29 citations · 92 across the 11 of their papers we have counts for
11 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…
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…
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…
The role of solvent interfacial structural ordering in maintaining stable graphene dispersions
Urvesh Patil, Nuala M. Caffrey
Liquid phase exfoliation (LPE) is the most promising method for the low-cost, scalable production of two-dimensional nanosheets from their bulk counterparts. Extensive exfoliation…
Linear Jacobi-Legendre expansion of the charge density for machine learning-accelerated electronic structure calculations
Bruno Focassio, Michelangelo Domina, Urvesh Patil +2
Kohn-Sham density functional theory (KS-DFT) is a powerful method to obtain key materials' properties, but the iterative solution of the KS equations is a numerically intensive tas…
Cluster expansion constructed over Jacobi-Legendre polynomials for accurate force fields
Michelangelo Domina, Urvesh Patil, Matteo Cobelli +1
We introduce a compact cluster expansion method, constructed over Jacobi and Legendre polynomials, to generate highly accurate and flexible machine-learning force fields. The const…
Composition Dependence of the Charge Driven Phase Transition in Group-VI Transition Metal Dichalcogenides
Urvesh Patil, Nuala M. Caffrey
Materials exhibiting multiple stable phases can be used as functional components in electronic and optical applications if the phase transition is controllable. Group-VI transition…
Adsorption of common solvent molecules on graphene and MoS from first-principles
Urvesh Patil, Nuala M. Caffrey
Solvents are an essential element in the production and processing of two-dimensional (2D) materials. For example, the liquid phase exfoliation of layered materials requires a solv…
Resonant enhancement in nanostructured thermoelectric performance via electronic thermal conductivity engineering
Urvesh Patil, Bhaskaran Muralidharan
The use of an asymmetric broadening in the transport distribution, a characteristic of resonant structures, is proposed as a route to engineer a decrease in electronic thermal cond…
Recurrences in Driven Quantum Systems
V. Poduri, D. A. Browne, U. Patil
We consider an initially bound quantum particle subject to an external time-dependent field. When the external field is large, the particle shows a tendency to repeatedly return to…