Crystal-structure phase mapping is a core, long-standing challenge in materials science that requires identifying crystal structures, or mixtures thereof, in synthesized materials. Materials science experts excel at solving simple systems but cannot solve complex systems, creating a major bottleneck in high-throughput materials discovery. Herein we show how to automate crystal-structure phase mapping. We formulate phase mapping as an unsupervised pattern demixing problem and describe how to solve it using Deep Reasoning Networks (DRNets). DRNets combine deep learning with constraint reasoning for incorporating scientific prior knowledge and consequently require only a modest amount of (unlabeled) data. DRNets compensate for the limited data by exploiting and magnifying the rich prior knowledge about the thermodynamic rules governing the mixtures of crystals with constraint reasoning seamlessly integrated into neural network optimization. DRNets are designed with an interpretable latent space for encoding prior-knowledge domain constraints and seamlessly integrate constraint reasoning into neural network optimization. DRNets surpass previous approaches on crystal-structure phase mapping, unraveling the Bi-Cu-V oxide phase diagram, and aiding the discovery of solar-fuels materials.
翻译:水晶结构阶段绘图是材料科学的一个长期的核心挑战,需要用合成材料来识别晶体结构或其混合物。 材料科学专家擅长解决简单的系统,但无法解决复杂的系统,从而在高通量材料的发现方面造成一个重大瓶颈。 这里我们展示了如何将晶体结构阶段绘图自动化。 我们将阶段绘图作为一个不受监督的模式解密问题,并描述如何使用深理性网络(DRNets)解决这个问题。 DRNets结合了深层次的学习和将先前科学知识纳入网络的制约性推理,因此只需要少量(未贴标签的)数据。 DRNets通过利用和放大关于规范水晶体混合物的丰富的先前知识,将约束性推理无缝地纳入神经网络优化。DRNets设计了一个可解释的潜在空间,用于将前知识领域限制和无缝整合到神经网络优化中的制约性推理。DRNets超越了以往关于水晶结构阶段绘图、解析二氧化二氧化品阶段图表和协助发现太阳能材料的方法,从而弥补了有限的数据。