurements on hundreds of samples in a single high-throughput batch experiment. As manual data analysis is becoming more and more impractical, there is a growing need to develop new techniques to automatically analyze and interpret such data. We describe a novel approach to the phase map identification problem where we integrate domain-specific scientific background knowledge about the physical and chemical properties of the materials into an SMT reasoning framework. We evaluate the performance of our method on realistic synthetic measurements, and we show that it provides accurate and physically meaningful interpretations of the data, even in the presence of artificially added noise.

在组合材料发现中，人们通过在一次高通量批量实验中对数百个样本进行测量来寻找具有理想性能的新材料。由于手动数据分析变得越来越不切实际，因此越来越需要开发新技术来自动分析和解释此类数据。我们描述了一种解决相图识别问题的新方法，在该方法中，我们将有关材料物理和化学性质的特定领域科学背景知识整合到一个可满足性模理论（SMT）推理框架中。我们在实际的合成测量数据上评估了我们方法的性能，并且表明，即使存在人为添加的噪声，它也能对数据提供准确且具有物理意义的解释。