Phase‐Mapper: Accelerating Materials Discovery with AI

Abstract

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery

Information Fusion for Multi-Source Material Data: Progress and Challenges

“…New computer technologies such as machine learning [76], data cubes [77], and knowledge graphs [78] can be used to help the development of new materials and improvements in existing material-making processes, has had a significant impact on the advancement of the materials industry. Some applications as phase mapper [79] help scientists filter worthless material structures in the research of new materials, which has greatly accelerated the research and development of new materials. This paper is elaborated on the significance and difficulties of the integration and fusion of multi-source heterogeneous materials data.…”

Machine Learning for Sustainable Energy Systems

“…Zhang et al (138) review additional applications of ML to the design of perovskite materials. Bai et al (139) and Gomes et al (140) propose a method combining physics-and AI-based reasoning techniques to scalably characterize crystal structures in proposed solar light absorbers. Zitnick et al (141) propose the use of ML to create efficient, scalable simulations of potential electrocatalysts for power-to-gas applications, and present the Open Catalyst Dataset to spur research in this area.…”

Comparing End-to-End Machine Learning Methods for Spectra Classification

“…They used PCA as spectra preprocessing and dimensionality reduction, and then applied the kernel extreme learning machine for classification. One of the most relevant studies to our work [34] presented the Phase-Mapper, based on convolutive nonnegative matrix factorization (NMF), which can effectively solve the multi-phase identification problem in the context of x-ray diffraction (XRD). Even though these traditional ML classification methods are widely recognized, there are still some questions.…”