JUNE 17-19, 2024
WASHINGTON, DC    

AI TechConnect
 

AI, Modeling, and Simulation for Advanced Materials Design

Symposium Co-Chairs

Shruti VenkatramShruti Venkatram
Materials Data Scientist
3M

Jan-Willem HandgraafJan-Willem Handgraaf
Senior Technical Product Manager
Siemens Digital Industries Software


 
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2024 Symposium Sessions

Tuesday June 18

1:30AI Modeling & Simulation
4:00AI Modeling & Simulation - Posters

2024 Symposium Program

Tuesday June 18

1:30AI Modeling & SimulationChesapeake C
Session chair: Jan-Willem Handgraaf, Siemens, NL
Towards chemical foundation models for digital prediction of experimental measurements
E. Annevelink, Physics Inverted Materials, US
Digital methods for material discovery, process design, scaleup and manufacturing
R. Aglave, J.-W. Handgraaf, T. Sweere, T. Eppinger, S. Gaagat, Siemens Digital Industries Software, 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
4:00AI Modeling & Simulation - PostersExpo 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
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
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
Topics & Application Areas
  • AI, Modeling & Simulation for Materials Design
  • Materials Informatics
  • Machine Learning
  • Autonomous Research Approaches
  • Quantitative Structure Property Relationship (QSAR) Methods
  • Data Science
  • Intersection of Simulation and Experimentation
  • Method Development
  • Battery Application
  • Sustainability Application
  • Other
 

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