Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling
Simulating catalytic reactivity under operative conditions poses a significant challenge due
to the dynamic nature of the catalysts and the high computational cost of electronic structure …
to the dynamic nature of the catalysts and the high computational cost of electronic structure …
Machine Learning Potentials for Heterogeneous Catalysis
The production of many bulk chemicals relies on heterogeneous catalysis. The rational
design or improvement of the required catalysts critically depends on insights into the …
design or improvement of the required catalysts critically depends on insights into the …
The Operando Nature of Isobutene Adsorbed in Zeolite H− SSZ− 13 Unraveled by Machine Learning Potentials Beyond DFT Accuracy
Unraveling the nature of adsorbed olefins in zeolites is crucial to understand numerous
zeolite‐catalyzed processes. A well‐grounded theoretical description critically depends on …
zeolite‐catalyzed processes. A well‐grounded theoretical description critically depends on …
A mini review on the applications of artificial intelligence (AI) in surface chemistry and catalysis
This review critically analyzes the incorporation of artificial intelligence (AI) in surface
chemistry and catalysis to emphasize the revolutionary impact of AI techniques in this field …
chemistry and catalysis to emphasize the revolutionary impact of AI techniques in this field …
Unraveling the nature of adsorbed isobutene in H-SSZ-13 with operando simulations at the top of Jacob's ladder
Unraveling the nature of adsorbed olefins in zeolites is crucial to understand numerous
zeolite-catalyzed processes. A well-grounded theoretical description critically depends on …
zeolite-catalyzed processes. A well-grounded theoretical description critically depends on …
[PDF][PDF] Fine-tuning Foundation Models for Molecular Dynamics: A Data-Efficient Approach with Random Features
Accurate modeling of atomistic interactions using machine learning potentials has become
an essential tool for molecular dynamics simulations. However, training these models …
an essential tool for molecular dynamics simulations. However, training these models …