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Abstract
Discovering novel materials is crucial for various industries. Conventional methods are time-consuming and inefficient. This paper introduces MLMD, a programming-free AI platform for materials design. MLMD uses model inference, surrogate optimization, and active learning to discover materials with desired properties, even with limited data. It integrates data analysis, descriptor forecasting, hyperparameter optimization, and property prediction, offering a user-friendly web-based interface. MLMD has been successfully applied to various materials, demonstrating its potential to accelerate materials discovery.
Publisher
Nature Materials
Published On
Jul 26, 2024
Authors
Jiaxuan Ma, Bin Cao, Shuya Dong, Yuan Tian, Menghuan Wang, Jie Xiong, Sheng Sun
DOI
https://doi.org/10.1038/s41524-024-01243-4
Tags
materials design
AI platform
MLMD
machine learning
surrogate optimization
active learning
property prediction

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