AI TechConnect Virtual Summit Series Abstracts

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On Demand	AI for Advanced Manufacturing	View Program
On Demand	AI for Materials Characterization	View Program
On Demand	AI for Medical & Biomaterials	View Program
On Demand	AI for Materials Discovery	View Program
On Demand	AI for Materials Design & Innovation	View Program

AI for Advanced Manufacturing

Chairs: Brent Segal, Lockheed Martin and Fiona Case, TechConnect

Artificial intelligence and machine learning are disrupting the manufacturing sector. AI can optimize functional design and leverage more effective manufacturing. Processes can be monitored and re-evaluated in real time to cut unplanned downtime. Enhanced data collection and AI-powered analytics can significantly increase efficiency, product quality and employee safety. Join us for this exciting symposium featuring the latest innovations and future directions for AI in advanced manufacturing.

Introductory remarks

Brent M. Segal, Lockheed Martin

Mind Before Matter: AI-Powered Manufacturing From Concept to Creation

Chris Milroy, NVIDIA

Mechanical Design with a Bayesian Experimental Autonomous Researcher (BEAR)

Keith Brown, Boston University

Machine Learning for Glass Manufacturing

Mathieu Bauchy, UCLA

Semantic Segmentation for 3D Feature Detection in the Automation of High Mix Industrial Processes

Matthew Powelson, Southwest Research Institute

AI for Industry 4.0

Jayant R. Kalagnanam, IBM Research

Discussion Panel - AI for Advanced Manufacturing

Machine Learning for Materials Characterization and Imaging

Chairs: Greg Haugstad, University of Minnesota and Dalia Yablon, TechConnect

The purpose of this symposium is to explore the application of machine learning to various characterization techniques including electron microscopy, probe microscopy, and x-ray based measurements. A natural point at the nexus of ML and such methods is image analysis and processing. In addition to faster, "deeper", and more powerful image processing, additional issues that will be addressed include a broader role for ML improving our current capabilities, enabling new modes and techniques, and ultimately leading to a role in instrument operation.

Introductory remarks

Dalia Yablon, TechConnect

Autonomous Synchrotron X-ray Diffraction for Phase Mapping and Materials Optimization

Aaron Gilad Kusne, NIST

Towards automated information extraction from high resolution transmission electron microscopy images

Mary Scott, University of California, Berkeley

Artificially Intelligent Transmission Electron Microscopy

Huolin Xin, University of California, Irvine

Correlative and causal machine learning in scanning probe and electron microscopy

Maxim Ziatdinov, Oak Ridge National Laboratory

Role of machine learning for atomic force microscopy

Ishita Chakraborty, Stress Engineering

Discussion Panel - Machine Learning for Materials Characterization & Imaging

AI for Biomaterials and Drug Design

Chairs: Payel Das, IBM and Sarah Tao, Sanofi

Innovations in machine learning, artificial intelligence, and big data analysis - coupled with advances in computational modeling and simulation and high throughput experimentation - are revolutionizing biomaterials, drug discovery and development processes. Join the thought leaders who are opening new vistas in medical research.

Introductory remarks

Sarah Tao, Sanofi

Trusted AI Models For Antimicrobial and Antiviral Discovery

Payel Das, IBM

Chemical Discovery and AI-Assisted Chemical Synthesis

Connor W. Coley, Massachusetts Institute of Technology, US

Machine learning methods for the de-novo design of proteins and antibodies

Philip M. Kim, University of Toronto

Combining machine learning with other computational methods for drug design

Glenn Butterfoss, ProteinQure

Explainable Deep Models for Compound-Protein Binding Affinity Prediction and Deep Generative Models for Protein Design

Yang Shen, Texas A&M University

Accelerating Therapeutics With Machine Learning and Modeling for COVID-19

Lalitha Subramanian, Dassault Systemes

Discussion Panel - AI for Biomaterials & Drug Design

AI for Materials Discovery

Chairs: Rick Ross, 3M Company and Peter Koenig, Procter & Gamble

Encoding the physics of materials behavior, and harnessing the domain knowledge from many decades of materials development and testing, machine learning, artificial intelligence and simulation approaches are being used to discover entirely new materials for applications from batteries to biomaterials and packaging materials. Join us as we highlight the latest advances in AI, machine learning and autonomous research approaches for materials discovery, design and innovation.

Introductory remarks

Peter Koenig, Procter & Gamble

Data-Driven Discovery of New Materials for Solid-State Batteries

Yifei Mo, University of Maryland

Accelerating Materials Discovery and Design using AI and Machine Learning

Subramanian Sankaranarayanan, Argonne National Laboratory

Multiscale modeling and machine learning for accelerated decision making for formulation and packaging materials

Lalitha Subramanian, Dassault Systemes

Combining High-throughput Atomic Scale Simulation and Deep Reinforcement Learning in the Discovery of Novel OLED Materials with Targeted Optoelectronic Properties

Yuling An, Schrodinger, Inc.

Machine Learning for the Optimization of Optical Nanomaterials

André-Pierre Blanchard-Dionne, EPFL (École Polytechnique Fédérale de Lausanne)

Adaptive Machine Learning enabled Search for Functional Materials with Targeted Properties

Prasanna V. Balachandran, University of Virginia

Discussion Panel - AI for Materials Discovery

AI for Materials Design & Innovation

Chair: Keith Brown, Boston University

Join us as we highlight the latest advances in AI, machine learning and autonomous research approaches for materials discovery, design and innovation.

Introductory remarks

Keith Brown, Boston University

ML for early drug discovery needs data: DEL to the rescue!

Patrick Riley, Google

AI-accelerated materials innovation: Discovery of electrochromic materials for smart window applications

Christoph Kreisbeck, Kebotix

Materials Design by Integrated Computational Materials Engineering (ICME) and AI

Changning Niu, QuesTek Innovations

Molecular Models Empowering Data-Driven Approaches to Materials Discovery

Jianzhong Wu, University of California, Riverside

Discussion Panel - AI for Materials Design & Innovation

Keith Brown, Boston University
Patrick Riley, Google
Tonio Buonassisi, MIT
Christoph Kreisbeck, Kebotix
Changning Niu, QuesTek Innovations
Jianzhong Wu, University of California, Riverside
Fiona Case, TechConnect