:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Sun, Liang, Chen, Mohan
Format:	Preprint
Published:	2024
Subjects:	Materials Science
Online Access:	https://arxiv.org/abs/2408.15062
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Machine learning based nonlocal kinetic energy density functional for simple metals and alloys
by: Sun, Liang, et al.
Published: (2023)

Fast and stable tight-binding framework for nonlocal kinetic energy density functional reconstruction in orbital-free density functional calculations
by: Chen, Yongshuo, et al.
Published: (2024)

Rationalizing defect formation energies in metals and semiconductors with semilocal density functionals
by: Bazantes, Jorge Vega, et al.
Published: (2026)

Exploring the energy landscape of aluminas through machine learning interatomic potential
by: Zhang, Lei, et al.
Published: (2024)

Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
by: Manzhos, Sergei, et al.
Published: (2025)

Systematic study of one-point kinetic energy density functionals for atomic nuclei
by: Shang, Tian Shuai, et al.
Published: (2026)

Deep learning of spectra: Predicting the dielectric function of semiconductors
by: Grunert, Malte, et al.
Published: (2024)

Adaptive energy reference for machine-learning models of the electronic density of states
by: How, Wei Bin, et al.
Published: (2024)

Machine learning of kinetic energy densities with target and feature averaging: better results with fewer training data
by: Manzhos, Sergei, et al.
Published: (2023)

The role of the solid-melt interface in accelerating the self-catalyzed growth kinetics of III-V semiconductors
by: Xi, Zhucong, et al.
Published: (2025)

A machine-learned kinetic energy model for light weight metals and compounds of group III-V elements
by: Lüder, Johann, et al.
Published: (2024)

Bridging electronic and classical density-functional theory using universal machine-learned functional approximations
by: Kelley, Michelle M., et al.
Published: (2024)

Can machines learn density functionals? Past, present, and future of ML in DFT
by: Akashi, Ryosuke, et al.
Published: (2025)

Interpretable machine learned predictions of adsorption energies at the metal--oxide interface
by: Nielsen, Marius Juul, et al.
Published: (2025)

Extending Nonlocal Kinetic Energy Density Functionals to Isolated Systems via a Density-Functional-Dependent Kernel
by: Sun, Liang, et al.
Published: (2025)

Computational discovery of bifunctional organic semiconductors for energy and biosensing
by: Kongo, Patrick Sorrel Mvoto, et al.
Published: (2026)

Mg$_{2}$Si and Ca$_{2}$Si semiconductors for photovoltaic applications: Calculations based on density-functional theory and the Bethe-Salpeter equation
by: Solet, Vinod Kumar, et al.
Published: (2024)

Symmetry-restricted energy landscapes as a benchmark for machine learned interatomic potentials
by: Parackal, Abhijith S, et al.
Published: (2026)

Truncated Non-Local Kinetic Energy Density Functionals for Simple Metals and Silicon
by: Sun, Liang, et al.
Published: (2023)

Occurrence of the collective Ziman limit of heat transport in cubic semiconductors: scattering channels and size effects
by: Sjakste, Jelena, et al.
Published: (2024)

Quasicrystal bulk and surface energies from density functional theory
by: Baek, Woohyeon, et al.
Published: (2024)

Accelerating the prediction of stacking fault energy by combining ab initio calculations and machine learning
by: Linda, Albert, et al.
Published: (2024)

A universal machine learning model for the electronic density of states
by: How, Wei Bin, et al.
Published: (2025)

Discovery of stable surfaces with extreme work functions by high-throughput density functional theory and machine learning
by: Schindler, Peter, et al.
Published: (2020)

Grad DFT: a software library for machine learning enhanced density functional theory
by: Casares, Pablo A. M., et al.
Published: (2023)

Deep learning density functional theory Hamiltonian in real space
by: Yuan, Zilong, et al.
Published: (2024)

Phonon-mediated renormalization of exciton energies and absorption spectra in polar semiconductors
by: Schebek, Maximilian, et al.
Published: (2024)

Universal materials model of deep-learning density functional theory Hamiltonian
by: Wang, Yuxiang, et al.
Published: (2024)

Cross-functional transferability in universal machine learning interatomic potentials
by: Huang, Xu, et al.
Published: (2025)

Pathway to lowest-energy structures and stress relaxation for the surface triple junction verified by machine learning
by: Fang, Yuan, et al.
Published: (2026)

Free-energy perturbation in the exchange-correlation space accelerated by machine learning: Application to silica polymorphs
by: Forslund, Axel, et al.
Published: (2025)

Machine learning based approach for solving atomic structures of nanomaterials combining pair distribution functions with density functional theory
by: Kløve, Magnus, et al.
Published: (2022)

Predicting ionic conductivity in solids from the machine-learned potential energy landscape
by: Maevskiy, Artem, et al.
Published: (2024)

Going beyond density functional theory accuracy: Leveraging experimental data to refine pre-trained machine learning interatomic potentials
by: Gumber, Shriya, et al.
Published: (2025)

Measuring vacancy-type defect density in monolayer semiconductors
by: Radic, Aleksandar, et al.
Published: (2024)

Accelerating QM/MM simulations of electrochemical interfaces through machine learning of electronic charge densities
by: Grisafi, Andrea, et al.
Published: (2024)

Efficient small-cell sampling for machine-learning potentials of multi-principal element alloys
by: Liu, Yan, et al.
Published: (2025)

Deep-learning density functional perturbation theory
by: Li, He, et al.
Published: (2024)

Driving force of atomic ordering in Fe$_{1-x}$Pt$_{x}$, investigated by density functional theory and machine-learning interatomic potentials Monte Carlo simulations
by: Tsuyama, Tomoyuki, et al.
Published: (2024)

General-purpose machine-learned potential for 16 elemental metals and their alloys
by: Song, Keke, et al.
Published: (2023)