Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and
powerful computational resources, impacting many fields, including protein structural …

Combining materials science, artificial intelligence (AI), physical chemistry, and other
disciplines, materials informatics is continuously accelerating the vigorous development of …

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics
simulations using machine learning potentials known as Deep Potential (DP) models. This …

Neural networks (NNs) often assign high confidence to their predictions, even for points far
out of distribution, making uncertainty quantification (UQ) a challenge. When they are …

Electron density-based implicit solvation models are a class of techniques for quantifying
solvation effects and calculating free energies of solvation without an explicit representation …

Quantum mechanical (QM) calculations are critical in quantitatively understanding the
relationship between the structure and physicochemical properties of various chemical …

Quantum mechanics (QM) approaches (DFT, MP2, CCSD (T), etc.) play an important role in
calculating molecules and crystals with a high accuracy and acceptable efficiency. In recent …

Atomistic simulations have become an invaluable tool for industrial applications ranging
from the optimization of protein-ligand interactions for drug discovery to the design of new …

The shuttle effect of lithium polysulfides (LiPS) leads to fast capacity loss in lithium-sulfur
batteries, which hinders the practical applications and makes the discovery of shuttle effect …

Discovery of new energy materials with thermal stability and special electro-optical
properties has always been the goal and challenge of material science. As an important …