Computational strategies to address the catalytic activity of nanoclusters

Unveiling the Au Surface Reconstruction in a CO Environment by Surface Dynamics and Ab Initio Thermodynamics

Surface reconstruction changes the atomic configuration of the metal surface and thus alters its intrinsic physical and chemical properties. Recent in situ experiments have shown a variety of surface reconstructions under reaction conditions, but how to effectively predict and characterize these structures remains challenging. Herein, we combine a DFT-based kinetic Monte Carlo simulation method and ab initio thermodynamics to explore the low-energy configurations of metal surface reconstructions, which takes the surface dynamics under the reactive environment into account. We systematically simulate 13 Au surfaces ((100), (110), (111), (210), (211), (221), (310), (311), (320), (321), (322), (331), and (332)) in the CO environment and identify 19 candidate reconstruction patterns driven by CO adsorption. The breakup of the original surfaces is attributed to the lateral interactions among the nearest-neighboring adsorbates. This work provides an efficient approach to unveil the reconstructed metal surface structures in reactive environments for guiding the experiments.

AI in drug development: a multidisciplinary perspective

The introduction of a new drug to the commercial market follows a complex and long process that typically spans over several years and entails large monetary costs due to a high attrition rate. Because of this, there is an urgent need to improve this process using innovative technologies such as artificial intelligence (AI). Different AI tools are being applied to support all four steps of the drug development process (basic research for drug discovery; pre-clinical phase; clinical phase; and postmarketing). Some of the main tasks where AI has proven useful include identifying molecular targets, searching for hit and lead compounds, synthesising drug-like compounds and predicting ADME-Tox. This review, on the one hand, brings in a mathematical vision of some of the key AI methods used in drug development closer to medicinal chemists and, on the other hand, brings the drug development process and the use of different models closer to mathematicians. Emphasis is placed on two aspects not mentioned in similar surveys, namely, Bayesian approaches and their applications to molecular modelling and the eventual final use of the methods to actually support decisions. Promoting a perfect synergy.