Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

Proton-coupled electron transfer (PCET) plays an essential role in a wide range of
electrocatalytic processes. A vast array of theoretical and computational methods have been …

Carbon dots (CDs), as emerging carbon nanomaterials, have been regarded as promising
alternatives for electroluminescent light‐emitting diodes (LEDs) owing to their distinct …

Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …

Abstract Machine-learned force fields combine the accuracy of ab initio methods with the
efficiency of conventional force fields. However, current machine-learned force fields …

The rational design of molecules with desired properties is a long-standing challenge in
chemistry. Generative neural networks have emerged as a powerful approach to sample …

Molecular dynamics simulation is an important tool in computational materials science and
chemistry, and in the past decade it has been revolutionized by machine learning. This rapid …

Recent years have seen vast progress in the development of machine learned force fields
(MLFFs) based on ab-initio reference calculations. Despite achieving low test errors, the …