Poster
Learning the Electronic Hamiltonian of Large Atomic Structures
Chen Hao Xia · Manasa Kaniselvan · Alexandros Nikolaos Ziogas · Marko Mladenovic · Rayen Mahjoub · Alexander Maeder · Mathieu Luisier
West Exhibition Hall B2-B3 #W-117
Our work focuses on the machine learning of Hamiltonian matrices for large systems of atoms. The Hamiltonian matrix is an operator that allows key information about a material to be extracted, including how well it conducts electricity. It is therefore an integral part of material/device research and is normally computed from the ground up using physics approaches.Previous work on this topic is largely restricted to either small, isolated groups of atoms (molecules) or atomic structures with repeating patterns (e.g. crystals). In real life, however, materials are rarely perfect, often containing defects, disorder, and interfaces. To capture these details, the Hamiltonian of large numbers of atoms (1000+) in various arrangements needs to be computed, making previous ground-up computational methods unfeasible. In this work, we aim to overcome this open problem by proposing a local graph network combined with an approach that breaks down large atomic graphs into small, independent slices. Besides overcoming memory limitations of hardware, it also allows for flexible, efficient parallel training while maintaining the correct atomic neighborhoods. In short, our work bridges the gap between ML methods and real-life large scale materials applications.