CDS&E: Collaborative Research: Designing New Zintl Phases with Motif-based Deep Learning and Ab Initio Methods

NONTECHNICAL SUMMARY

This award supports computational and data-science-enabled design of a useful but under-explored class of materials called Zintl phases, which have unique properties suitable for thermoelectrics, batteries, and photovoltaics. Data-driven design of materials with tailored properties has become increasingly viable with the expansion of computing resources, algorithmic advances, and growing databases.

However, it remains challenging in practice to explore vast search spaces, and data-driven models still face difficulties with limited accuracy and interpretability. In this project, the research team will study the central hypothesis that materials properties for certain classes of materials such as Zintl phases are more accurately modeled through interactions between groups of atoms within a structure rather than interactions between individual atoms.

This physical intuition will be used to build a new suite of methods integrating advanced computer simulations with state-of-the-art machine learning (ML) models for enabling fast inverse materials design. In addition to improving accuracy and speed, these methods will be designed to enable interpretation and scientific understanding of the important interactions between groups of atoms in the material that contribute to desired properties.

The research team will use these methods to discover new Zintl phases and work with experimental collaborators to validate their discoveries. The interdisciplinary research team of computational materials scientists and computer scientists is well positioned to advance knowledge in these challenging problems of fundamental and technological interest.

This project also supports training of graduate and undergraduate students to be the next generation of leaders in interdisciplinary research at the intersection of materials and data science. Activities planned within this context include the development of cross-disciplinary seminars and workshops on data-enabled materials discovery and course materials introducing materials applications into ML courses. The project will also support public engagement through a podcast on ML for materials science.

TECHNICAL SUMMARY

This award supports computational and data science-enabled inverse design of a class of materials called Zintl phases that have applications in thermoelectrics, batteries, and photovoltaics, among others. Computational design of new materials has become increasingly viable with advances in computing power and methodologies. However, exploration of large chemical spaces with millions of compounds is still computationally intractable for ab-initio methods.

Machine learning (ML) has emerged as a means to accelerate such large explorations, but popular ML methods still have limitations in accuracy and interpretability. In this project, the research team will develop novel computational methods for materials design focused on the central idea that crystal properties are better modeled through interactions between motifs (groups of atoms) within a structure rather than between individual atoms.

The team will apply this idea to design new Zintl phases, where the structural motifs strongly influence functional properties. Zintl phases are an under-explored class of compounds, presenting opportunities to discover and design new phases. The ultimate goal of this project is to discover Zintl phases with new compositions and structure types and learn relationships between their compositions, structural motifs, and functional properties that will allow inverse design of Zintl phases with tailored properties.

The team will use chemical decorations and Lego-like assembly to "construct" Zintl phases and develop hierarchical graph representation algorithms to automatically learn the relationships between structural motifs and functional properties.

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.