Artificial intelligence: the silver bullet for sustainable materials development. Issue 21 (15th October 2020)

• Record Type: Journal Article
• Authors: Hardian, Rifan; Liang, Zhenwen; Zhang, Xiangliang; Szekely, Gyorgy
• Is Part Of: Green chemistry. Volume 22:Issue 21(2020)
• Page Start: 7521
• Page End: 7528
• Publication Date: 2020-10-15
• Subjects: Environmental chemistry -- Industrial applications -- Periodicals; Environmental management -- Periodicals; 660
• DOI: 10.1039/d0gc02956d
• Languages: English
• ISSNs: 1463-9262

Abstract

Abstract : Artificial intelligence in combination with design of experiments for sustainable process development achieving high quality metal–organic frameworks (MOFs) with minimal environmental effect. 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.