Machine learning accelerates the materials discovery

J Fang, M Xie, X He, J Zhang, J Hu, Y Chen… - Materials Today …, 2022 - Elsevier
As the big data generated by the development of modern experiments and computing
technology becomes more and more accessible, the material design method based on …

Recent trends in computational tools and data-driven modeling for advanced materials

V Singh, S Patra, NA Murugan, DC Toncu… - Materials …, 2022 - pubs.rsc.org
The paradigm of advanced materials has grown exponentially over the last decade, with
their new dimensions including digital design, dynamics, and functions. Materials modeling …

Chatgpt research group for optimizing the crystallinity of mofs and cofs

Z Zheng, O Zhang, HL Nguyen, N Rampal… - ACS Central …, 2023 - ACS Publications
We leveraged the power of ChatGPT and Bayesian optimization in the development of a
multi-AI-driven system, backed by seven large language model-based assistants and …

Applying machine learning to design delicate amorphous micro-nano materials for rechargeable batteries

T Zheng, Z Huang, H Ge, P Hu, X Fan, B Jia - Energy Storage Materials, 2024 - Elsevier
As modern society evolves, the global importance of energy requirements has grown
significantly. Thus, exploring new materials for renewable energy storage is urgently …

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

K Noordhoek, C Bartel - Nanoscale, 2024 - pubs.rsc.org
The surface properties of solid-state materials often dictate their functionality, especially for
applications where nanoscale effects become important. The relevant surface (s) and their …

Recent progress on surface chemistry II: property and characterization

X Li, Z Xu, D Bu, J Cai, H Chen, Q Chen, T Chen… - Chinese Chemical …, 2025 - Elsevier
Surface with well-defined components and structures possesses unique electronic,
magnetic, optical and chemical properties. As a result, surface chemistry research plays a …

Performance evaluation of ZnSnN2 solar cells with Si back surface field using SCAPS-1D: A theoretical study

A Laidouci, VN Singh, PK Dakua, DK Panda - Heliyon, 2023 - cell.com
The earth-abundant semiconductor zinc tin nitride (ZnSnN 2) has garnered significant
attention as a prospective material in photovoltaic and lighting applications, primarily due to …

Predicting Molecular Self-Assembly on Metal Surfaces Using Graph Neural Networks Based on Experimental Data Sets

F Zheng, J Lu, Z Zhu, H Jiang, Y Yan, Y He, S Yuan… - ACS …, 2023 - ACS Publications
The application of supramolecular chemistry on solid surfaces has received extensive
attention in the past few decades. To date, combining experiments with quantum mechanical …

[HTML][HTML] Elucidating precipitation in FeCrAl alloys through explainable AI: A case study

SK Ravi, I Roy, S Roychowdhury, B Feng… - Computational Materials …, 2023 - Elsevier
A primary challenge of using FeCrAl in high temperature industrial settings is the formation
of α′-precipitates that causes brittleness in the alloy, resulting in failure through fracture …

Comparison of Machine Learning Approaches for Prediction of the Equivalent Alkane Carbon Number for Microemulsions Based on Molecular Properties

NR Furth, AE Imel, TA Zawodzinski - The Journal of Physical …, 2024 - ACS Publications
The chemical properties of oils are vital in the design of microemulsion systems. The
hydrophilic–lipophilic difference equation used to predict microemulsions' phase behavior …