Issue 21, 2020

Artificial intelligence: the silver bullet for sustainable materials development

Abstract

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

Supplementary files

Article information

Article type
Paper
Submitted
30 kol 2020
Accepted
09 lis 2020
First published
09 lis 2020
This article is Open Access
Creative Commons BY license

Green Chem., 2020,22, 7521-7528

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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