Advances in machine learning for high value-added applications of lignocellulosic biomass

H Ge, J Zheng, H Xu - Bioresource Technology, 2023 - Elsevier
Lignocellulose can be converted into biofuel or functional materials to achieve high value-
added utilization. Biomass utilization process is complex and multi-dimensional. This paper …

Application of machine learning in material synthesis and property prediction

G Huang, Y Guo, Y Chen, Z Nie - Materials, 2023 - mdpi.com
Material innovation plays a very important role in technological progress and industrial
development. Traditional experimental exploration and numerical simulation often require …

ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT

J Deb, L Saikia, KD Dihingia… - Journal of Chemical …, 2024 - ACS Publications
The pursuit of designing smart and functional materials is of paramount importance across
various domains, such as material science, engineering, chemical technology, electronics …

Screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network

J Ojih, A Rodriguez, J Hu, M Hu - Energy and AI, 2023 - Elsevier
Mechanical and thermal properties of materials are extremely important for various
engineering and scientific fields such as energy conversion and energy storage. However …

Graph theory and graph neural network assisted high-throughput crystal structure prediction and screening for energy conversion and storage

J Ojih, M Al-Fahdi, Y Yao, J Hu, M Hu - Journal of Materials Chemistry …, 2024 - pubs.rsc.org
Prediction of crystal structures with desirable material properties is a grand challenge in
materials research, due to the enormous search space of possible combinations of elements …

High-throughput computational discovery of 3218 ultralow thermal conductivity and dynamically stable materials by dual machine learning models

J Ojih, C Shen, A Rodriguez, H Zhang… - Journal of Materials …, 2023 - pubs.rsc.org
Materials with ultralow lattice thermal conductivity (LTC) continue to be of great interest for
technologically important applications such as thermal insulators and thermoelectrics. We …

Machine learning aided High-throughput first-principles calculations to predict the formation enthalpy of σ phase

Y Su, J Wang - Calphad, 2023 - Elsevier
The σ phase is a topologically close-packed phase and can significantly influence the
performance and properties of materials. Accurate prediction the formation enthalpy of the σ …

Machine-learning-assisted discovery of boron-doped graphene with high work function as an anode material for Li/Na/K-ion batteries

Y Luo, H Chen, J Wang, X Niu - Physical Chemistry Chemical Physics, 2023 - pubs.rsc.org
Work function (WF) modulation is a crucial descriptor for carbon-based electrodes in
optoelectronic, catalytic, and energy storage applications. Boron-doped graphene is …

Revolutionizing solar photovoltaic efficiency: A comprehensive review of cutting-edge thermal management methods for advanced and conventional solar …

SY Khan, S Rauf, S Liu, W Chen, Y Shen… - Energy & Environmental …, 2025 - pubs.rsc.org
Studies have been conducted to explore innovative performance-enhancing thermal
management strategies (PETS) aimed at improving the efficiency of Photovoltaic (PV) …

[HTML][HTML] An integrated machine learning and metaheuristic approach for advanced packed bed latent heat storage system design and optimization

A Anagnostopoulos, T Xenitopoulos, Y Ding, P Seferlis - Energy, 2024 - Elsevier
To tackle the challenge of waste heat recovery in the industrial sector, this research presents
a novel design and optimization framework for Packed Bed Latent Heat Storage Systems …