How dynamics changes ammonia cracking on iron surfaces

S Perego, L Bonati, S Tripathi, M Parrinello - ACS Catalysis, 2024 - ACS Publications
Being rich in hydrogen and easy to transport, ammonia is a promising hydrogen carrier.
However, a microscopic characterization of the ammonia cracking reaction is still lacking …

Constructing accurate and efficient general-purpose atomistic machine learning model with transferable accuracy for quantum chemistry

Y Chen, W Yan, Z Wang, J Wu, X Xu - Journal of Chemical Theory …, 2024 - ACS Publications
Density functional theory (DFT) has been a cornerstone in computational science, providing
powerful insights into structure–property relationships for molecules and materials through …

Analytical ab initio hessian from a deep learning potential for transition state optimization

ECY Yuan, A Kumar, X Guan, ED Hermes… - Nature …, 2024 - nature.com
Identifying transition states—saddle points on the potential energy surface connecting
reactant and product minima—is central to predicting kinetic barriers and understanding …

How does structural disorder impact heterogeneous catalysts? the case of ammonia decomposition on non-stoichiometric lithium imide

F Mambretti, U Raucci, M Yang, M Parrinello - ACS Catalysis, 2024 - ACS Publications
Among the many catalysts suggested for ammonia decomposition, Li2NH has been shown
to be quite promising. In the recent past, we have performed extensive ab initio-quality …

Data efficient machine learning potentials for modeling catalytic reactivity via active learning and enhanced sampling

S Perego, L Bonati - npj Computational Materials, 2024 - nature.com
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 …

Modeling Dynamic Catalysis at ab Initio Accuracy: The Need for Free-Energy Calculation

QY Fan, FQ Gong, YP Liu, HX Zhu, J Cheng - ACS Catalysis, 2024 - ACS Publications
Heterogeneous catalysis plays an increasingly important role in the modern chemical
industry. The active site, as proposed by Taylor, 1 is one of the most fundamental concepts …

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation

LG F. dos Santos, BT Nebgen, AEA Allen… - Journal of Chemical …, 2025 - ACS Publications
In the field of computational chemistry, predicting bond dissociation energies (BDEs)
presents well-known challenges, particularly due to the multireference character of reactive …

Following the dynamics of industrial catalysts under operando conditions

V Van Speybroeck - Proceedings of the National Academy of Sciences, 2024 - pnas.org
Catalytic reactions taking place in industrial processes are often performed under extreme
conditions of temperatures and pressures. A typical example is the Haber–Bosch process to …

Deep Learning of ab initio Hessians for Transition State Optimization

ECY Yuan, A Kumar, X Guan, ED Hermes… - arxiv preprint arxiv …, 2024 - arxiv.org
Identifying transition states--saddle points on the potential energy surface connecting
reactant and product minima--is central to predicting kinetic barriers and understanding …