This webinar series and panel events are organized by Dilip Krishnamurthy and Venkat Viswanathan with the goal of cross-pollinating ideas between the various emerging methods at the intersection of physics and machine learning.
Webinar Format: Presenters can use the opportunity to showcase a paper or two with an explicit focus on the methodology and approach. Duration: 40 minutes of methodology + 20 minutes of implementation (code) walk-through + 20 minutes of questions. Invited session chairs will guide the discussion along with offering their perspective on the field. The Q&A session is typically very interactive with a small group of enthusiastic audience.
Seminar Time: Thursdays 11 am to 12:30 pm Eastern Time
Mar 11: Melanie Senn and Alina Negoita, Innovation Center California of Volkswagen Group of America
High-Throughput Screening Framework for Battery Materials Design
Session Chair: Shailendra Kaushik, General Motors
Mar 18: Austin Sendek, Aionics, Inc.
Aionics: Harnessing ML to supercharge battery discovery, design, and deployment in industry
Session Chair: Venkat Viswanathan, Carnegie Mellon University
Mar 25: Payel Das, IBM Thomas J
Watson Research Center
Trustworthiness in AI for Accelerating Discovery
Session Chair: Isidoros Doxas, Northrop Grumman Mission Systems
Apr 1: Aniruddha Mukhopadhyay, ANSYS, Inc.
Shifting Landscape in ML-Driven Product Engineering
Session Chair: Vivek Singh, NVIDIA
Apr 8: Keith Task, BASF
Data Science for Chemicals and Materials Development at BASF
Session Chair: Amra Peles, Pacific Northwest National Laboratory
Molecular ML for Drug Discovery:
Feb 11: Wengong Jin, Massachusetts Institute of Technology
Graph Neural Networks and Generative Models for Drug Discovery
Session Chair: Alex Wiltschko, Google Research
Feb 18: Bharath Ramsundar and Seyone Chithrananda ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction
Session Chair: Tom Miller, Caltech and Entos, Inc.
Feb 25: Dominik Lemm Energy-Free Machine Learning Predictions of Ab Initio Structures
March 4: Mario Krenn Robust Molecular String Representation for Molecular Machine Learning
Session Chair: Olexandr Isayev, Carnegie Mellon University
Panel Discussion on Open Challenges in ML:
This session is focused on discussing challenges and technological bottlenecks at the intersection of machine learning and science/engineering. Industry leaders at original equipment manufacturers (OEMs) and venture capitalists (VCs) will provide their perspective and directions for research and development. We anticipate that this session will facilitate effective TT & O (Tech. Transfer and Outreach).
Oct 29 (note schedule change): Jan Hermann
, Freie Universität Berlin & Humboldt University of Berlin Physics PhD Deep neural network solution of the electronic Schrödinger equation
Session Chair: Prof. Giuseppe Carleo, École polytechnique fédérale de Lausanne (EPFL)