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December 2021: Scientific Reports


Despite the surfeit in the amount of research interest towards organic semiconductors in the last few decades, the underlying charge transport picture still remains hazy with no consensus among the different models. Here, we employ a purely data-driven approach, namely symbolic regression, on temperature-dependent field-effect mobility data of different organic semiconductors to describe the transport and compare the same with a physics-inspired renormalization fitting approach where we used a scaled, dimensionless mobility with respect to a scale-invariant reference temperature. We find that the renormalization approach is powerful compared to purely data-driven symbolic regression, providing an intuitive understanding of data with extrapolative ability.




Topic: Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design.

Speaker: Mr. Wenhao Gao from Department of Chemical Engineering at Massachusetts Institute of Technology (MIT)




We’re super delighted to announce our next speaker, Mr. Wenhao Gao, from Department of Chemical Engineering at Massachusetts Institute of Technology (MIT), to bring us:


Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design


Abstract

Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation. We report an amortized approach to generate synthetic pathways as a Markov decision process conditioned on a target molecular embedding. This approach allows us to conduct synthesis planning in a bottom-up manner and design synthesizable molecules by decoding from optimized conditional codes, demonstrating the potential to solve both problems of design and synthesis simultaneously. The approach leverages neural networks to probabilistically model the synthetic trees, one reaction step at a time, according to reactivity rules encoded in a discrete action space of reaction templates. We train these networks on hundreds of thousands of artificial pathways generated from a pool of purchasable compounds and a list of expert-curated templates. We validate our method with (a) the recovery of molecules using conditional generation, (b) the identification of synthesizable structural analogs, and (c) the optimization of molecular structures given oracle functions relevant to drug discovery.


Biography

Wenhao Gao is a Ph.D. candidate in Chemical Engineering at MIT. Under advisory from Prof. Connor Coley, his research focuses on using artificial intelligence and robotic automation to accelerate chemical discovery processes. Before joining the Coley research group, he received his B.S. in Chemistry from Peking University and M.S. in Chemical and Biomolecular Engineering from Johns Hopkins University.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 22 December! https://mit.zoom.us/j/96231985116

Warmest regards, @Thway @kedar @Tonio


Topic: Machine Learning for Scanning Probe and Electron Microscopy for Materials Discovery.

Speaker: Dr. Sergei V. Kalinin from Center for Nanophase Materials Sciences at Oak Ridge National Laboratory



We’re super delighted to announce our next speaker, Dr. Sergei V. Kalinin, from Center for Nanophase Materials Sciences at Oak Ridge National Laboratory to bring us:


Machine Learning for Scanning Probe and Electron Microscopy for Materials Discovery


Abstract

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with domain applications ranging from theory and materials prediction to high-throughput data analysis. However, the constantly emerging question is how to match the correlative nature of classical ML with hypothesis-driven causal nature of physical sciences. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment (AE) in imaging.


In this presentation, I will discuss recent progress in automated experiment in electron and scanning probe microscopy, ranging from feature to physics discovery via active learning. The applications of classical deep learning methods in streaming image analysis are strongly affected by the out of distribution drift effects, and the approaches to minimize though are discussed. We further present invariant variational autoencoders as a method to disentangle affine distortions and rotational degrees of freedom from other latent variables in imaging and spectral data. The analysis of the latent space of autoencoders further allows establishing physically relevant transformation mechanisms. Extension of encoder approach towards establishing structure-property relationships will be illustrated on the example of plasmonic structures. I will further illustrate transition from post-experiment data analysis to active learning process, including learning structure-property relationships and materials discovery in composition spread libraries. Here, the strategies based on simple Gaussian Processes often tend to produce sub-optimal results due to the lack of prior knowledge and very simplified (via learned kernel function) representation of spatial complexity of the system. Comparatively, deep kernel learning (DKL) methods allow to realize both the exploration of complex systems towards the discovery of structure-property relationship, and enable automated experiment targeting physics (rather than simple spatial feature) discovery. The latter is illustrated via experimental discovery of the edge plasmons in STEM/EELS and ferroelectric domain dynamics in PFM. For composition spread libraries, I will demonstrate the combination of the structured Gaussian process and reinforcement learning, the approach we refer to as hypothesis learning.


Biography

Sergei Kalinin is a corporate fellow and a group leader at the Center for Nanophase Materials Sciences at Oak Ridge National Laboratory. He received his MS degree from Moscow State University in 1998 and Ph.D. from the University of Pennsylvania (with Dawn Bonnell) in 2002. His research presently focuses on the applications of big data and artificial intelligence methods in atomically resolved imaging by scanning transmission electron microscopy and scanning probes for applications including physics discovery and atomic fabrication, as well as mesoscopic studies of electrochemical, ferroelectric, and transport phenomena via scanning probe microscopy.


Sergei has co-authored >650 publications, with a total citation of >33,000 and an h-index of >94. He is a fellow of MRS, APS, IoP, IEEE, Foresight Institute, and AVS; a recipient of the Blavatnik Award for Physical Sciences (2018), RMS medal for Scanning Probe Microscopy (2015), Presidential Early Career Award for Scientists and Engineers (PECASE) (2009); Burton medal of Microscopy Society of America (2010); 4 R&D100 Awards (2008, 2010, 2016, and 2018); and a number of other distinctions.

Very much looking forward to the seminar – hope to see you all on Wednesday: (SGT) 9am, Wed, 8 December! https://mit.zoom.us/j/96231985116

Warmest regards, @Thway @kedar @Tonio


© 2024 by Kedar Hippalgaonkar. Created with Wix.com

Materials Science and Engineering, Nanyang Technological University

Institute of Materials Research and Engineering, Singapore

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