Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …
conversion, chemical manufacturing and environmental remediation. Significant advances …
Artificial intelligence in chemistry: current trends and future directions
ZJ Baum, X Yu, PY Ayala, Y Zhao… - Journal of Chemical …, 2021 - ACS Publications
The application of artificial intelligence (AI) to chemistry has grown tremendously in recent
years. In this Review, we studied the growth and distribution of AI-related chemistry …
years. In this Review, we studied the growth and distribution of AI-related chemistry …
Guidelines to achieving high selectivity for the hydrogenation of α, β-unsaturated aldehydes with bimetallic and dilute alloy catalysts: a review
Selective hydrogenation of α, β-unsaturated aldehydes to unsaturated alcohols is a
challenging class of reactions, yielding valuable intermediates for the production of …
challenging class of reactions, yielding valuable intermediates for the production of …
Dilute alloys based on Au, Ag, or Cu for efficient catalysis: from synthesis to active sites
The development of new catalyst materials for energy-efficient chemical synthesis is critical
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …
Decoding reactive structures in dilute alloy catalysts
Rational catalyst design is crucial toward achieving more energy-efficient and sustainable
catalytic processes. Understanding and modeling catalytic reaction pathways and kinetics …
catalytic processes. Understanding and modeling catalytic reaction pathways and kinetics …
Active learning of reactive Bayesian force fields applied to heterogeneous catalysis dynamics of H/Pt
Atomistic modeling of chemically reactive systems has so far relied on either expensive ab
initio methods or bond-order force fields requiring arduous parametrization. Here, we …
initio methods or bond-order force fields requiring arduous parametrization. Here, we …
Quo vadis multiscale modeling in reaction engineering?–A perspective
This work reports the results of a perspective workshop held in summer 2021 discussing the
current status and future needs for multiscale modeling in reaction engineering. This …
current status and future needs for multiscale modeling in reaction engineering. This …
In Situ Surface Structures of PdAg Catalyst and Their Influence on Acetylene Semihydrogenation Revealed by Machine Learning and Experiment
PdAg alloy is an industrial catalyst for acetylene-selective hydrogenation in excess ethene.
While significant efforts have been devoted to increase the selectivity, there has been little …
While significant efforts have been devoted to increase the selectivity, there has been little …
Modeling Interfacial Dynamics on Single Atom Electrocatalysts: Explicit Solvation and Potential Dependence
Conspectus Single atom electrocatalysts, with noble metal-free composition, maximal atom
efficiency, and exceptional reactivity toward various energy and environmental applications …
efficiency, and exceptional reactivity toward various energy and environmental applications …
Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
We present a way to dramatically accelerate Gaussian process models for interatomic force
fields based on many-body kernels by mapping both forces and uncertainties onto functions …
fields based on many-body kernels by mapping both forces and uncertainties onto functions …