Theory-Guided Discovery of Tin-Based Materials

Suny At Binghamton, Binghamton NY

Investigators

Abstract

NON-TECHNICAL SUMMARY This award supports computational and theoretical research to advance theory-guided discovery of new materials through rapid search of the large space of chemical compositions. The central aims of this project are to perform a systematic screening of tin alloys and develop a library of neural network-based interatomic potentials for an extended set of chemical elements. The neural network models will be used to accelerate the search over possible structures at the level of atoms. Research into tin alloys has attracted renewed interest due to their potential to display novel physics and next-generation functional features. Finely tuned tin-based topological insulators could find future use in spintronics, quantum computing, and thermoelectric materials which can generate electricity from heat. High-capacity tin-based electrodes with improved durability could make batteries cheaper and safer. Carefully optimized tin-based solders may reduce the use of toxic lead-containing materials. This project includes educational activities to attract young students and members of underrepresented groups to scientific research. The PI will contribute a new theme to Binghamton University's Physics Outreach Program for middle school students, develop a set of hands-on presentations on neural networks for undergraduate students, and recruit students from different majors enrolled in Binghamton University's Evolutionary Studies program to carry out interdisciplinary undergraduate research. TECHNICAL SUMMARY This award supports computational and theoretical research to advance theory-guided discovery of new materials through rapid search of the large space of chemical compositions. The PI will focus on the systematic study of tin-based materials. The work is motivated by the remarkable richness of the tin alloys' structural and electronic properties enabling their use as topological insulators, battery anodes, solders, and more. The main challenges associated with the study and development of tin alloys lie in the complexity of their structures and the diversity of their bonding mechanisms. These factors limit the scope of ab initio-based study and the accuracy of classical potential-based modeling. The PI aims to demonstrate that the efficiency and reliability of materials prediction can be improved considerably by: (i) screening a large materials class with a suite of diverse search strategies, and (ii) using emerging neural network methodology for modeling interatomic interactions to accelerate the search. For the comprehensive sampling of the configuration space of structures and compositions, the research team will rely on a combination of high-throughput, evolutionary, and rational design searches. Identification of new tin-based wide-gap topological insulators, durable battery anodes, and stable lead-free solders will advance knowledge in several areas of basic and application-driven research. For the systematic construction of reusable neural network models, the research team will use a recently developed stratified training procedure enabling a natural extension of libraries to larger sets of chemical systems. The neural network models will be freely available as a part of the group's open-source MAISE package. This effort will promote the development and application of emerging machine learning methods in materials research. The scientific work will be integrated with educational and outreach activities which will foster the interest of the next generation in science, technology, engineering, and mathematics disciplines. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.