AI, Modeling, and Simulation for Advanced Materials Design

JUNE 9-11, 2025 | AUSTIN, TX
View Co-Located Events

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 April 4

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

Michael Rauch

Associate Director, Materials Science

Schrödinger

Nobuyuki Matsuzawa

Executive Engineer

Panasonic Industry Co, Japan

Jan-Willem Handgraaf

Senior Technical Product Manager

Siemens Digital Industries Software

Key Speakers Include

Hiromasa Kaneko

Senior Assistant Professor

Meiji University, Japan

Multiscale machine learning accelerates materials computations

Boris Kozinsky

Professor,

Harvard University

Complex Polymer Design in the Age of AI: What, How, and Why?

Michael Webb

Assistant Professor,

Princeton University

Timothy Gardner

Chief Informatics Officer

Cambium

Accelerating Materials Innovation with an Integrated Computational Materials Engineering (ICME) Framework

Sak Arumugam

Senior Product Manager,

Ansys

Better Together: Combining Machine Learning and Physics-Based Models to Accelerate Product Development

Steve Edkins

Director of Strategy and Operations,

Citrine Informatics

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

Symposium Program
	Monday June 9

1:30	Emerging Methods for Materials Design with Artifical Intelligence I
	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
	AI-Driven Innovation for Polymeric Materials and Formulations: The PolymRize Approach R. Gurnani, Matmerize, 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
	Tuesday June 10

9:30	Emerging Methods for Materials Design with Artifical Intelligence II
	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
	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

1:30	Applications of AI-based Materials Design Methods
	Session chair: Jan-Willem Handgraaf, Siemens, US
	Accelerating Materials Innovation with an Integrated Computational Materials Engineering (ICME) Framework S. Arumugam, C. Bream, Ansys, UK
	Designing extreme-performance materials for air, land, sea and space with advanced computation T. Gardner, Cambium, 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
	Machine-Learning-Driven Predictive 3D Ramified Foam Fabrication and Mechanistic Understanding Y. Liu, The University of texas at austin, US
	Sustainable Graphite Production from Plastic and Biomass: a Modeling Approach T. Hossain, L. Toba, Idaho National Laboratory, US

4:00	AI for Materials Design - Posters
	Automated Synthesis and Precision Growth of 2D Materials for Next-Generation Electronics M.A Vignon, Analyticaxpress, US
	A Review of AI-Enhanced Materials for Sustainable Energy Systems H. Saha, A.K. Saha, Haldia Institute of Technology, IN