Artificial intelligence: the silver bullet for sustainable materials development

Rifan Hardian, Zhenwen Liang, Xiangliang Zhang, Gyorgy Szekely

Research output: Contribution to journalArticlepeer-review

55 Scopus citations


Materials discovery is rapidly revolutionizing all aspects of our lives. However, the design and fabrication of materials are often unsustainable and resource-intensive. Hence, we need a paradigm shift towards designing sustainable materials in silico. Machine learning, a subfield of artificial intelligence (AI), is emerging within the sustainability agenda because it promises to benefit science and engineering through improved quality, performance, and predictive power. Here we present a new methodology to extend the application of AI to develop materials in an environmentally friendly way. We demonstrate successful materials development by combining design of experiments with a new machine learning module that comprises a support vector machine, an evolutionary algorithm, and a desirability function. We use our AI-based method to realize the sustainable electrochemical synthesis of ZIF-8 metal-organic framework and explore the hyperdimensional relationship between the synthesis parameters, product qualities, and process sustainability. The presented AI-based methodology paves the way for solving the challenge of the materials fabrication-sustainability nexus, and facilitates the paradigm shift from the wet lab to the wired lab.
Original languageEnglish (US)
JournalGreen Chemistry
StatePublished - Oct 9 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-13
Acknowledgements: Fig. 1 and the Table of Contents illustrations were created by Xavier Pita, scientific illustrator at King Abdullah University of Science and Technology (KAUST). The research reported in this publication was supported by funding from KAUST.


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