28 citations · 69 across the 10 of their papers we have counts for
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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…
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…
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…