Way to determine how the surfaces of materials behave

MIT researchers devised a machine-learning-based method to investigate how materials behave at their surfaces. The approach could help in developing compounds or alloys for use as catalysts, semiconductors, or battery components. Credits : Image: MIT News Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components. Designing new compounds or alloys whose surfaces can be used as catalysts in chemical reactions can be a complex process relying heavily on the intuition of experienced chemists. A team of researchers at MIT has devised a new approach using machine learning that removes the need for intuition and provides more detailed information than conventional methods can practically achieve. For example, applying the new system to a material that has already been studied for 30 years by conventional means, the team found the compound's surface could form two new atomic configurations that had not previously been identified, and that one other configuration seen in previous works is likely unstable. The findings are described this week in the journal Nature Computational Science , in a paper by MIT graduate student Xiaochen Du, professors Rafael Gómez-Bombarelli and Bilge Yildiz, MIT Lincoln Laboratory technical staff member Lin Li, and three others.