Machine learning for electrocatalyst and photocatalyst design and discovery

H Mai, TC Le, D Chen, DA Winkler… - Chemical …, 2022‏ - ACS Publications
Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels,
reducing the impact of global warming, and providing solutions to environmental pollution …

Electrocatalysis in alkaline media and alkaline membrane-based energy technologies

Y Yang, CR Peltier, R Zeng, R Schimmenti, Q Li… - Chemical …, 2022‏ - ACS Publications
Hydrogen energy-based electrochemical energy conversion technologies offer the promise
of enabling a transition of the global energy landscape from fossil fuels to renewable energy …

Biomolecular dynamics with machine-learned quantum-mechanical force fields trained on diverse chemical fragments

OT Unke, M Stöhr, S Ganscha, T Unterthiner… - Science …, 2024‏ - science.org
Molecular dynamics (MD) simulations allow insights into complex processes, but accurate
MD simulations require costly quantum-mechanical calculations. For larger systems, efficient …

Uni-mol: A universal 3d molecular representation learning framework

G Zhou, Z Gao, Q Ding, H Zheng, H Xu, Z Wei, L Zhang… - 2023‏ - chemrxiv.org
Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

Recent advances and applications of deep learning methods in materials science

K Choudhary, B DeCost, C Chen, A Jain… - npj Computational …, 2022‏ - nature.com
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …

[HTML][HTML] GPAW: An open Python package for electronic structure calculations

JJ Mortensen, AH Larsen, M Kuisma… - The Journal of …, 2024‏ - pubs.aip.org
We review the GPAW open-source Python package for electronic structure calculations.
GPAW is based on the projector-augmented wave method and can solve the self-consistent …

Prospective de novo drug design with deep interactome learning

K Atz, L Cotos, C Isert, M Håkansson, D Focht… - Nature …, 2024‏ - nature.com
De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …

Combustion machine learning: Principles, progress and prospects

M Ihme, WT Chung, AA Mishra - Progress in Energy and Combustion …, 2022‏ - Elsevier
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

Gaussian process regression for materials and molecules

VL Deringer, AP Bartók, N Bernstein… - Chemical …, 2021‏ - ACS Publications
We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Machine learning for alloys

GLW Hart, T Mueller, C Toher, S Curtarolo - Nature Reviews Materials, 2021‏ - nature.com
Alloy modelling has a history of machine-learning-like approaches, preceding the tide of
data-science-inspired work. The dawn of computational databases has made the integration …