A Technical Framework for Materials by Design in Enterprise R&D

DATE

February 25, 2026

TIME

8:00 a.m. PST, 11:00 a.m. EST, 16:00 GMT, 17:00 CET- Duration: 60 Minutes

Overview

Materials by Design (MbD), a high-value computational design paradigm, represents a fundamental shift from the traditional trial-and-error approach to an optimization-based approach to materials discovery and process development.

Advancements in scientific artificial intelligence (AI) have enabled new possibilities in materials R&D, making MbD more tractable and achievable for industry-relevant problems and timescales.

Join Enthought’s AI and Materials Informatics experts for an essential discussion about Materials by Design and a modern technical framework for achieving it in enterprise R&D by leveraging scientific AI and machine learning, focusing on three core concepts:

addressing unknown, complex, and conflicting performance requirements
navigating and constraining vast search spaces and getting better results with less data
balancing broad exploration with the efficient exploitation of known, high-potential areas

Attendees will also learn about real-world use cases from leading specialty chemicals and advanced materials companies who are successfully driving towards MbD for transformational market advantage.

Key Learning Objectives:

To gain a foundational understanding of scientific AI/ML-enabled Materials by Design
To understand the technical considerations to achieve Materials by Design in enterprise R&D
To hear real-world use cases from specialty chemicals and advanced materials companies

Who Should Attend:

Materials and chemistry R&D leaders
R&D AI and technology leaders
Materials product owners and R&D teams
Materials Informatics leaders
Principal scientists and senior computational scientists

Brought to you by:

Speakers

Michael Connell, EdD
Chief Operating Officer,
Enthought

Connell holds an M.S. in Electrical Engineering and Computer Science from MIT, and a doctorate in Education from Harvard University. He is a learning scientist with a background in robotics, artificial intelligence, software engineering, and education. He is passionate about using data science to solve practical problems, from data mining to predictive modeling to adaptive learning design. Connell is a former software design engineer for Microsoft and has also served as a faculty member at Harvard University, Dartmouth College, and The University of Texas at Austin.

Michael Heiber, Ph.D.
Director, Professional Services and Customer Success, Materials Informatics,
Enthought

Heiber holds a Ph.D. in polymer science from The University of Akron and a B.S. in materials science and engineering from the University of Illinois at Urbana-Champaign with expertise in polymers for optoelectronic applications. He leads Enthought’s Materials Informatics technical team and supports customers in large-scale MI initiatives. Prior to joining Enthought, he worked as a postdoctoral researcher at several institutions, where he developed improved physical models for organic electronic devices that connect the complex materials microstructure to semiconductor device physics.

Melissa O’Meara
Forensic Science Consultant,
C&EN Media Group

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