85 citations · 96 across the 6 of their papers we have counts for
6 papers
Graph Neural Network Predictions of Carbon 1s Binding Energies with Near-Experimental Accuracy
Adam E. A. Fouda, Joshua Zhou, Rodrigo Ferreira +10
Graph neural networks are promising architectures for fast, accurate and transferable predictions of core-electron binding energies, which depend on the local bond environment. Her…
Equivariant Interatomic Potentials without Tensor Products
Thiago Reschützegger, Sarp Aykent, Gabriel Jacob Perin +5
Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical space…
Computing band gaps of periodic materials via sample-based quantum diagonalization
Alan Duriez, Pamela C. Carvalho, Marco Antonio Barroca +7
A key objective of computational solid state physics is to predict electronic properties of periodic materials. However, electronic structure simulations based on density functiona…
Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions
Yuri Alexeev, Maximilian Amsler, Paul Baity +124
Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of e…
Symbolic Learning for Material Discovery
Daniel Cunnington, Flaviu Cipcigan, Rodrigo Neumann Barros Ferreira +1
Discovering new materials is essential to solve challenges in climate change, sustainability and healthcare. A typical task in materials discovery is to search for a material in a…
Discovery of Novel Reticular Materials for Carbon Dioxide Capture using GFlowNets
Flaviu Cipcigan, Jonathan Booth, Rodrigo Neumann Barros Ferreira +2
Artificial intelligence holds promise to improve materials discovery. GFlowNets are an emerging deep learning algorithm with many applications in AI-assisted discovery. By using GF…