Machine-learning models for predicting adsorption energies on metallic surfaces often rely
on basic elemental properties and electronic and geometric descriptors. Here, we apply …

The configuration spaces for bimetallic AuPd nanoclusters of various sizes are explored
efficiently and analyzed accurately by combining genetic algorithms with neural networks …

The interactions between propylene and heterogeneous catalysts play a crucial role in
determining the catalytic performance in various propylene-related reactions. In this work …

Intermetallic compounds are promising materials in numerous fields, especially those
involving surface interactions, such as catalysis. A key factor to investigate their surface …

Despite the rapid development of computational methods, including density functional
theory (DFT), predicting the performance of a catalytic material merely based on its atomic …

We present a systematic study into the effect of adding atomic adsorption configurations into
the training and validation dataset for a neural network's predictions of the adsorption …

In the recent decades, laser powder bed fusion (LPBF) of aluminum alloy has been widely
used in many fields. However, the poor surface quality, inevitable defects, and limited …

Nowadays, biodegradable metals and alloys, as well as their corrosion behavior, are of
particular interest. The corrosion process of metals and alloys under various harsh …

Abstract Machine learning has been widely applied to accelerate molecular simulations and
predict molecular/material properties. Machine learning accomplishes this by leveraging the …