85 citations · 104 across the 11 of their papers we have counts for
11 papers
ViBra: Configuration Interaction for Anharmonic Vibrational Spectroscopy and Quantum-Sampled Configuration Spaces
Raphael F. Ligorio, Marco Antonio Barroca, Alan Duriez +1
Quantum-centric workflows are a promising route to improving the accuracy of property predictions in computational chemistry and materials science. By integrating quantum sampling…
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…
TBHubbard: tight-binding and extended Hubbard model database for metal-organic frameworks
Pamela C. Carvalho, Federico Zipoli, Alan C. Duriez +5
Metal-organic frameworks (MOFs) are porous materials composed of metal ions and organic linkers. Due to their chemical diversity, MOFs can support a broad range of applications in…
Scaling active spaces in simulations of surface reactions through sample-based quantum diagonalization
Marco Antonio Barroca, Tanvi Gujarati, Vidushi Sharma +6
Quantum-chemical simulations are essential for predicting energies of chemical reactions. Accurately solving the many-body Schrödinger equation for reagent and product states of mo…
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…
Simulating carbon mineralization at pore scale in capillary networks of digital rock
David A. Lazo Vasquez, Jaione Tirapu Azpiroz, Rodrigo Neumann Barros Ferreira +4
Predicting the geometrical evolution of the pore space in geological formations due to fluid-solid interactions has applications in reservoir engineering, oil recovery, and geologi…
Exploration of Quantum Computing in Materials Discovery for Direct Air Capture Applications
Marco Antonio Barroca, Rodrigo Neumann Barros Ferreira, Mathias Steiner
Direct air capture (DAC) of carbon dioxide is a promising method for mitigating climate change. Solid sorbents, such as metal-organic frameworks, are currently being tested for DAC…
Optimizing carbon dioxide trapping for geological storage
Jaione Tirapu Azpiroz, Ronaldo Giro, Rodrigo Neumann Barros Ferreira +8
Carbon dioxide (CO2) trapping in capillary networks of reservoir rocks is a pathway to long-term geological storage. At pore scale, the CO2 trapping potential depends on injection…
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…
Predicting polymerization reactions via transfer learning using chemical language models
Brenda S. Ferrari, Matteo Manica, Ronaldo Giro +2
Polymers are candidate materials for a wide range of sustainability applications such as carbon capture and energy storage. However, computational polymer discovery lacks automated…
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…