Symposium Sessions |
| Monday June 17 |
10:30 | AI for Advanced Manufacturing |
1:30 | AI for Biomaterials, Medical and Biotech Applications |
| Tuesday June 18 |
10:30 | AI for Materials Development |
1:30 | AI for Materials Development |
| Wednesday June 19 |
8:30 | AI Keynotes |
10:30 | AI for Materials Development |
1:30 | AI Innovations & Implementation |
3:30 | Materials Modeling, Informatics & Machine Learning: Posters |
3:30 | AI & Machine Learning Applications: Posters |
|
Symposium Program |
| Monday June 17 |
|
10:30 | AI for Advanced Manufacturing | 304 |
| Session chair: Brent Segal, Lockheed Martin, US |
10:30 | AI at Scale: Real World Industrial Applications C. Lefebvre, nDimensional, US |
10:50 | Science-Guided AI for Development of New Biofuels and Bioenergy Production Technologies M. Urgun-Demirtas, Y. Lin, P. Laible, Argonne National Laboratory, US |
11:10 | Rapid Artificially Intelligent Design S. Guerin, Z. Rogers, Additive Rocket Corporation, US |
11:30 | Robot Axis Control Using a Differential Learning Algorithm B. Abegaz, Loyola University of Chicago, US |
11:50 | Tension prediction using web moving speed and natural vibration frequency X. Du, J. Yan, University of Massachusetts, Amherst, US |
|
1:30 | AI for Biomaterials, Medical and Biotech Applications | 304 |
| Session chair: Sarah Tao, Sanofi, US |
1:30 | Automating Chemical Synthesis using AI and Automated Systems P. Madrid, J.P. Malerich, M. Latendresse, M. Krummenacker, D. Stout, J-P. Lim, Vi-Anh Vu, J. Szeto, K. Rucker, J. White, N. Collins, SRI, US |
1:55 | The Concept of “Sniper Shoot” in Discovery of a New Drugs A. Yu Rogachev, Illinois Institute of Technology, US |
2:15 | Next-Generation Cheminformatics Approaches for Rational Drug Discovery D. Fourches, North Carolina State University, US |
2:35 | Deep convolutional neural network and image prior based super resolution for X-ray nano-tomography K.C. Prabhat, V. DeAndrade, N. Kasturi, X. Yang, Argonne National Laboratory; The University of Chicago, US |
2:55 | Effective Radiation Therapy Using Accurate Tumor Segmentation G. Rasool, Rowan University, US |
| Tuesday June 18 |
|
10:30 | AI for Materials Development | 304 |
| Session chair: Peter Koenig, Procter & Gamble, US |
10:30 | Learning from Small Data: Optimization of Complex Material Systems with Hierarchical Machine Learning A. Menon, N. Washburn, Carnegie Mellon University, US |
10:55 | AI and Machine Learning for Accelerating Materials Design and Discovery S. Sankaranarayanan, Argonne National Laboratory, US |
11:20 | Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals C. Chen, University of California, San Diego, US |
11:45 | Combining data-driven models with physics-based models for industrial materials discovery, design, and manufacturing L. Subramanian, Dassault Systems, US |
12:10 | Accelerating Materials Development with Uncertainty-Aware Sequential Learning M. Hutchinson, Citrine Informatics, CA |
|
1:30 | AI for Materials Development | 304 |
| Session chair: Keith Brown, Boston University, US |
1:30 | Towards Autonomous Materials Research Systems J. Hattrick-Simpers, NIST, US |
1:55 | Autonomous Research for Carbon Nanotube Synthesis Benji Maruyama, Air Force Research Laboratory, US |
2:20 | Polymer Genome: An Informatics Platform for Rational Polymer Design H. Tran, Georgia Institute of Technology, US |
2:45 | A Self-Driving Laboratory for Accelerating Materials Discovery C.P. Berlinguette, J.E. Hein, A. Aspuru-Guzik, B.P. MacLeod, F.G.L. Parlane, B. Lam, University of British Columbia, CA |
3:05 | Smart design of organic and organometallic materials using automated machine learning methods and workflows H.S. Kwak, T. Robertson, C.M. Krauter, K. Leswing, M.D. Halls, Schrodinger, Inc., US |
3:25 | Computational screening of Li and Na fast ion conductors using high-throughput bond-valence calculations and machine-learning analysis Javier Carrasco, CIC Energigune, ES |
| Wednesday June 19 |
|
8:30 | AI Keynotes | 304 |
| Session chair: Brent Segal, Lockheed Martin, US |
8:30 | Welcome - Sector Expanding AI Innovation Impact B.M. Segal, Lockheed Martin, US |
8:55 | AI and Robotics for Rapid Innovation of Materials J.S. Becker, Kebotix, US |
9:20 | Advances in AI for Design and Discovery J. Kautz, NVIDIA, US |
|
10:30 | AI for Materials Development | 304 |
| Session chair: Peter Koenig, Procter & Gamble, US |
10:30 | The Search for Ground Truth: Machine Learning for Mechanical Design K. Brown, Boston University, US |
10:55 | Towards Trusted AI For Advancing Science and Innovation P. Das, IBM Thomas J Watson Research Center, US |
11:20 | Predicting potential energy surfaces with machine learning M. Hellström, Software for Chemistry & Materials BV, NL |
11:40 | Coarse-grained modeling of polycrystalline ice in supercooled water H. Chan, M. Cherukara, B. Narayanan, T. Loeffler, C. Benmore, S. Gray, S. Sankaranarayanan, Argonne National Laboratory, US |
12:00 | Machine Learning for Glass Science and Engineering M. Bauchy, University of California, Los Angeles, US |
|
1:30 | AI Innovations & Implementation | 304 |
| Session chair: Brent Segal, Lockheed Martin, US |
1:30 | 3 Tiers of Cyber Security - The Future of Cyber Science M.H. Nance, C. Johnson-Bey, Lockheed Martin, US |
1:50 | MiniZinc - a constraint modeling language D. Hemmi, Monash University, AU |
2:10 | Telling the story of AI internally - the missing piece of implementation E. Thoresen, Midwest Capital Group, US |
|
3:30 | Materials Modeling, Informatics & Machine Learning: Posters | Boylston Hallway |
| Analysis of Gate-Length Dependence of Lags and Current Collapse in Field-Plate AlGaN/GaN HEMTs T. Chiba, Y. Saito, R. Tsurumaki, K. Horio, Shibaura Institute of Technology, JP |
| Analysis of Breakdown Characteristics in Field-Plate AlGaN/GaN HEMTs: Dependence on Deep-Acceptor Density in Buffer Layer S. Akiyama, M. Kondo, L. Wada, K. Horio, Shibaura Institute of Technology, JP |
| Improved Electric Field Decomposition Capacitance Model with 3-D Terminal and Fringe Components in Sub-28nm Interconnect S. Ueda, R. Tomita, Y. Kawada, K. Horio, Shibaura Institute of Technology, JP |
| Interaction modeling of interfacial surfaces with molecular agents: an approach to the problem of bioaccumulation of lead in fish O. Torres, E. González, Pontificia Universidad Javeriana, CO |
| Molecular Dynamics Simulation Study on Nanoelectromechanical Oscillator based on Graphene Nanoflake O.K. Kwon, J.W. Kang, Semyung University, KR |
| Violation of the Zeroth Law of Thermodynamics -- Thermodynamic Properties of a long-range Interacting System Z-Y. Yang, Duke University, US |
|
3:30 | AI & Machine Learning Applications: Posters | Boylston Hallway |
| Intelligent Transportation Asset Management System P. Bhavsar, N. Bouaynaya, Y. Mehta, G. Rasool, CREATEs at Rowan University, US |
| A Learning-based Approach to Cover Short-term Camera Failure in a Monocular Visual Inertial Odometry System Y. Tian, Embry-Riddle Aeronautical University, US |
| Feedback Control of an ROS-Enabled Autonomous Vehicle B. Abegaz, Loyola University of Chicago, US |
| Smart Control of an Electric Power Assisted Steering (EPAS) B. Abegaz, Loyola University of Chicago, US |
| Fader Axis D. Hemmi, Monash University, AU |
| Bayesian Reasoning For Better Thinking D. Hemmi, Monash University, AU |
| Image-based damage conditional assessment of large-scale infrastructure systems using remote sensing and deep learning approaches H. Pan, Z. Zhang, X. Wang, Z. Lin, North Dakota State University, US |
| Real World Optimization of Traffic Flow Inside a Large Manufacturing Facility M. Griffin, Insight, US |
|
The Informatics, Modeling and Simulation symposium provides a comprehensive forum for the multidisciplinary materials design, modeling, simulation, and informatics communities. You are invited to submit on topics ranging from applications-focused theoretical developments to industry applications and case studies.