Bridging the complexity gap in computational heterogeneous catalysis with machine learning

T Mou, HS Pillai, S Wang, M Wan, X Han… - Nature Catalysis, 2023 - nature.com
Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …

Representations of materials for machine learning

J Damewood, J Karaguesian, JR Lunger… - Annual Review of …, 2023 - annualreviews.org
High-throughput data generation methods and machine learning (ML) algorithms have
given rise to a new era of computational materials science by learning the relations between …

Machine learning-assisted low-dimensional electrocatalysts design for hydrogen evolution reaction

J Li, N Wu, J Zhang, HH Wu, K Pan, Y Wang, G Liu… - Nano-Micro Letters, 2023 - Springer
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …

Intelligent biomaterialomics: molecular design, manufacturing, and biomedical applications

Y Yi, HW An, H Wang - Advanced Materials, 2024 - Wiley Online Library
Materialomics integrates experiment, theory, and computation in a high‐throughput manner,
and has changed the paradigm for the research and development of new functional …

Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back

BA Koscher, RB Canty, MA McDonald, KP Greenman… - Science, 2023 - science.org
A closed-loop, autonomous molecular discovery platform driven by integrated machine
learning tools was developed to accelerate the design of molecules with desired properties …

Machine Learning Descriptors for Data‐Driven Catalysis Study

LH Mou, TT Han, PES Smith, E Sharman… - Advanced …, 2023 - Wiley Online Library
Traditional trial‐and‐error experiments and theoretical simulations have difficulty optimizing
catalytic processes and developing new, better‐performing catalysts. Machine learning (ML) …

A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

JC Thomas, W Chen, Y Xiong, BA Barker… - Nature …, 2024 - nature.com
Point defects in two-dimensional materials are of key interest for quantum information
science. However, the parameter space of possible defects is immense, making the …

Recent advances in multifunctional reticular framework nanoparticles: a paradigm shift in materials science road to a structured future

M Chafiq, A Chaouiki, YG Ko - Nano-Micro Letters, 2023 - Springer
Porous organic frameworks (POFs) have become a highly sought-after research domain that
offers a promising avenue for developing cutting-edge nanostructured materials, both in …

Closed‐Loop Multi‐Objective Optimization for Cu–Sb–S Photo‐Electrocatalytic Materials' Discovery

Y Bai, ZHJ Khoo, RI Made, H Xie, CYJ Lim… - Advanced …, 2024 - Wiley Online Library
Copper antimony sulfides are regarded as promising catalysts for photo‐electrochemical
water splitting because of their earth abundance and broad light absorption. The unique …

Design principles for transition metal nitride stability and ammonia generation in acid

J Peng, JJ Giner-Sanz, L Giordano, WP Mounfield… - Joule, 2023 - cell.com
Transition metal nitrides have shown promise as electrocatalysts in proton exchange
membrane fuel cells and electrolyzers, but the instability of these nitrides in acid has limited …