EAGER: SSMCDAT2023: Revealing Local Symmetry Breaking in Intermetallics: Combining Statistical Mechanics and Machine Learning in PDF Analysis

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PART 1: NON-TECHNICAL SUMMARYThis award is made on an EAGER proposal. It supports progress on a project advanced at the SSMCDAT 2023 Datathon held at Lehigh University. This EAGER project furthers the understanding of the structure of semiconductors, especially those with a high degree of atomic disorder. Semiconductors are critical to modern electronics, with their physical properties, like conductivity and band gap, largely depending on their atomic arrangements. Despite significant progress in studying well-ordered crystals over the past century, understanding the structure of disordered crystals remains a challenge. Key to this understanding is how factors such as composition and synthesis methods affect atomic structure and, consequently, properties like electronic and thermal transport. This project employs cutting-edge artificial intelligence techniques, specifically symmetry-aware neural networks, to predict the forces between atoms in disordered materials. These predictions aid in modeling the results of experimental measurements, offering insight into these complex structures. Given the inherent complexity of these disordered materials, the project also involves developing innovative ways to visualize these structures at an atomic level, enabling researchers to identify patterns between semiconductor structure and electronic properties.PART 2: TECHNICAL SUMMARYEstablishing predictive relationships between composition, processing conditions, and material properties in intermetallics and alloys has been hampered by difficulties in understanding local structure. In an intermetallic material, the local structure includes both the chemical tiling on the lattice (i.e., motifs) and atomic distortions arising from asymmetric coordination environments. Even with high fidelity determination of such structures, the diversity of local structures remains challenging to visualize. As such, both measurement and understanding of local structure remain key challenges, making current intermetallic materials development efforts primarily empirical. This EAGER project addresses these challenges with an integrated computational-experimental approach. The Ge(1-x)MnxTe system serves as a model system due to its significant solubility, strong neutron scattering, and continuous phase transition with temperature and composition. Experimental insights are gleaned from neutron pair distribution function measurements (PDF) collected from intermetallics with various compositions. Computational insights into fitting these PDF measurements hinge on (i) recent advancements in equivariant neural net-based force fields for molecular dynamics simulations and (ii) a robust statistical mechanics treatment of configurational and vibrational energies. This fitting procedure remains true to the underlying bonding energetics within the intermetallic or alloy, meaning the resulting structures can be scrutinized for their distribution of local structures. A key part of this project is the featurization of the resulting structural distortions and bonding. Featurization optimization, unsupervised learning, and visualization developments will allow insights into the impact of processing conditions and composition on the local structure of these materials.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.