Dr. Jason Hattrick-Simpers (17 March 2021)

Topic: Building Trust in Materials AI Predictions

Speaker: Dr. Jason Hattrick-Simpers from National Institute of Standard and Technology in the USA

It is our immense pleasure and delight to host Dr. Jason Hattrick-Simpers from NIST this week to present on—I quote, "We want to build Machine Learning models from some combination of our data and literature data, I will talk about how we can evaluate their explainability and uncertainties in the face of labeling uncertainties." Jae has pioneered the application of machine learning and high-throughput experiments in the past few years and we’re glad he’s able to join us this week!

Biography

Jason Hattrick-Simpers is a Materials Research Engineer at the National Institute of Standard and Technology in the USA. He works at the confluence of high-throughput experimental methodologies, experimental automation, and machine learning.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 17 Mar! https://mit.zoom.us/j/96231985116

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

Dr. Zhenpeng Yao (3 March 2021)

Topic: Deep Generative Models Automated Crystalline Materials Inverse Design

Speaker: Dr. Zhenpeng Yao from Harvard and University of Toronto

We’re very pleased to host Dr. Zhenpeng Yao from Harvard and University of Toronto this week to present his work on "Deep Generative Models Automated Crystalline Materials Inverse Design".

Abstract

Many aspects of renewable energy, from its generation (e.g., high-efficiency photovoltaic cells) and storage (e.g., large-capacity battery electrodes) to its utilization (e.g., lightweight alloys), are profoundly associated with materials discovery. Energy materials, like battery electrodes, generally function in a complex chemical environment involving various processes. The design of better energy materials, therefore, requires a comprehensive understanding of the material behaviors upon operation. Traditionally, the discovery of new materials with targeted properties involves a large number of experimental trials, while these efforts are far from adequate considering the well-recognized near-infinite chemical space. The efficient investigation of the unexplored chemical space calls for automated techniques with smart navigation. In this talk, I will first give an overview of how simulations, cheminformatics, deep generative models, and robotics have been accelerating the materials design process. Followingly, I will demonstrate the realization of the generative design of reticular frameworks (e.g., MOFs and COFs) using a supramolecular variational autoencoder empowered automated nanoporous materials discovery platform.

Biography

Dr. Zhenpeng Yao is a postdoctoral research associate of Chemistry and Computer Science at Harvard and the University of Toronto. Zhenpeng received his B.Sc. and M.Sc. in Mechanical Engineering from the Huazhong University of Science and Technology in 2008 and Shanghai Jiao Tong University in 2012. Zhenpeng obtained his Ph.D. in Materials Science and Engineering from Northwestern University in 2018. Then Zhenpeng joined Harvard University as a postdoc fellow till now. Zhenpeng’s research interests cover advanced battery electrodes and electrolytes, solid-state conductors, 2D materials, reticular chemistry, cheminformatics, deep generative model-based materials inverse design.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 3 Mar! https://mit.zoom.us/j/96231985116

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

Anas & Pawan correlate charge and TE transport to paracrystallinity in conducting polymers

Jan 2020: Nature Communications https://www.nature.com/articles/s41467-020-15399-2

The conceptual understanding of charge transport in conducting polymers is still ambiguous due to a wide range of paracrystallinity (disorder). Here, we advance this understanding by presenting the relationship between transport, electronic density of states and scattering parameter in conducting polymers. We show that the tail of the density of states possesses a Gaussian form confirmed by two-dimensional tight-binding model supported by Density Functional Theory and Molecular Dynamics simulations. Furthermore, by using the Boltzmann Transport Equation, we find that transport can be understood by the scattering parameter and the effective density of states. Our model aligns well with the experimental transport properties of a variety of conducting polymers; the scattering parameter affects electrical conductivity, carrier mobility, and Seebeck coefficient, while the effective density of states only affects the electrical conductivity. We hope our results advance the fundamental understanding of charge transport in conducting polymers to further enhance their performance in electronic applications.