The development of machine learning models for electrocatalysts requires a broad set of
training data to enable their use across a wide variety of materials. One class of materials …

Extensive trial and error in the variable space is the main cause of low efficiency and high
cost in material development. The experimental tasks can be reduced significantly in the …

Abstract 2D materials have offered essential contributions to boosting biocatalytic efficiency
in diverse biomedical applications due to the intrinsic enzyme‐mimetic activity and massive …

Electrosynthesis of hydrogen peroxide via selective two-electron transfer oxygen reduction
or water oxidation reactions offers a cleaner, cost-effective alternative to anthraquinone …

Recent advances in machine learning (ML) have led to substantial performance
improvement in material database benchmarks, but an excellent benchmark score may not …

In this work, we demonstrate a method to quantify uncertainty in corrections to density
functional theory (DFT) energies based on empirical results. Such corrections are commonly …

Fluorine substitution is a critical enabler for improving the cycle life and energy density of
disordered rocksalt (DRX) Li‐ion battery cathode materials which offer prospects for high …

Databases for two-dimensional materials host numerous ferromagnetic materials without the
vital information of Curie temperature since its calculation involves a manually intensive …

Structure characterization and classification is frequently based on local environment
information of all or selected atomic sites in the crystal structure. Therefore, reliable and …

DFT+ U provides a convenient, cost-effective correction for the self-interaction error (SIE)
that arises when describing correlated electronic states using conventional approximate …