28 citations · 69 across the 10 of their papers we have counts for
4 papers · 1 filter
1D Kinetic Energy Density Functionals learned with Symbolic Regression
Michael A. J. Mitchell, Teresa Del Aguila Ferrandis, Stefano Sanvito
Orbital-free density functional theory promises to deliver linear-scaling electronic structure calculations. This requires the knowledge of the non-interacting kinetic-energy densi…
Covariant Jacobi-Legendre expansion for total energy calculations within the projector-augmented-wave formalism
Bruno Focassio, Michelangelo Domina, Urvesh Patil +2
Machine-learning models can be trained to predict the converged electron charge density of a density functional theory (DFT) calculation. In general, the value of the density at a…
Quantum-Accurate Machine Learning Potentials for Metal-Organic Frameworks using Temperature Driven Active Learning
Abhishek Sharma, Stefano Sanvito
Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs…
Predicting The One-Particle Density Matrix With Machine Learning
S. Hazra, U. Patil, S. Sanvito
Two of the most widely used electronic structure theory methods, namely Hartree-Fock and Kohn-Sham density functional theory, both requires the iterative solution of a set of Schrö…