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 …

Data‐driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology

X Geng, F Wang, HH Wu, S Wang, G Wu… - Materials Genome …, 2023 - Wiley Online Library
With the development of new information technology, big data technology and artificial
intelligence (AI) have accelerated material research and development and industrial …

Atomistic simulation of nanoindentation behavior of amorphous/crystalline dual-phase high entropy alloys

RC Han, HY Song, S Li, T Guo - Journal of Materials Science & Technology, 2024 - Elsevier
High-entropy alloys (HEAs) are a new type of multi-principal metal materials that exhibit
excellent mechanical properties. However, the strength–ductility balance in the HEAs …

Modeling of solid solution strengthening in fcc alloys: Atomistic simulations, statistical models and elastic continuous approaches

PA Geslin - Computational Materials Science, 2024 - Elsevier
Solid solution strengthening is a technologically important mechanism controlling the
strength of a wide range of alloys. Understanding and predicting the temperature-dependent …

Exploring interpretable features of hardness for intermetallic compounds prepared by spark plasma sintering

X Li, D Zhu, K Pan, HH Wu, Y Ren, C Hu… - International Journal of …, 2023 - Elsevier
Intermetallic compounds, known for their excellent hardness, conductivity, and strength,
have significant applications in aerospace and automotive industries. Hardness is a crucial …

[HTML][HTML] The effect of induced precipitations and pre-twins on the bending neutral layer migration behaviors of AZ80 Mg alloy sheet

Z Zhang, L Wang, HH Wu, X Pan, B Gao… - Journal of Materials …, 2023 - Elsevier
To explore the effect of the precipitated phase particles and twins on the microstructure
evolution and neutral layer evolution during bending,{10–12} tensile twins were introduced …

Predictive Modeling of Tensile Strength in Aluminum Alloys via Machine Learning

K Fu, D Zhu, Y Zhang, C Zhang, X Wang, C Wang… - Materials, 2023 - mdpi.com
Aluminum alloys are widely used due to their exceptional properties, but the systematic
relationship between their grain size and their tensile strength has not been thoroughly …

[PDF][PDF] Anisotropic thermal transport in chalcogenide perovskite CaZrS3 from machine learning interatomic potential

Y Wang, J Tang, G Li, J Zheng, X Song, Q Wang, Z Cui… - Eng Sci, 2023 - researchgate.net
Chalcogenide perovskites are being actively considered for photovoltaic, optoelectronic,
and thermoelectric applications due to their high carrier mobility, strong light absorption …

[HTML][HTML] A brief review of machine learning-assisted Mg alloy design, processing, and property predictions

Y Cheng, L Wang, C Yang, Y Bai, H Wang… - Journal of Materials …, 2024 - Elsevier
Owing to the hexagonal close-packed (HCP) crystal structure inherent in Mg alloys, strong
basal texture can readily be induced through the processes of rolling or extrusion. The …

Biomimetic Fusion: Platyper's Dual Vision for Prediction Protein-Surface Interactions

C Hong, X Wu, J Huang, H Dai - Materials Horizons, 2024 - pubs.rsc.org
Predicting protein's binding with material surface still remains a challenge. Here, a novel
approach, Platypus Dual Perception Neural Network (Platyper) was developed to describe …