Materials by Design

Materials by Design in R&D

Design Materials Smarter, Faster, and With Fewer Experiments

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The Power of Materials by Design

Materials by Design (MbD) is a high-value computational design paradigm, representing a fundamental shift from the traditional trial-and-error approach to an inverse design 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.

R&D organizations that take a MbD approach transform slow, serial trial-and-error workflows into design-driven discovery engines that dramatically accelerate time-to-market while reducing experimental cost.
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R&D as a Search Problem

At its core, materials and chemistry R&D is a search problem—finding a new or novel compound that meets strict specifications and scales to manufacturing. R&D teams must search through a vast space of possible materials, with knowledge scattered and siloed across human experts, data sources, and instruments.

Today, that search is mostly manual:
formulate → test → analyze → repeat

Materials by Design transforms the R&D search problem into a computationally guided system.

The Materials by Design Framework
Specify the Design Space

Specify the Design Space

Define objectives, constraints, and key parameters.

Shape the Design Space

Shape the Design Space

Use data and models to structure the space and focus on viable regions.

Navigate the Design Space

Navigate the Design Space

Search efficiently using computational tools instead of blind experimentation.

These stages reflect how teams move from manual search to model-guided discovery.

What We Deliver

Enthought’s Materials by Design Roadmap

Materials by Design is a multi-year transformation. It isn’t something you can or should buy off the shelf.

 

Enthought’s Roadmap provides a tactical plan for R&D teams to move from costly and slow trial-and-error experimentation to model-guided and increasingly automated discovery, delivering value at each stage.
Enthought's Materials by Design Roadmap

Purpose-Built Solutions Leveraging the Best Fit Technologies

Our solutions are built on proven architectures and the latest advances in AI, cloud infrastructure, and workflow automation—ensuring scalability and reliability for even the most complex scientific environments.
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Core Technologies

Machine Learning, Deep Learning, Bayesian Optimization, Generative Adversarial Networks, Graph Neural Networks

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Advanced Modeling & Systems

Reasoning Models, Multi-Scale Modeling, Surrogate Modeling, Simulation, Image Processing, Agentic AI Systems

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Language & Generative AI

Natural Language Processing, Foundation Models, Generative AI, Large Language Models

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Ready to Take the Next Step?

Let’s discuss how Materials by Design can make your innovation pipeline faster, more reliable, and more predictable.
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