Conspectus Density functional theory (DFT) calculations are used in over 40,000 scientific
papers each year, in chemistry, materials science, and far beyond. DFT is extremely useful …

Phase transitions are a common phenomenon in condensed matter and act as a critical
degree of freedom that can be employed to tailor the mechanical or electronic properties of …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Electronic wave function calculation is a fundamental task of computational quantum
chemistry. Knowledge of the wave function parameters allows one to compute physical and …

We use voxel deep neural networks to predict energy densities and functional derivatives of
electron kinetic energies for the Thomas–Fermi model and Kohn–Sham density functional …

Machine learning (ML) has recently gained attention as a means to develop more accurate
exchange-correlation (XC) functionals for density functional theory, but functionals …

Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …

We present two machine learning methodologies that are capable of predicting diffusion
Monte Carlo (DMC) energies with small data sets (≈ 60 DMC calculations in total). The first …

We introduce multiconfiguration data-driven functional methods (MC-DDFMs), a group of
methods which aim to correct the total or classical energy of a qualitatively accurate …

The traditional approach for materials discovery has been the domain of experimentalists,
where elemental composition and synthesis conditions are often based on a trial-and-error …