Related papers: Predicting structure-dependent Hubbard U parameter…
Density-functional theory with on-site $U$ and inter-site $V$ Hubbard corrections (DFT+$U$+$V$) is a powerful and accurate method for predicting various properties of transition-metal compounds. However, its accuracy depends critically on…
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules. The performance of each…
The formally exact framework of equilibrium Density Functional Theory (DFT) is capable of simultaneously and consistently describing thermodynamic and structural properties of interacting many-body systems in arbitrary external potentials.…
Density functional theory is the standard theory for computing the electronic structure of materials, which is based on a functional that maps the electron density to the energy. However, a rigorous form of the functional is not known and…
We propose a self-consistent site-dependent Hubbard $U$ approach for DFT+$U$ calculations of defects in complex transition-metal oxides, using Hubbard parameters computed via linear-response theory. The formation of a defect locally…
We present a machine learning (ML) method for predicting electronic structure correlation energies using Hartree-Fock input.The total correlation energy is expressed in terms of individual and pair contributions from occupied molecular…
Providing highly simplified models of strongly correlated electronic systems that challenge {\it ab initio} calculations can serve as a valuable testing ground to improve these methods. In this study, we present a comprehensive study of the…
The properties of electrons in matter are of fundamental importance. They give rise to virtually all molecular and material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant…
The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOFs are widely known for outstanding adsorption properties, so…
DFT+U provides a convenient, cost-effective correction for the self-interaction error (SIE) that arises when describing correlated electronic states using conventional approximate density functional theory (DFT). The success of a DFT+U(+J)…
Density functional theory augmented with Hubbard-$U$ corrections (DFT+$U$) is currently one of the widely used methods for first-principles electronic structure modeling of insulating transition metal oxides (TMOs). Since $U$ is relatively…
The ground state electron density -- obtainable using Kohn-Sham Density Functional Theory (KS-DFT) simulations -- contains a wealth of material information, making its prediction via machine learning (ML) models attractive. However, the…
The accurate prediction of the electronic properties of materials at a low computational expense is a necessary conditions for the development of effective high-throughput quantum-mechanics (HTQM) frameworks for accelerated materials…
Metal-organic frameworks (MOFs) are promising materials for methane capture due to their high surface area and tunable properties. Metal substitution represents a powerful strategy to enhance MOF performance, yet systematic exploration of…
The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system's Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature.…
A density functional theory (DFT) approach to computing transition metal oxide heat of formation without adjustable parameters is presented. Different degrees of $d$-electron localization in oxides are treated within the DFT+$U$ approach…
The LDA+DMFT method is a very powerful tool for gaining insight into the physics of strongly correlated materials. It combines traditional ab-initio density-functional techniques with the dynamical mean-field theory. The core aspects of the…
The design of novel cathode materials for Li-ion batteries would greatly benefit from accurate first-principles predictions of structural, electronic, and magnetic properties as well as intercalation voltages in compounds containing…
We present the Materials Learning Algorithms (MALA) package, a scalable machine learning framework designed to accelerate density functional theory (DFT) calculations suitable for large-scale atomistic simulations. Using local descriptors…
Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose…