Iron nitride formation and decomposition during ammonia decomposition over a wustite-based bulk iron catalyst

M Purcel, S Berendts, L Bonati, S Perego, A Muller… - ACS …, 2024 - ACS Publications
Hydrogen production using renewable sources shows great promise in lowering the
dependence on fossil fuels in our current energy system, but challenges persist in its storage …

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 …

Machine Learning Potentials for Heterogeneous Catalysis

A Omranpour, J Elsner, KN Lausch, J Behler - ACS Catalysis, 2024 - ACS Publications
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 …

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 …

ML-Accelerated Automatic Process Exploration Reveals Facile O-Induced Pd Step-Edge Restructuring on Catalytic Time Scales

P Poths, KC Lai, F Cannizzaro, C Scheurer… - ACS …, 2024 - ACS Publications
We combine automatic process exploration with an iteratively trained machine-learning
interatomic potential to systematically identify elementary processes occurring during the …

Everything everywhere all at once, a probability-based enhanced sampling approach to rare events

E Trizio, P Kang, M Parrinello - arxiv preprint arxiv:2410.17029, 2024 - arxiv.org
The problem of studying rare events is central to many areas of computer simulations. In a
recent paper [Kang, P., et al., Nat. Comput. Sci. 4, 451-460, 2024], we have shown that a …

Understanding the Mechanism of Nanocluster Formation from Machine-Learned Potential-based Simulations

V Tiwari, T Karmakar - 2025 - chemrxiv.org
Understanding the mechanism for the formation of metal nanoclusters is an open challenge
in the nanoscience field. Computational modeling can provide molecular details of …

[PDF][PDF] Exploring the Importance of Dynamics in Materials from the Atomic to the Supramolecular Scale Using Advanced Computational Methods

M Cioni - 2024 - tesidottorato.depositolegale.it
In the field of materials science, metals constitute a fundamental class of material that
possess unique properties and that have been crucial in technological and industrial …