TY - JOUR
T1 - MPpredictor
T2 - An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction
AU - Gupta, Vishu
AU - Choudhary, Kamal
AU - Mao, Yuwei
AU - Wang, Kewei
AU - Tavazza, Francesca
AU - Campbell, Carelyn
AU - Liao, Wei Keng
AU - Choudhary, Alok
AU - Agrawal, Ankit
N1 - Funding Information:
This work was performed under the following financial assistance: Award 70NANB19H005 from the U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). Partial support is also acknowledged from NSF Award CMMI-2053929 and DOE Awards DE-SC0019358 and DE-SC0021399, and Northwestern Center for Nanocombinatorics.
Publisher Copyright:
© 2023 The Authors. Published by American Chemical Society.
PY - 2023/4/10
Y1 - 2023/4/10
N2 - The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available data and accelerate materials discovery and design for future applications. In an attempt to assist with this process, we deploy predictive models for multiple material properties, given the composition of the material. The deep learning models described here are built using a cross-property deep transfer learning technique, which leverages source models trained on large data sets to build target models on small data sets with different properties. We deploy these models in an online software tool that takes a number of material compositions as input, performs preprocessing to generate composition-based attributes for each material, and feeds them into the predictive models to obtain up to 41 different material property values. The material property predictor is available online at http://ai.eecs.northwestern.edu/MPpredictor.
AB - The applications of artificial intelligence, machine learning, and deep learning techniques in the field of materials science are becoming increasingly common due to their promising abilities to extract and utilize data-driven information from available data and accelerate materials discovery and design for future applications. In an attempt to assist with this process, we deploy predictive models for multiple material properties, given the composition of the material. The deep learning models described here are built using a cross-property deep transfer learning technique, which leverages source models trained on large data sets to build target models on small data sets with different properties. We deploy these models in an online software tool that takes a number of material compositions as input, performs preprocessing to generate composition-based attributes for each material, and feeds them into the predictive models to obtain up to 41 different material property values. The material property predictor is available online at http://ai.eecs.northwestern.edu/MPpredictor.
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U2 - 10.1021/acs.jcim.3c00307
DO - 10.1021/acs.jcim.3c00307
M3 - Article
C2 - 36972592
AN - SCOPUS:85151318035
SN - 1549-9596
VL - 63
SP - 1865
EP - 1871
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 7
ER -