AI, Modeling, and Simulation for Advanced Materials Design

MARCH 10-12, 2026 | RALEIGH, NC
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AI, Modeling, and Simulation
for Advanced Materials Design

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.

Submit Your Abstract - Due January 9

Please first review the information for authors — abstract submission guidelines.

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

Symposium Co-Chairs

Jan-Willem Handgraaf

Senior Technical Product Manager

Siemens Digital Industries Software

Michael Webb

Assistant Professor

Princeton University

Key Speakers

Bridging Accuracy and Scale: AI-Accelerated Atomistic Simulation with Matlantis™

Taku Watanabe

Principal researcher,

Matlantis

AI-driven polymers & formulations innovations at the industrial scale

Jahavi Nistane

Scientist,

Matmerize, Inc

Accelerating Polymer Discovery: Integrating High-Throughput Automation and Machine Learning for Tailored Macro

Matt Tamasi

CTO,

Plexymer, Inc.

Back to Top ↑ 2025 Symposium Sessions
	Monday June 9
1:30	Emerging Methods for Materials Design with Artificial Intelligence I
4:00	AI for Materials Design - Posters
	Tuesday June 10
9:30	Emerging Methods for Materials Design with Artificial Intelligence II

2025 Symposium Program
	Monday June 9

1:30	Emerging Methods for Materials Design with Artificial Intelligence I	Room 311
	Session chair: Michael Rauch, Schrodinger, US
	Multiscale machine learning accelerates materials computations B. Kozinsky, Harvard University, US
	Complex Polymer Design in the Age of AI: What, How, and Why? M.A. Webb, Princeton University, US
	Towards Complex Materials Development: Integration of Physics-Based and Machine Learning Approaches E.M. Collins, K.B. Moore, H. Abroshan, D. Giesen, M.D. Halls, A. Chandrasekaran, Schrödinger, Inc., US
	CAMINNO Gen6: Enhancing Advanced Manufacturing for High-Tech Applications with Digital Twins and Scientific Machine Learning A. Stein, CAMINNO, Inc., US

4:00	AI for Materials Design - Posters	JW Grand Ballroom
	Model Based Iterative Reconstruction (MBIR) X-Ray computed tomography for nano-micro characterization of complex biological structures A.F.D. Ornelas, J. Lipp, C. Bouman, G. Buzzard, M. Ostendorf, V. Roman, J. Roth, D. Kisailus, S. Farajollahi, AFRL, US
	Closing the Gap Between Theory & Experiment: A Retrosynthesis Platform for Inorganic Solids M.J. McDermott, Newfound Materials, US
	Tuesday June 10

9:30	Emerging Methods for Materials Design with Artificial Intelligence II	Room 311
	Session chair: Nobuyuki N. Matsuzawa, Panasonic, US
	Direct Inverse Analysis of Machine Learning Models for Desings of Molecules, Materials, and Processes H. Kaneko, Meiji University, JP
	Designing extreme-performance materials for air, land, sea and space with advanced computation T. Gardner, Cambium, US
	Revolutionizing Materials Science: Accelerating Discovery and Innovation with LQMs T. Mustard, T. Sours, A. Singh, O. Allam, M. Cormier, A. Xiao, SandboxAQ, US
	Surrogate modeling with finite-element-based physics-informed neural networks R.B. Sills, P. Sunil, M. Vasoya, Rutgers University, US
	Better Together: Combining Machine Learning and Physics-Based Models to Accelerate Product Development S.D. Edkins, Citrine Informatics, US
	Reactivity of Detached XLi₆ Nanoclusters with SEI Components: A Computational Framework for Lithium-Ion Battery Stability F.A. Soto, The Penn State University-Harrisburg, US
	Applying Artificial Intelligence (AI) to build new generation gas sensors to positively impact society R.A. Potyrailo, S. Shan, T. Wang, GE Vernova Advanced Research Center, US