Prof. Tonio Buonassisi and Asst. Prof. Kedar Hippalgaonkar (23 June 2021)

Topic: Papers our teams have submitted to ArXiv lately.

Speakers: Prof. Tonio Buonassisi from Massachusetts Institute of Technology (MIT), and Asst. Prof. Kedar Hippalgaonkar, a joint appointee with the Materials Science and Engineering Department at Nanyang Technological University (NTU) and as a Senior Scientist at the Institute of Materials Research and Engineering (IMRE) at the Agency for Science Technology and Research (A*STAR)

Due to a scheduling conflict, we have an opportunity to present what our research groups are up to! We bring to you:

“Papers our teams have submitted to ArXiv lately”

Abstract

Your seminar hosts will review some of our latest work posted to arXiv / ChemRXiv, with personal touch describing key innovations, challenges, and future work. Come join this interactive session and learn about the latest research in this field!

Biography

Tonio Buonassisi is a Professor of Mechanical Engineering at the Massachusetts Institute of Technology (MIT). He is pioneering the application of artificial intelligence to develop new materials for societally beneficial applications. His research in solar photovoltaics and technoeconomic analysis assisted technology developments in dozens of companies, earning him a US Presidential Early Career Award for Scientists and Engineers (PECASE), a National Science Foundation CAREER Award, and a Google Faculty Award. He founded the MIT PVLab and co-founded the Fraunhofer Center for Sustainable Energy Systems in Boston USA. A recipient of the prestigious MIT Everett Moore Baker Memorial Award for Excellence in Undergraduate Teaching, his passion for education is evidenced by the >73k views of his OpenCourseware/YouTube PV lectures series, and a recent YouTube video series focused on the application of AI to materials research, entitled “Accelerated Materials Development for Manufacturing".

Dr. Kedar Hippalgaonkar is a joint appointee with the Materials Science and Engineering Department at Nanyang Technological University (NTU) and as a Senior Scientist at the Institute of Materials Research and Engineering (IMRE) at the Agency for Science Technology and Research (A*STAR). He is leading the Accelerated Materials Development for Manufacturing (AMDM) program from 2018-2023 focusing on the development of new materials, processes and optimization using Machine Learning, AI and high-throughput computations and experiments in electronic and plasmonic materials and polymers. He is also leading the Pharos Program on Hybrid (inorganic-organic) thermoelectrics for ambient applications from 2016-2020.

He has published over 50 research papers, and was nominated as a Journal of Materials Chemistry Emerging Investigator in 2019. He was recognized as a Science and Technology for Society Young Leader in Kyoto in 2015. For his outstanding graduate research, he was awarded the Materials Research Society Silver Medal in 2014. He graduated with a Bachelor of Science (Distinction) from the Department of Mechanical Engineering at Purdue University in 2003 and obtained his Doctor of Philosophy from the Department of Mechanical Engineering at UC Berkeley in 2014. While pursuing his doctoral studies, he conducted research on fundamentals of heat, charge and light in solid state materials.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9.30am, Wed, 23 June! https://mit.zoom.us/j/96231985116

(Note: The time is rescheduled to 9:30am—10:30am for this session because of a scheduling conflict, just this week.)

Warmest regards, @Siyu Tian (Isaac) @kedar @Tonio

Dr. Jakoah Brgoch (9 June 2021)

Topic: Finding Thermally Robust Superhard Materials with Machine Learning

Speaker: Dr. Jakoah Brgoch from University of Houston

We’re super thrilled to host our next speaker, Dr. Jakoah Brgoch from University of Houston, to bring us:

“Finding Thermally Robust Superhard Materials with Machine Learning”

Jakoah is pioneering the use of machine learning to address the problem of inorganic materials synthesizability, with the solid-state chemistry perspectives in looking at inverse design.

Abstract

Superhard materials with a Vickers hardness >40 GPa are essential in applications ranging from manufacturing to energy production. Finding new superhard materials has traditionally been guided by empirical design rules derived from classically known materials. However, the ability to quantitatively predict hardness remains a significant barrier in materials design. To address this challenge, we constructed an ensemble machine-learning model capable of directly predicting load-dependent hardness. The predictive power of our model was validated on eight unmeasured metal disilicides and a hold-out set of superhard materials. The trained model was then used to screen compounds in Pearson’s Crystal Data (PCD) set and combined with our recently developed machine-learning phase diagram tool to suggest previously unreported superhard compounds. Finally, industrial materials often experience tremendous heat during application; thus, we are building a method for predicting hardness at elevated temperatures.

Biography

Prof. Jakoah Brgoch is an Associate Professor in the Department of Chemistry and a Principal Investigator in the Texas Center of Superconductivity. Jakoah also has a courtesy appointment in the William A. Brookshire Department of Chemical and Biomolecular Engineering and he is a member of the Hewlett-Packard Enterprise Data Science Institute. Jakoah completed his bachelors and masters in Chemistry from Illinois State University followed by his Ph.D. from Iowa State University and Ames National Laboratory under the supervision of Gordon Miller followed by postdoctoral research at the University of California, Santa Barbara in the Materials Research Laboratory with Ram Seshadri. Jakoah is now leading a multidisciplinary research group with research topics ranging from the development of persistent luminescent materials for bio-imaging to understanding the mechanical response in superhard materials all through a combination of materials synthesis, characterization, first-principles computation, and machine learning. He has published more than 85 peer-reviewed papers, earned a 2019 NSF CAREER research award, and is a 2020 Alfred P. Sloan Research Fellow in Chemistry.

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

Warmest regards, @Siyu Tian (Isaac) @kedar @Tonio

Dr. Taylor Sparks (12 May 2021)

Topic: Accuracy, Uncertainty, Inspectability: The benefits of compositionally-restricted attention-based networks.

Speaker: Dr. Taylor Sparks from the University of Utah

We’re super delighted to announce our next speaker, Dr. Taylor Sparks from University of Utah, to bring us:

“Accuracy, Uncertainty, Inspectability: The benefits of compositionally-restricted attention-based networks.”

Abstract

We describe a new model architecture, the Compositionally-Restricted Attention-Based Network (CrabNet). CrabNet generates high-fidelity predictions based on the self-attention mechanism, a fundamental component of the transformer architecture that revolutionized natural language processing. The transformer encoder uses self-attention to encode the context-dependent behavior for the components within a system. In physical environments, elements contribute differently to a material’s property based on the materials system itself. For example, boron behaving as an electrical dopant in one system while behaving as a mechanical strengthening bond modification in another. CrabNet’s ability to potentially capture this type of context-dependent behavior allows for highly accurate model predictions. Importantly, CrabNet generates simple and inspectable self-attention maps. These attention maps govern the learned material property by representing element importance and interactions. The visualization and analysis of these attention maps are available during training and inference periods.

Biography

Dr. Sparks is an Associate Professor and Associate Chair of the Materials Science and Engineering Department at the University of Utah. He is originally from Utah and an alumni of the department he now teaches in. Before graduate school he worked at Ceramatec Inc. He did his MS in Materials at UCSB and his PhD in Applied Physics at Harvard University in David Clarke’s laboratory and then did a postdoc with Ram Seshadri in the Materials Research Laboratory at UCSB. His current research centers on the discovery, synthesis, characterization, and properties of new materials for energy applications. He is a pioneer in the emerging field of materials informatics whereby big data, data mining, and machine learning are leveraged to solve challenges in materials science. He also hosts a podcast entitled “Materialism” where he discusses the past, present, and future of Materials Science.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 12 May! https://mit.zoom.us/j/96231985116

Warmest regards, @Siyu Tian (Isaac) @kedar @Tonio