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Found 6 papers, 5 papers with code
Complete and Efficient Graph Transformers for Crystal Material Property Prediction
Crystal structures are characterized by atomic bases within a primitive unit cell that repeats along a regular lattice throughout 3D space.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT).
Efficient and Equivariant Graph Networks for Predicting Quantum Hamiltonian
We consider the prediction of the Hamiltonian matrix, which finds use in quantum chemistry and condensed matter physics.
The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques.
Materials Science Computational Physics
Graph Neural Network for Hamiltonian-Based Material Property Prediction
Development of next-generation electronic devices for applications call for the discovery of quantum materials hosting novel electronic, magnetic, and topological properties.
