Cover Image

BMach: a Bayesian machine for optimizing Hubbard U parameters in DFT+U with machine learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Das, Ritwik
Format:	Preprint
Published:	2024
Subjects:	Computational Physics Materials Science Strongly Correlated Electrons
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Classification of magnetic order from electronic structure by using machine learning
by: Jang, Yerin, et al.
Published: (2023)

Facilities and practices for linear response Hubbard parameters U and J in Abinit
by: MacEnulty, Lórien, et al.
Published: (2024)

Combining Quasiparticle Self-Consistent $GW$ and Machine-Learned DFT+$U$ in Search of Half-Metallic Heuslers
by: Cai, Zefeng, et al.
Published: (2026)

DFT+U+J with linear response parameters predicts non-magnetic oxide band gaps with hybrid-functional accuracy
by: Lambert, Daniel S., et al.
Published: (2021)

Validity of DFT+U band gaps in all its known functional forms
by: Burgess, Andrew C., et al.
Published: (2026)

From Density Functional Theory to Spin Hamiltonians: Magnetism in $d^5$ Honeycomb Compound OsCl$_3$
by: Das, Ritwik, et al.
Published: (2025)

Orbital-resolved DFT+U for molecules and solids
by: Macke, Eric, et al.
Published: (2023)

Explicit demonstration of the equivalence between DFT+U and the Hartree-Fock limit of DFT+DMFT
by: Carta, Alberto, et al.
Published: (2024)

Controlling Projection-Space Artifacts in DFT+U via Projection-Consistent U_{eff}
by: Raman, Manjula, et al.
Published: (2026)

Accurate bandgaps of photovoltaic kesterites from first-principles DFT+U
by: Burgess, Andrew C., et al.
Published: (2025)

Magnetization-Tunable Topological Phase Transitions in Ferromagnetic Kagome Monolayers of Co$_3$X$_3$Y$_2$ ($X=\mathrm{Sn},\mathrm{Pb}$; $Y=\mathrm{S},\mathrm{Se}$)
by: Das, Ritwik, et al.
Published: (2025)

First-Principles Calculation of Hubbard U for Terbium Metal under High Pressure
by: Burnett, Logan A., et al.
Published: (2024)

TBHubbard: tight-binding and extended Hubbard model database for metal-organic frameworks
by: Carvalho, Pamela C., et al.
Published: (2025)

Automated multiphase identification and refinement in powder diffraction using mismatch-tolerant machine learning
by: Yadav, Lalit, et al.
Published: (2026)

Quantum Modelling of Magnetism in Strongly Correlated Materials: Evaluating Constrained DFT and the Hubbard Model for Y114
by: Tantardini, Christian, et al.
Published: (2024)

Revisiting spin Hamiltonian parameters in a Kitaev material via Bayesian optimization of magnetization curves
by: Misawa, Takahiro, et al.
Published: (2026)

Sublattice modulated superconductivity in the Kagome Hubbard model
by: Schwemmer, Tilman, et al.
Published: (2023)

Comparative analysis of methods for calculating Hubbard parameters using cRPA
by: Reddy, Indukuru Ramesh, et al.
Published: (2025)

Getting the manifold right: The crucial role of orbital resolution in DFT+U for mixed d-f electron compounds
by: Warda, Kinga, et al.
Published: (2025)

Computational toolkit for predicting thickness of 2D materials using machine learning and autogenerated dataset by large language model
by: Ekuma, Chinedu
Published: (2024)

General linear correction method for DFT+X energy: application to U-M (M=Al, Ga, In) alloys under high pressure
by: Pan, X. L., et al.
Published: (2026)

Electronic and magnetic properties of (NdNiO$_3$)/(La$_{2/3}$Sr$_{1/3}$MnO$_3$) superlattices: a DFT+U perspective
by: Cardoso, Henrique M. M., et al.
Published: (2024)

Benchmarking total energies with Hund's J terms in Hubbard-corrected spin-crossover chemistry
by: MacEnulty, Lórien, et al.
Published: (2025)

DFT+DMFT study of correlated electronic structure in the monolayer-trilayer phase of La$_3$Ni$_2$O$_7$
by: Ouyang, Zhenfeng, et al.
Published: (2024)

Quantum control of Hubbard excitons
by: Baykusheva, D. R., et al.
Published: (2026)

Machine learning model for efficient nonthermal tuning of the charge density wave in monolayer NbSe$_2$
by: Benić, Luka, et al.
Published: (2025)

Implementation of the bilayer Hubbard model in a moiré heterostructure
by: Polovnikov, Borislav, et al.
Published: (2024)

Non-Fermi liquid and antiferromagnetic correlations with hole doping in the bilayer two-orbital Hubbard model of La$_3$Ni$_2$O$_7$ at zero temperature
by: Chen, Yin, et al.
Published: (2024)

Bandwidth Control and Symmetry Breaking in a Mott-Hubbard Correlated Metal
by: Shoham, Lishai, et al.
Published: (2023)

Self-consistent dynamical Hubbard functional for correlated solids
by: Chiarotti, Tommaso, et al.
Published: (2025)

A single crystal study of Kagome metals U$_2$Mn$_3$Ge and U$_2$Fe$_3$Ge
by: Lin, Wanyue, et al.
Published: (2024)

Haerter-Shastry kinetic magnetism and metallicity in the triangular Hubbard model
by: Sherif, Sogoud, et al.
Published: (2025)

Dynamical Hubbard approach to correlated materials: the case of transition-metal monoxides
by: Caserta, Mario, et al.
Published: (2025)

Optimization strategies developed on NiO for Heisenberg exchange coupling calculations using projector augmented wave based first-principles DFT+U+J
by: MacEnulty, Lórien, et al.
Published: (2023)

Oxygen vacancies in vanadium dioxide: A DFT$+V$ study
by: Leibnitz, Oskar, et al.
Published: (2025)

Intermediate-valence behavior in U2Rh2Sb
by: Legut, D., et al.
Published: (2025)

Solution of the mean-field Hubbard model of graphene rectangulenes
by: García-Fuente, Amador, et al.
Published: (2024)

Dynamical control of Coulomb interactions and Hubbard bands in monolayer 1T-TaS$_2$
by: Notter, Niklas, et al.
Published: (2025)

Beyond-Hubbard pairing in a cuprate ladder
by: Padma, Hari, et al.
Published: (2025)

The alloying of first-principles calculations with quasiparticle methodologies for the converged solution of the quantum many-electron states in the correlated compound Iron monoxide
by: Das, Suvadip
Published: (2025)