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JUNE 9-11, 2025 | AUSTIN, TX |
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JUNE 9-11, 2025 | AUSTIN, TX |
The use of AI and ML have revolutionized the way new materials are being developed and it has shown to be the most effective when combined with physics based simulation models. Especially to boost the accuracy level of the simulation of materials and related material properties AI/ML techniques play a key role. In this symposium the latest developments in this field will be presented and discussed with a focus on relevant cases from industry.
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Back to Top ↑2024 Symposium Sessions | ||
Monday June 17 | ||
| 3:30 | AI-Accelerated Materials Design and Deployment Town Hall - Materials Genome Initiative (MGI) | |
Tuesday June 18 | ||
| 1:30 | AI Modeling & Simulation | |
| 4:00 | AI Modeling & Simulation - Posters | |
2024 Symposium Program | ||
Monday June 17 | ||
| 3:30 | AI-Accelerated Materials Design and Deployment Town Hall - Materials Genome Initiative (MGI) | Annapolis 3-4 |
| Artificial intelligence (AI) has the potential to revolutionize the way we design, develop, and deploy new materials. This town hall will bring together industry leaders to discuss priorities and strategies for harnessing AI to accelerate materials innovation. Join us for a lively discussion on topics such as autonomous R&D, AI-enabled exploration of the vast materials design space, and opportunities for collaboration among industry, government, and academia. | ||
| Session chair: Lisa E. Friedersdorf, Office of Science and Technology Policy, US | ||
| Moderator B. Segal, Lockheed Martin, US | ||
| Panelist L. Lee, IBM Research (Zürich), CH | ||
| Panelist S. Arturo, Dow, US | ||
| Panelist C. Boswell-Koller, National Science Foundation, US | ||
| Panelist E. Breckenfeld, NVIDIA, US | ||
Tuesday June 18 | ||
| 1:30 | AI Modeling & Simulation | Chesapeake C |
| Session chair: Jan-Willem Handgraaf, Siemens, NL | ||
| Towards chemical foundation models for digital prediction of experimental measurements E. Annevelink, Physics Inverted Materials, US | ||
| Utilizing Genetic Algorithms for Autonomous RF FEM Simulation & Optimization V. Gjokaj, NuPhotonics LLC, US | ||
| Leveraging Physics-Based Simulations and Machine Learning to Identify Promising Formulations for Materials Science Applications A.K. Chew, M.A.F. Afzal, A. Chandrasekaran, M.D. Halls, Schrödinger, US | ||
| When Can We Ignore Missing Data in Model Training? C. Zhen, A. Singh, A. Termehchy, Oregon State University, US | ||
| Analyzing and optimizing CO2 geothermal energy production utilizing artificial intelligence – a deep basin approach K. Katterbauer, A. Alhashboul, H. Chen, A. Yousef, Saudi Aramco, SA | ||
| Common Data Model to Rapidly Certify AM Parts with Reduced Inspection Leveraging AI / ML D. Reed, J. Shah, W. Sobol, T. Kirk, A. Kitt, MxD USA, US | ||
| 4:00 | AI Modeling & Simulation - Posters | Expo Hall BC |
| The effect of moisture on the mechanical and thermophysical properties of the crosslinked network of the SU-8 photoresist. A. Goldberg, A.R. Browning, T. Morisato, T. Vadicherla, M.D. Halls, Schrodinger, US | ||
| Finite Difference Simulation of Surface Smoothing Induced by Atomic Layer Etching M.F. Leung, Pasadena City College, US | ||
| Using Advanced Hybrid Power Systems Controls for Precision Sustainment Through AI D. Moorman, Moser Energy Systems, US | ||
| Estimating solid-liquid interfacial anisotropy using phase-field simulations and machine learning G. Kim, S. Hyun, H. Ko, Korea Institute of Ceramics Engineering and Technology, KR | ||
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