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Rise of machine learning potentials in heterogeneous catalysis: Developments, applications, and prospects
The urgency of tackling climate change is driving a global shift towards renewable sources
of energy, with a growing contribution from alternative energy sources such as solar, wind …
of energy, with a growing contribution from alternative energy sources such as solar, wind …
[HTML][HTML] Integrating artificial intelligence in energy transition: A comprehensive review
The global energy transition, driven by the imperative to mitigate climate change, demands
innovative solutions to address the technical, economic, and social challenges of …
innovative solutions to address the technical, economic, and social challenges of …
Leveraging language representation for materials exploration and discovery
J Qu, YR **e, KM Ciesielski, CE Porter… - npj Computational …, 2024 - nature.com
Data-driven approaches to materials exploration and discovery are building momentum due
to emerging advances in machine learning. However, parsimonious representations of …
to emerging advances in machine learning. However, parsimonious representations of …
Towards atom-level understanding of metal oxide catalysts for the oxygen evolution reaction with machine learning
Green hydrogen production is crucial for a sustainable future, but current catalysts for the
oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce …
oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce …
Machine-learning-accelerated simulations to enable automatic surface reconstruction
Understanding material surfaces and interfaces is vital in applications such as catalysis or
electronics. By combining energies from electronic structure with statistical mechanics, ab …
electronics. By combining energies from electronic structure with statistical mechanics, ab …
Machine learning-accelerated discovery of heat-resistant polysulfates for electrostatic energy storage
The development of heat-resistant dielectric polymers that withstand intense electric fields at
high temperatures is critical for electrification. Balancing thermal stability and electrical …
high temperatures is critical for electrification. Balancing thermal stability and electrical …
Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials
K Noordhoek, C Bartel - Nanoscale, 2024 - pubs.rsc.org
The surface properties of solid-state materials often dictate their functionality, especially for
applications where nanoscale effects become important. The relevant surface (s) and their …
applications where nanoscale effects become important. The relevant surface (s) and their …
When Metal Nanoclusters Meet Smart Synthesis
Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall
nanoparticles with molecule-like properties, bridging conventional metal–ligand complexes …
nanoparticles with molecule-like properties, bridging conventional metal–ligand complexes …
Matsciml: A broad, multi-task benchmark for solid-state materials modeling
KLK Lee, C Gonzales, M Nassar, M Spellings… - arxiv preprint arxiv …, 2023 - arxiv.org
We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine
Learning (MatSci ML) methods focused on solid-state materials with periodic crystal …
Learning (MatSci ML) methods focused on solid-state materials with periodic crystal …
Geometric data analysis-based machine learning for two-dimensional perovskite design
CS Hu, R Mayengbam, MC Wu, K **a… - Communications …, 2024 - nature.com
With extraordinarily high efficiency, low cost, and excellent stability, 2D perovskite has
demonstrated a great potential to revolutionize photovoltaics technology. However …
demonstrated a great potential to revolutionize photovoltaics technology. However …