28 citations · 69 across the 10 of their papers we have counts for
10 papers
Machine-learning approach for the phase stability and mechanical properties of disordered alloys at finite temperature
Rutchapon Hunkao, Urvesh Patil, Stefano Sanvito
The prediction of stable alloys forming solid-state solutions across large portions of the composition space is a serious theoretical challenge, since one has to evaluate the Gibbs…
A charge-density machine-learning workflow for computing the infrared spectrum of molecules
Suman Hazra, Urvesh Patil, Stefano Sanvito
We present a machine-learning workflow for the calculation of the infrared spectrum of molecules, and more generally of other temperature-dependent electronic observables. The main…
A general formalism for machine-learning models based on multipolar-spherical harmonics
Michelangelo Domina, Stefano Sanvito
The formulation of descriptors of the local chemical environment, enabling the construction of machine-learning models, is usually obtained by studying the properties of the expans…
Machine-learning semi-local exchange-correlation functionals for Kohn-Sham density functional theory of the Hubbard model
Eoghan Cronin, Rajarshi Tiwari, Stefano Sanvito
The Hubbard model provides a test bed to investigate the complex behaviour arising from electron-electron interaction in strongly-correlated systems and naturally emerges as the fo…
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…
Electric field response of strongly correlated one-dimensional metals: a Bethe-Ansatz density functional theory study
A. Akande, S. Sanvito
We present a theoretical study on the response properties to an external electric field of strongly correlated one-dimensional metals. Our investigation is based on the recently de…
Structural relaxation effects on interface and transport properties of Fe/MgO(001) tunnel junctions
Xiaobing Feng, O. Bengone, M. Alouani +3
The interface structure of Fe/MgO(100) magnetic tunnel junctions predicted by density functional theory (DFT) depends significantly on the choice of exchange and correlation functi…