Machine learning models are poised to make a transformative impact on chemical sciences
by dramatically accelerating computational algorithms and amplifying insights available from …

Over the last few years, extraordinary advances in experimental and theoretical tools have
allowed us to monitor and control matter at short time and atomic scales with a high degree …

Density functional theory (DFT) has become a routine tool for the computation of electronic
structure in the physics, materials and chemistry fields. Yet the application of traditional DFT …

Real-space grids are a powerful alternative for the simulation of electronic systems. One of
the main advantages of the approach is the flexibility and simplicity of working directly in real …

Abstract We present DFT-FE 1.0, building on DFT-FE 0.6 Motamarri et al.(2020)[28], to
conduct fast and accurate large-scale density functional theory (DFT) calculations …

We have ported and optimized the graphics processing unit (GPU)-accelerated QUICK and
AMBER-based ab initio quantum mechanics/molecular mechanics (QM/MM) implementation …

We show that deep neural networks can be integrated into, or fully replace, the Kohn-Sham
density functional theory (DFT) scheme for multielectron systems in simple harmonic …

The conductor-like polarization model (C-PCM) with switching/Gaussian smooth
discretization is a widely used implicit solvation model in chemical simulations. However, its …

Accurate large-scale first principles calculations based on density functional theory (DFT) in
metallic systems are prohibitively expensive due to the asymptotic cubic scaling …

Ab initio molecular dynamics is an irreplaceable technique for the realistic simulation of
complex molecular systems and processes from first principles. This paper proposes a …