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The development of modern ab initio methods has rapidly increased our understanding of physics, chemistry and materials science. Unfortunately, intensive ab initio calculations are intractable for large and complex systems. On the other…

The advancement of machine learning technologies has revolutionized the search and optimization of material properties. These algorithms often rely on theoretical calculations, such as density functional theory (DFT), for data inputs and…

Control of dopants in silicon remains the most important approach to tailoring the properties of electronic materials for integrated circuits, with Group V impurities the most important n-type dopants. At the same time, silicon is finding…

DFT is used throughout nanoscience, especially when modeling spin-dependent properties that are important in spintronics. But standard quantum chemical methods (both CCSD(T) and self-consistent semilocal density functional calculations)…

The stochastic density functional theory (DFT) [Phys. Rev. Lett. 111, 106402 (2013)] is a valuable linear scaling approach to Kohn-Sham DFT that does not rely on the sparsity of the density matrix. Linear (and often sub-linear) scaling is…

Neural Network Potentials (NNPs) have attracted significant attention as a method for accelerating density functional theory (DFT) calculations. However, conventional NNP models typically do not incorporate spin degrees of freedom, limiting…

We perform hybrid-density functional calculations to investigate the charged defect formation energy of native point defects in $Mg_2 Si$, $Mg_2 Sn$, and their solid solutions. The band gap correction by hybrid-density functional is found…

The transport properties of diluted magnetic semiconductors (DMS) are calculated using dynamical mean field theory (DMFT) and Boltzmann transport theory. Within DMFT we study the density of states and the dc-resistivity, which are strongly…

Electromagnetic properties of doped perovskites depend sensitively on the doping level. Both the superconducting transition temperature of Bi2Sr2Ca(Pr)Cu2O8+d compounds and the magnetic and electronic transport properties of La(Sr)Co(Fe)O3…

The electronic properties of pure and As-doped Si nanowires with radii up to 9.53 nm are studied using large scale density functional theory (DFT) calculations. We show that, for the undoped nanowires, the DFT bandgap reduces with…

While doping is widely used for tuning physical properties of perovskites in experiments, it remains a challenge to exactly know how doping achieves the desired effects. Here, we propose an empirical and computationally tractable model to…

Orbital-free (OF) methods promise significant speed-up of computations based on density functional theory (DFT). In this field, the development of accurate kinetic-energy density functionals remains an open question. In this chapter we…

Molecules can form myriad crystalline polymorphs, each with distinct properties affecting their performance across diverse applications, from pharmaceuticals to functional materials and more. Predicting the thermodynamically most stable…

Density Functional Theory (DFT) is widely used for first-principles simulations in chemistry and materials science, but its computational cost remains a key limitation for large systems. Motivated by recent advances in ML-based…

Making devices with graphene necessarily involves making contacts with metals. We use density functional theory to study how graphene is doped by adsorption on metal substrates and find that weak bonding on Al, Ag, Cu, Au and Pt, while…

Bi$_2$Se$_3$ is one of the most promising topological insulators, but it suffers from intrinsic n-doping due to Se-vacancies, which shifts the Fermi level into the bulk conduction band, leading to topologically trivial carriers. Recently it…

We present a systematic, quasi-automated methodology for generating electronic models in the framework of second-principles density functional theory (SPDFT). This approach enables the construction of accurate and computationally efficient…

We develop new transfer learning algorithms to accelerate prediction of material properties from ab initio simulations based on density functional theory (DFT). Transfer learning has been successfully utilized for data-efficient modeling in…

Machine learning models of materials$^{1-5}$ accelerate discovery compared to ab initio methods: deep learning models now reproduce density functional theory (DFT)-calculated results at one hundred thousandths of the cost of DFT$^{6}$. To…

To date, inorganic halide perovskite nanocrystals show promising contributions in emerging luminescent materials due to their high tolerance to defects. In particular, the development of cesium lead iodide (CsPbI3) has shown its efficiency…