Thrusts

Addressing the inherent complexity of materials modeling software, HeteroFAM aims to make this software accessible, intuitive, and user-friendly for experts and non-experts alike. By developing streamlined interfaces and intuitive workflows, the team is empowering individuals from a wide range of backgrounds and levels of expertise to harness the power of advanced computational tools for materials design and discovery. Through these efforts, we are striving to democratize access to cutting-edge computational methodologies. By fostering interdisciplinary collaboration and bridging the gap between theory and practice, we will help span the "valley of death" inherent in Exascale software development—enhancing the utility, efficiency, and scalability of computational tools tailored for the computational design of functional materials.

HeteroFAM consists of three thrusts that are focused on developing accessible, intuitive, and user-friendly materials modeling software for experts and non-experts alike.

Thrust 1: Machine Learning Workflows for Correlated and Topological Moiré Systems

Moiré materials, which form from the stacking and twisting of 2-D layers with mismatched lattice constants, have emerged as a platform for discovering correlated electron states and topological phases. These systems exhibit highly tunable properties, such as superconductivity, magnetism, and exotic quantum phenomena. This thrust will develop machine learning workflows that analyze electronic interactions and band structures in moiré systems, automate the discovery of new correlated and topological states by integrating high-throughput data with advanced computational models, and enable the rapid computational exploration of parameter spaces, such as twist angles, external fields, and layer alignments.

Figure highlighting the overarching approach in thrust 1 of the HeteroFAM project — (Figure by Ting Cao | the University of Washington)

Thrust 2: Prototype Development for Correlated Electron Physics and Emergent States

Understanding the physics of strongly correlated electrons is critical for discovering correlated electron states and topological phases in materials as well as designing materials with unique quantum properties. This thrust will create digital protypes to study emergent states in correlated materials, including complex spin configurations, quantum critical points, and novel magnetic phases. We will also build and refine next-generation models to understand and predict materials’ quantum behaviors from first principles. Finally, we will reinforce and validate machine learning and AI-driven workflows to help bridge the gap between theory and experiments.

Figure showing HeteroFAM Thrust 2 approach — (Figure by Nick Bauman | Pacific Northwest National Laboratory)

Thrust 3: AI-Driven Expert System for Spin Configurations in Transition Metal Oxides

Transition metal oxides are key materials with applications in energy, catalysis, and spintronics. Their complex spin configurations present both challenges and opportunities for material design. This thrust is designing an AI-driven expert system to predict and optimize spin configurations in transition metal oxides. We will also integrate first-principles calculations with data-driven approaches to uncover trends in magnetic and electronic behaviors. Throughout the process, we will also streamline workflows to design materials with specific magnetic properties for quantum computing and energy storage applications.

Figure showing the operating model of Thrust 3 of HeteroFAM — (Figure by Eric Bylaska | Pacific Northwest National Laboratory)