DFT exchange: sharing perspectives on the workhorse of quantum chemistry and materials science

AM Teale, T Helgaker, A Savin, C Adamo… - Physical chemistry …, 2022 - pubs.rsc.org
In this paper, the history, present status, and future of density-functional theory (DFT) is
informally reviewed and discussed by 70 workers in the field, including molecular scientists …

Smooth, exact rotational symmetrization for deep learning on point clouds

S Pozdnyakov, M Ceriotti - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Point clouds are versatile representations of 3D objects and have found widespread
application in science and engineering. Many successful deep-learning models have been …

Probing the effects of broken symmetries in machine learning

MF Langer, SN Pozdnyakov… - … Learning: Science and …, 2024 - iopscience.iop.org
Symmetry is one of the most central concepts in physics, and it is no surprise that it has also
been widely adopted as an inductive bias for machine-learning models applied to the …

[PDF][PDF] DFTK: A Julian approach for simulating electrons in solids

MF Herbst, A Levitt, E Cancès - Proceedings of the …, 2021 - proceedings.juliacon.org
Density-functional theory (DFT) is a widespread method for simulating the quantum-
chemical behaviour of electrons in matter. It provides a first-principles description of many …

[BOOK][B] Solving Nonlinear Equations with Iterative Methods: Solvers and Examples in Julia

CT Kelley - 2022 - SIAM
This book on solvers for nonlinear equations is a user-oriented guide to algorithms and
implementation. It is a sequel to [111], which used MATLAB for the solvers and examples …

NQCDynamics. jl: A Julia package for nonadiabatic quantum classical molecular dynamics in the condensed phase

J Gardner, OA Douglas-Gallardo, WG Stark… - The Journal of …, 2022 - pubs.aip.org
Accurate and efficient methods to simulate nonadiabatic and quantum nuclear effects in high-
dimensional and dissipative systems are crucial for the prediction of chemical dynamics in …

Body-ordered approximations of atomic properties

J Thomas, H Chen, C Ortner - Archive for Rational Mechanics and …, 2022 - Springer
We show that the local density of states (LDOS) of a wide class of tight-binding models has a
weak body-order expansion. Specifically, we prove that the resulting body-order expansion …

Predictive mixing for density functional theory (and other fixed-point problems)

LD Marks - Journal of Chemical Theory and Computation, 2021 - ACS Publications
Density functional theory calculations use a significant fraction of current supercomputing
time. The resources required scale with the problem size, the internal workings of the code …

A robust and efficient line search for self-consistent field iterations

MF Herbst, A Levitt - Journal of Computational Physics, 2022 - Elsevier
We propose a novel adaptive dam** algorithm for the self-consistent field (SCF) iterations
of Kohn-Sham density-functional theory, using a backtracking line search to automatically …

Numerical methods for Kohn–Sham models: Discretization, algorithms, and error analysis

E Cancès, A Levitt, Y Maday, C Yang - Density Functional Theory …, 2022 - Springer
Numerical Methods for Kohn–Sham Models: Discretization, Algorithms, and Error Analysis |
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