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Issue 21, 2020
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Artificial intelligence: the silver bullet for sustainable materials development

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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 a 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.

Graphical abstract: Artificial intelligence: the silver bullet for sustainable materials development

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Article information

30 Aug 2020
09 Oct 2020
First published
09 Oct 2020

This article is Open Access

Green Chem., 2020,22, 7521-7528
Article type

Artificial intelligence: the silver bullet for sustainable materials development

R. Hardian, Z. Liang, X. Zhang and G. Szekely, Green Chem., 2020, 22, 7521 DOI: 10.1039/D0GC02956D

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