Smart systems engineering contributing to materials discovery for carbon-neutral future

Xiaonan Wang*

Department of Chemical Engineering, Tsinghua University, Beijing, China

EXTENDED ABSTRACT: In light of the pressing environmental and climate change challenges, smart approaches are indispensable for sustainable development towards the target of net-zero emissions in future. Advances in artificial intelligence (AI), especially machine learning (ML), provide an enormous variety of smart tools for processing complex data and information generated from experimental and computational research, as well as industrial applications. AI and ML have substantially impacted the research and development norm of new materials and processes for energy and environment. Different data-driven strategies can be utilized at each stage of material characterization, property prediction, synthesis, and theory paradigm discovery to accelerate the whole development process. This talk will first provide an overview and perspectives on ML applications to materials/chemicals discovery and synthesis. Furthermore, active learning strategies to incorporate ML in the high-throughput computational and/or experimental loop are introduced, as an effective approach to accelerate the discovery of new materials with desired functionality. Ultimately, these smart technologies could enable autonomous materials discovery, process optimization, and all aspects of the next-generation intelligent laboratory and industry, thereby facilitating improvement in energy efficiency, emission reduction, and eventually the realization of carbon neutrality target. The challenges to be addressed and prospects of AI applications in material and process discovery are also highlighted.

Keywords: Machine learning; AI; Data-driven; Carbon neutrality; Materials


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Brief Introduction of Speaker
Xiaonan Wang

Dr Xiaonan Wang is currently an associate professor in the Department of Chemical Engineering at Tsinghua University. She received her BEng from Tsinghua University in 2011 and PhD from University of California, Davis in 2015. After working as a postdoctoral research associate at Imperial College London, she joined the National University of Singapore (NUS) as an assistant professor since 2017 and later became an adjunct associate professor. Her research focuses on the development of intelligent computational methods including multi­scale modelling, optimization, data analytics and machine learning for applications in advanced materials, energy, environmental and manufacturing systems to support smart and sustainable development. She is leading a Smart Systems Engineering research group at NUS and Tsinghua and also the deputy director of the Accelerated Materials Development programme in Singapore. She has published more than 120 peer-reviewed papers and serves as an associate editor or editorial board member of 10 SCI journals e.g., Applied Energy, Advanced Intelligent Systems. She was recognized as a World's Top 2% Scientists, AIChE-SLS Outstanding Young Principal Investigator and selected for Royal Society International Exchanges Award, as well several best paper awards at IEEE and Applied Energy conferences and journals.