Machine learning (ML) integrated density functional theory (DFT) calculations have recently
been used to accelerate the design and discovery of heterogeneous catalysts such as single …

Traditional trial‐and‐error experiments and theoretical simulations have difficulty optimizing
catalytic processes and developing new, better‐performing catalysts. Machine learning (ML) …

The search for alternative non‑carbon-emitting uses of the huge reserves of natural gas has
renewed the interest on direct conversion of methane to value added chemicals. Oxidative …

Methane is a major component in natural gas, and its reforming or conversion to commodity
chemicals (oxygenates, olefins, aromatics) has attracted a lot of attention. While many …

In order to combine chaos theory and machine learning technology to predict sports
performance, a research on sports performance prediction based on chaos theory and …

Herein, multi-component La2O3-based catalysts for the oxidative coupling of methane
(OCM) were designed based on high-throughput screening (HTS) and literature datasets …

The design of efficient catalysts remains an challenge for complex systems such as the
oxidative coupling of methane (OCM), where reaction mechanisms are still debated …

Product yields of catalytic reaction networks are dependent on many factors, encompassing
both catalyst properties and reaction conditions. The oxidative coupling of methane (OCM) is …

We designed a series of single transition metal-anchored arsenene coordinated with
nitrogen atoms (TMN x@ As) as electrocatalysts for chlorine evolution reaction (CER) …

Identifying factors that influence interactions at the surface is still an active area of research.
In this work, the importance of analyzing bond length activations (BLact) along with …