Artificial intelligence: the silver bullet for sustainable materials development
KAUST DepartmentAdvanced Membranes and Porous Materials Research Center
Chemical Engineering Program
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
Physical Science and Engineering (PSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/665538
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AbstractMaterials 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.
CitationHardian, R., Liang, Z., Zhang, X., & Szekely, G. (2020). Artificial intelligence: the silver bullet for sustainable materials development. Green Chemistry. doi:10.1039/d0gc02956d
SponsorsFig. 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.
PublisherRoyal Society of Chemistry (RSC)
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.