Dr. John Gregoire (27 October 2021)

Topic: Deep Reasoning Networks in the landscape of automated materials discovery.

Speaker: Dr. John Gregoire from High Throughput Experimentation Group at Caltech

We’re super delighted to announce our next speaker, Dr. John Gregoire from High Throughput Experimentation Group at Caltech, to bring us:

“Deep Reasoning Networks in the landscape of automated materials discovery”

Abstract

Considering the confluence of hardware and software required for automating materials discovery, the aspects of research workflows that are particularly difficult to automate include the analysis of data in the context of deep prior knowledge. When the prior knowledge is data centric, supervised learning can be effectively utilized. In science, prior knowledge is more often rules or principles-centric, where reasoning about the data is more critical than pattern recognition. To realize the concept of model training under supervision by reasoning, the Gomes group (Cornell) developed Deep Reasoning Networks in collaboration with the Gregoire group (Caltech) to solve crystal structure phase mapping, i.e., the inference of phase diagrams from large quantities of x-ray diffraction data. The phase mapping problem provides an excellent platform for comparing complementary approaches to automated data interpretation, highlighting the benefits of artificial intelligence methods that integrate reasoning and learning.

Biography

Dr. John Gregoire is a Research Professor of Applied Physics and Materials Science and leads the High Throughput Experimentation group at Caltech. He is also the Team Lead for Photoactive Materials in the Liquid Sunlight Alliance (LiSA), a U.S. DOE Energy Innovation Hub. His research team explores, discovers, and understands energy-related materials via combinatorial and high throughput experimental methods and their integration with materials theory and artificial intelligence. The group seeks to accelerate scientific discovery by automating critical components of research workflows, from synthesis and screening to data interpretation and hypothesis generation. He received his BA in Math and Physics from Concordia College and PhD in Physics from Cornell University. For more information, please see https://gregoire.people.caltech.edu.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 27 October! https://mit.zoom.us/j/96231985116

Warmest regards, @Thway @kedar @Tonio

Prof. Seungbum Hong (13 October 2021)

Topic: Materials and Molecular Modeling, Imaging, Informatics and Integration.

Speaker: Prof. Seungbum Hong from Department of Materials Science and Engineering, KAIST, Korea

We’re super delighted to announce our next speaker, Prof. Seungbum Hong from Department of Materials Science and Engineering, KAIST, Korea, to bring us:

“Materials and Molecular Modeling, Imaging, Informatics and Integration”

Abstract

M3I3 is an algorithm to perform a reverse engineering of future materials. Fast followers usually copy the first movers’ products by reverse engineering them. For example, in case of the state-of-the-art battery products, the competitors dissect them into pieces and analyze the structure and composition of each part such as cathode, anode, electrolyte and separator. This so called “reverse engineering” is the cheapest way to catch up with the forefront runners in the ever-expanding competing world. For the front-runners, they also need a way to defend themselves and aggressively keep the distance from their competitors, and that’s why they invest a huge amount of resources into research and development of new materials, devices, systems and platforms, and file patents all over the world. M3I3 provides a means to achieve this goal effectively by mimicking “reverse engineering” strategy with a higher level of creativity. M3I3 reverse engineers future materials of interest with superior performance and reliability as well as with minimum cost and environmental impact.

How is this possible? Reverse engineering starts from analyzing the structure and composition of the cutting-edge materials or products. Once we determine the performance of our targeted future materials, we need to know the candidate structure and composition of the future materials. This knowledge can only be available if we know the structure-property or the property-structure relationship of all materials and molecules at all scales. High-quality multi-scale and multi-dimensional experimental data will the key to the success of our approach. But there are critical challenges such as collecting and analyzing those data with consistency. We hope to address those challenges and form a clearer idea for our future direction.

Biography

Prof. Seungbum Hong is currently a professor in the Department of Materials Science and Engineering at KAIST, Republic of Korea. He has received many honours and awards including Frontier Award, Samsung Advanced Institute of Technology (2007), Young Investigator Outstanding Achievement Award, International Symposium on Integrated Ferroelectrics (2008), Frontier Scientist (Physics), The Korean Academy of Science and Technology (KAST) (2014), Grand Prize for Learning & Teaching Innovation Awards (이수영 교수학습혁신대상), KAIST (2020), and KCA Excellent Expert Awards (KCA 우수전문인 어워즈), Korea Customer Appraisal (KCA), (2021). He has attended more than 190 conferences, published 160 papers and 5 books. The number of patent with his name currently stands at 79. His main research area of accomplishments includes energy harvesting, nanostructures, microscopy and much more.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 13 October! https://mit.zoom.us/j/96231985116

Warmest regards, @Thway @kedar @Tonio

Assoc. Prof. Xiaonan Wang (29 September 2021)

Topic: Smart systems engineering contributing to the whole life cycle of material discovery and a net-zero future.

Speaker: Assoc. Prof. Xiaonan Wang (Department of Chemical Engineering, Tsinghua University, Beijing).

We're super delighted to announce our next speaker, Assoc. Prof. Xiaonan Wang from Department of Chemical Engineering, Tsinghua University, Beijing, to bring us:

“Smart systems engineering contributing to the whole life cycle of material discovery and a net-zero future”

Abstract

Facing the pressing environmental and climate change challenges, novel approaches are needed for sustainable development towards a carbon-neutral future. The emergence of big data analytics, internet of things, machine learning (ML), and general artificial intelligence (AI) provide enormous smart tools for processing complex data and information generated from experimental and computational research, as well as industrial applications, which could revolutionize next-generation research, industry and society. The potential contribution of ML combined with big data to energy and environmental is worth of investigation. In this talk, an overview of multi-scale smart systems engineering approaches and their applications in crucial domains of energy and environment management will be first given. Our recent developments of ML models and data-driven optimization will be demonstrated via a series of use cases, e.g. machine vision and AI automated molecular imaging, active learning guided sensor development and applied ML for prediction of CO2 adsorption and separation. The design, operation and management of multi-scale systems with enhanced economic and environmental performance are then presented. Finally, opportunities, challenges, and future directions of smart energy and environment management faced by the pressing sustainable development and carbon-neutrality targets are discussed.

Bio

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 became an adjunct associate professor. Since September 2021. Her research focuses on the development of intelligent computational methods including multi-scale 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 of more than 20 team members as PI and also a program leader lead of the Association of Pacific Rim Universities (APRU)’s Sustainable Waste Management Program. She has published more than 100 peer-reviewed papers, organized and chaired several international conferences, and delivered more than 50 presentations and invited talks at conferences and universities on five continents. She is an editorial board member of 10 SCI journals e.g. Applied Energy, ACS ES&T Engineering. She was recognized as an AIChE-SLS Outstanding Young Principal Investigator, Young Researcher Award for Engineering Sustainable Development, IChemE Global Awards Young Researcher finalist and selected for Royal Society International Exchanges Award, as well several best paper awards at IEEE and Applied Energy conferences and journals. She has been involved in the Accelerated Materials Development for Manufacturing programme in Singapore since 2018 and contributed the machine learning and data analytics expertise, while delivering a series of education workshops.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 29 September 2021!

https://mit.zoom.us/j/96231985116

Warmest regards,

@Thway @Kedar @Tonio