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Concurrent Materials Design, Accelerated by AI
This article references topics presented by Dr. Michael Heiber at Enthought’s 2025 R&D Innovation Summit in Tokyo. Link...
Innovation in Materials R&D
Companies that synthesize, formulate, process, and integrate advanced materials and chemicals into their products are facing heightened innovation pressures—and opportunities. Historically high-margin specialty products are becoming more commoditized, and the race is on for companies to differentiate themselves in existing markets and establish a leading presence in new emerging markets.
Industry success now more than ever is being dictated by the ability to continuously develop and/or leverage innovative new materials and chemicals. To find success, companies are actively investing in Materials Informatics.
What is Materials Informatics?
Materials Informatics (MI) is the application of data science, AI and machine learning, and materials science and chemistry to accelerate the discovery, design, and development of new materials. It leverages real-world and synthetic datasets, computational models, and algorithms to identify patterns and predict the properties of materials, enabling researchers to uncover novel insights more quickly than traditional trial-and-error methods. MI significantly reduces time and cost in materials R&D by providing actionable insights that can guide experiments and innovation.
Materials Informatics Solutions
Enthought partners with a diverse range of leaders in the materials science and engineering fields. We understand the pressures faced by those at the forefront of materials innovation:
- R&D executives and leaders striving to meet ambitious mandates and demonstrate the value of their R&D investments.
- Materials scientists and engineers battling long development cycles, siloed data, and inefficient manual workflows.
- Process engineers challenged with scaling novel materials from initial lab discoveries to full-scale production and the constant need to optimize complex manufacturing processes.
- Data and AI Leaders seeking to operationalize AI/ML solutions for scientific problems and build confidence in predictions drawn from sparse data.
Accelerate Materials
Discovery & Design
Predictive modeling
Adaptive Experimental Design
Agentic AI systems
Optimize Manufacturing
Processes & Scale-up
Process optimization
Surrogate models and digital twins
Real-time quality control
Unlock New Insights
from R&D Data
Data harmonization
Analytical pipelines
Multimodal knowledge systems
Digitalize &
Scale Expertise
Codify expert decision-making
Reproducible R&D workflows
Data augmentation
Improve Reproducibility
& Trust
Reproducible pipelines
Certifiable predictions through OUQ
Physics-informed AI
Automate Standard &
Repetitive Tasks
Data cleaning
Data collection and data processing
Properties calculations
