Dr. Prashun Gorai (4 August 2021)

Topic: Defects in Functional Materials: Doping, Discovery, and Dreams.

Speaker: Dr. Prashun Gorai from the Colorado School of Mines

This is a long awaited talk! We’re chuffed to announce our next speaker, who’s dreaming about defects, doping, DFT and machine learning Dr. Prashun Gorai from the Colorado School of Mines, to bring us:

“Defects in Functional Materials: Doping, Discovery, and Dreams”

Abstract

Defects play a critical role in influencing the functional properties of materials in applications such as thermoelectrics, optoelectronics, solid-state energy storage etc. In this talk, I will give a brief overview of first-principles modelling of point defect energetics and software packages for automation of such calculations. I will provide examples from our work on thermoelectrics, photovoltaics, and solid-state batteries, where we have utilized first-principles defect calculations to guide experimental doping efforts, enable discovery of new materials and develop new composition-structure-property relationships. I will also introduce our recent work on a doping by design approach to materials discovery, and extension of defect modelling to disordered phases. In the end, I will share my vision of how materials informatics can accelerate dopability predictions and therefore, materials discovery. In this context, I will discuss the need for a standardized data format for documenting defect calculations.

Biography

Dr. Prashun Gorai is a research assistant professor in the Department of Metallurgical and Materials Engineering at the Colorado School of Mines (CSM). He is also affiliated with the Materials Science Center at the National Renewable Energy Laboratory (NREL). He obtained his BTech degree from the Indian Institute of Technology Madras and his PhD from the University of Illinois at Urbana-Champaign. Subsequently, he was a postdoctoral fellow at CSM and NREL. Dr. Gorai’s research team uses computational tools, including first-principles methods and materials informatics, to discover and design functional materials for various energy-related applications.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 4 August! https://mit.zoom.us/j/96231985116

Warmest regards, @Thway @kedar @Tonio

Mr. Hitarth Choubisa (21 July 2021)

Topic: Interpretable discovery of new materials.

Speaker: Hitarth Choubisa from the University of Toronto

We’re excited to announce our next speaker, Hitarth Choubisa, from the University of Toronto. Hitarth is a PhD student in quantum chemistry at Ted Sargent's group in University of Toronto since 2018. This promises to be an exciting talk!

“Interpretable discovery of new materials”

Abstract

I will talk about how we can combine quantum chemistry, machine learning and genetic algorithms to perform inverse design in the chemical space. Examples would include search for thermodynamically stable materials that can emit light at desirable wavelengths. I will also discuss how we can use unsupervised learning instead of the closed-loop approach for finding rules-of-thumb. These rules can then be used by experimentalists directly to successfully synthesize materials with desirable properties.

Biography

Hitarth Choubisa is a PhD student in quantum chemistry at Ted Sargent's group in University of Toronto since 2018. He is supported by the ECE fellowship and Hatch Graduate Scholarship for sustainable energy research. Before starting his PhD, he completed his undergrad from Indian Institute of Technology, Bombay. His interests lie in accelerating materials discovery using computational tools such as machine learning.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 21 July! https://mit.zoom.us/j/96231985116

Warmest regards, @Thway @kedar @Tonio

Prof. Tengfei Luo and Dr. Zeyu Liu (7 July 2021)

Topic: Transfer Learning for Phonon and Thermal Property Predictions.

Speaker: Prof. Tengfei Luo and Dr. Zeyu Liu from the University of Notre Dame

We’re super delighted to announce our next speaker, Dr. Tengfei Luo and Dr. Zeyu Liu from the University of Notre Dame, to bring us:

“Transfer Learning for Phonon and Thermal Property Predictions”

Abstract

Machine learning is trending to be an integral part of thermal science. In this talk, I will introduce our efforts in utilizing machine learning (ML) techniques to predict phonon properties and thermal modeling. I will talk about the use of a new ML method, called transfer learning, to establish accurate models based on limited data for predicting phonon and thermal transport properties, such as phonon frequency gap, heat capacity, speed of sound and lattice thermal conductivity.

Biography

Dr. Tengfei Luo is a Professor in the Department of Aerospace and Mechanical Engineering (AME) at the University of Notre Dame (UND). Before joining UND, he was a postdoctoral associate at Massachusetts Institute of Technology (2009-2011) after obtaining his PhD from Michigan State University (2009). Dr. Luo’s research focuses on exploring the chemistry-conformation-property relationships of polymers and inorganic materials using molecular simulations, DFT calculation, machine learning and experiments. He is an ASME Fellow (2019), JSPS Invitational Fellow (2019), DuPont Young Professor Awardee (2016), DARPA Young Faculty Awardee (2015), and Air Force Summer Faculty Fellow (2015).

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 7 July! https://mit.zoom.us/j/96231985116

Warmest regards, @Thway @kedar @Tonio