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 …

With the development of advanced electronic devices and electric power systems, polymer-
based dielectric film capacitors with high energy storage capability have become particularly …

Since their introduction about 25 years ago, machine learning (ML) potentials have become
an important tool in the field of atomistic simulations. After the initial decade, in which neural …

In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Chemical vapor deposition has been most recently employed to fabricate centimeter-scale
high-quality single-layer MoSi 2 N 4 (Science; 2020; 369; 670). Motivated by this exciting …

Abstract Machine learning potentials have become an important tool for atomistic
simulations in many fields, from chemistry via molecular biology to materials science. Most of …

The design of heterogeneous catalysts relies on understanding the fundamental surface
kinetics that controls catalyst performance, and microkinetic modeling is a tool that can help …

Advances in machine learned interatomic potentials (MLIPs), such as those using neural
networks, have resulted in short-range models that can infer interaction energies with near …

Electronically excited states of molecules are at the heart of photochemistry, photophysics,
as well as photobiology and also play a role in material science. Their theoretical description …

Data‐driven science is heralded as a new paradigm in materials science. In this field, data is
the new resource, and knowledge is extracted from materials datasets that are too big or …