Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Heterogeneous catalysis underpins a wide variety of industrial processes including energy
conversion, chemical manufacturing and environmental remediation. Significant advances …
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 …
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
Efficient electrocatalysts are crucial for hydrogen generation from electrolyzing water.
Nevertheless, the conventional" trial and error" method for producing advanced …
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 …
and has changed the paradigm for the research and development of new functional …
Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back
A closed-loop, autonomous molecular discovery platform driven by integrated machine
learning tools was developed to accelerate the design of molecules with desired properties …
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) …
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
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 …
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
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 …
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
Copper antimony sulfides are regarded as promising catalysts for photo‐electrochemical
water splitting because of their earth abundance and broad light absorption. The unique …
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
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 …
membrane fuel cells and electrolyzers, but the instability of these nitrides in acid has limited …