TechConnect World 2020
Co-Located with Nanotech 2020 SBIR/STTR Spring AI TechConnect
Nanotech 2020
 

AI, Modeling & Simulation for Materials Design

Informatics, Modeling, and Simulation

Symposium Co-Chairs

Rick RossRick Ross
Senior Research Chemist
3M Company

Peter KoenigPeter Koenig
Group Head
Procter & Gamble

Key Speakers

Corinne LipscombMaterials Informatics Applications for Structural Adhesives at 3M
Corinne Lipscomb
CPO Materials Informatics,, 3M

Subramanian SankaranarayananAccelerating Materials Discovery and Design using AI and Machine Learning
Subramanian Sankaranarayanan
Scientist, Argonne National Laboratory

Anne FischerAnne Fischer
Program Manager
DARPA

Lalitha SubramanianMultiscale modeling and machine learning for accelerated decision making for formulation and packaging materials
Lalitha Subramanian
Director, Dassault Systèmes

Yifei MoData-Driven Discovery of New Materials for Solid-State Batteries
Yifei Mo
Associate Professor, Maryland Energy Innovation Institute, University of Maryland

Materials modeling and simulation approaches continue to provide valuable insights and guidance for researchers working on new materials and product development across a wide range of industries. It is becoming clear that by encoding the physics of materials behavior, and capturing the domain knowledge from many decades of materials development and testing, these techniques may provide even greater value in machine learning and AI approaches.

This symposium will highlight the latest advances in AI, machine learning and autonomous research approaches for materials development.

We will also highlight applications-focused theoretical developments, industry applications and case studies in materials modeling and simulation (across all length scales).

Submit your abstract today and plan to share your research results and insights at this exciting event.

 
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Symposium Sessions

Monday June 29

10:30AI, Modeling and Simulation for Materials Design
1:30AI, Modeling and Simulation for Materials Design

Tuesday June 30

8:30AI and Machine Learning Fireside Chat
10:30AI, Modeling and Simulation for Materials Design
1:30AI for Biomaterials and Drug Design
4:00Machine Learning for Materials Characterization & Imaging - Posters

Wednesday July 1

10:30Design, Modeling, Simulation & Software Innovation
10:30Innovations for Next-gen AI
4:00Innovations in AI - Posters

Symposium Program

Monday June 29

10:30AI, Modeling and Simulation for Materials Design
Session chair: Rick Ross, 3M Company, US; Peter Koenig, Procter & Gamble, US
Accelerating Materials Discovery and Design using AI and Machine Learning
S. Sankaranarayanan, University of Illinois at Chicago, US
Molecular Models Empowering Data-Driven Approaches to Materials Discovery
J. Wu, University of California, Riverside, US
Combining High-throughput Atomic Scale Simulation and Deep Reinforcement Learning in the Discovery of Novel OLED Materials with Targeted Optoelectronic Properties
Y. An, T.F. Hughes, D.J. Giesen, A. Chandrasekaran, M.A.F. Afzal, H.S. Kawk, K. Leswing, K. Marshall, T. Robertson, M.D. Halls, Schrodinger, Inc., US
An Improved Sampling Technique For Accelerated Numerical Simulations with Hybrid Uncertainties
F. Pourkamali-Anaraki, M.A. Hariri-Ardebili, S. Sattar, University of Massachusetts Lowell, US
Adaptive Machine Learning enabled Search for Functional Materials with Targeted Properties
P.V. Balachandran, University of Virginia, US
Deep Materials Informatics: Illustrative Applications of Deep Learning in Materials Science
A. Agrawal, Northwestern University, US
1:30AI, Modeling and Simulation for Materials Design
Session chair: Rick Ross, 3M Company, US; Peter Koenig, Procter & Gamble, US
Materials Informatics Applications for Structural Adhesives at 3M
C. Lipscomb, 3M, US
Multiscale modeling and machine learning for accelerated decision making for formulation and packaging materials
L. Subramanian, Dassault Systemes, US
Machine Learning for the Optimization of Optical Nano-Materials
A.-P. Blanchard-Dionne, O.J.F. Martin, Ecole Polytechnique Federale de Lausanne, CH
Identifying Crystal Structure from Open and Accessible Materials
J. Tate, J. Aguiar, M.L. Gong, T. Tasdizen, University of Utah, US
SMILES-X: Autonomous Molecular Compounds Characterisation For Small Datasets Without Descriptors
G. Lambard, E. Gracheva, National Institute for Materials Science, JP
AI-driven Advanced Materials-Manufacturing Innovation
M. Kolel-Veetil, S. Kalidindi, Naval Research Laboratory, US

Tuesday June 30

8:30AI and Machine Learning Fireside Chat
Session chair: Brent M. Segal, Lockheed Martin, US
10:30AI, Modeling and Simulation for Materials Design
Session chair: Rick Ross, 3M Company, US; Peter Koenig, Procter & Gamble, US
Data-Driven Discovery of New Materials for Solid-State Batteries
Y. Mo, University of Maryland, US
TBA
C. Kreisbeck, Kebotix, Inc., US
Self-driving laboratory for accelerated discovery of thin-film materials
C.P. Berlinguette, J.E. Hein, A. Aspuru-Guzik, B.P. MacLeod, F.G.L. Parlane, University of British Columbia, CA
Topology-Informed Machine Learning for the Prediction of Glass Properties
M. Bauchy, University of California, Los Angeles, US
Materials Design by Integrated Computational Materials Engineering (ICME) and AI
C. Niu, A. Saboo, Y. Lin, S. Sorkin, P. Lu, J. Gong, QuesTek Innovations LLC, US
1:30AI for Biomaterials and Drug Design
Session chair: Payel Das, IBM. Thomas J. Watson Research Center, US, Sarah Tao, Sanofi, US
Chemical Discovery and AI-Assisted Chemical Synthesis
C. Coley, Massachusetts Institute of Technology, US
Combining machine learning with other computational methods for drug design
G. Butterfoss, ProteinQure, CA
TBA
D. Marks, Harvard University Medical School, US
Machine learning methods for the de-novo design of proteins and antibodies
P. Kim, University of Toronto, CA
Accelerating Drug Discovery With Outcome-based Data Science and AI Application
L. Subramanian, S. Schweizer, 3DS, US
Explainable Deep Models for Compound-Protein Binding Affinity Prediction and Deep Generative Models for Protein Design
Y. Shen, Texas A&M University, US
4:00Machine Learning for Materials Characterization & Imaging - Posters
Machine learning for microstructures classification in functional materials
A.K. Choudhary, A. Jansche, O. Badmos, T. Bernthaler, G. Schneider, Aalen University, DE
A Machine Learning Driven Damage Quantification Algorithm in moisture-contaminated composites.
R.D. Guha, North Carolina State University, US
Application of Savitzky-Golay(SG) filter in image processing
S. Karmakar, S. Karmakar, Farmingdale State College- State University of New York, US

Wednesday July 1

10:30Design, Modeling, Simulation & Software Innovation
Session chair: Nanci Hardwick, MELD. Manufacturing Corporation, US
Additive manufacturing needs validated machine learning for simulation-based part quantification
W.K. Liu, Northwestern University, US
Large Generative Designs Optimized for Production Through Simulation and Produced with Multi-Axis Hybrid Metal Additive
S. Gardner, Big Metal Additive, LLC, US
Integrated process-structure-property modeling framework and methods for process design and performance prediction of additively manufactured material systems
W.K. Liu, Z. Gan, C. Yu, O.L. Kafka, K.K. Jones, Y. Lu, Northwestern University, US
Prediction and optimization of surface roughness in additive manufacturing with data-driven multiphysics models
Z. Gan, K.K. Jones, Y. Lu, L. Cheng, J. Lua, G. Wagner, W.K. Liu, Northwestern University, US
High Fidelity vs Low Fidelity Physics-Based Modeling of Laser Powderbed Fusion Processes: Accuracy vs. Speed
C. Katinas, Y.C. Shin, Purdue University, US
AM computational tech: Software for multiscale simulation and process modeling of additive manufacturing
H. Hosseinzadeh, Rowan University, US
Modeling Optical, Reaction, and Transport Effects in Continuous Stereolithographic 3D Printing
Z.D. Pritchard, M.P. de Beer, R.J. Whelan, T.F. Scott, M.A. Burns, University of Michigan, US
10:30Innovations for Next-gen AI
TBA
C. Milroy, NVIDIA, US
TBA
P. Das, IBM Thomas J Watson Research Center, US
How Can the DoD Leverage Big Data, AI and Machine Learning to Accelerate UxS Integration and Decision Making
G. Galdorisi, Naval Information Warfare Center Pacific, US
In-Storage Distributed Machine Learning for the Edge
V. Alves, NGD Systems, Inc., US
Graph-Centric Machine Learning: Algorithms, Systems, and Cybersecurity Applications
H. Huang, George Washington University, US
A Study of Famous CNN Architectures to Have Descent Base Models
M. Bari, T-Mobile, US
Concept for a Natural Language Processing (NLP) Application: Artificial Intelligence (AI) Technology for Text and Language Search (ATTLS)
M. Niv, N. Kumar, E. Henry, T&T Consulting Services, Inc, US
4:00Innovations in AI - Posters
An EHR using AI technology as a Clinical Decision Support Tool
J.M. Penn, Guidance Founation Inc., US
Autonomous Conceptualization and Design by DABUS
S. Thaler, Imagination Engines, Inc., US
IoT + DDoS = Disruptive (Business + Cyber) Risk!
A. Pabrai, ecfirst, US
Improve Health Outcomes and Maximize Quality Improvement: Using Artificial Intelligence Models
S. Kapoor, HealthEC, US
A Review of AI Influence in Intellectual Property Law
D. Mottley, Howard University, School of Law, US
ABSCA - Boost Converter Switching Controller using Machine Learning Algorithms
B. Abegaz, Loyola University of Chicago, US
AROSV - An ROS based Self-Driving Vehicle Controller using Unsupervised Machine Learning Methods
B. Abegaz, Loyola University of Chicago, US
Dropping 500 Feet in 20 Seconds: Simulating the Cockpit Experience of an Airliner with a Trim Control Failure
A. Redei, Central Michigan University, US
Artificial Intelligence Trends Based on the Patents Granted by the United States Patent and Trademark Office
H.H.N. Abadi, M. Pecht, University of Maryland - Center for Advanced Life Cycle Engineering (CALCE), US
A Survey of Artificial Intelligence Funding in China
Z. He, W. Diao, M.G. Pecht, University of Maryland, US
Next Generation PCIe Network Fabric for High Performance AI Computing
C. Reynolds, Technical Systems Integrators, US
AI - Lack of data characterization significantly reduces accuracy of AI results
M. Gilger, Modus Operandi, US
 
2019 Sponsors & Partners
2019 Sponsors & Partners