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 to video below.Over the last...
Software & AI
Scientific Software Development, Legacy Software Modernization, UI/UX,
Predictive Modeling, Custom Simulations, Web Applications,
Multimodal Knowledge Systems, API Development
Data Systems
Data Engineering, Process Engineering, Data Pipelining and Augmentation,
Workflow Automation and Redesign, Scientific Data Management Systems,
Data Capture Systems, High Volume Data Management, Database Design
Strategy & Design
R&D AI Transformation, R&D Digital Transformation, Strategic Roadmap Development,
Data System Design, Process Analysis
Infrastructure
Technical Upskilling for Scientists & Engineers, R&D Systems Integration,
R&D IT and Data Ops
Core Technologies
Machine Learning, Deep Learning, Baysian Optimization, Generative
Adversarial Networks, Graph Neural Networks
Advanced Modeling & Systems
Reasoning Models, Multi-Scale Modeling, Surrogate Modeling,
Simulation, Image Processing, Agentic AI Systems
Language & Generative AI
Natural Language Processing, Foundation Models, Generative AI,
Large Language Models
Discovery & Development
Property Prediction, Formulation Optimization, Structure Generation,
Materials Discovery, Materials Compatibility
Data Insights
Text Data Mining, Automated Data Analysis, Time Series Analysis,
Multimodal Search, Literature and Patent Search, Dashboards, Data Visualizations
Decision Support
Chatbots, Predictive Maintenance, Preventative Maintenance, AI
Recommendation Systems
Making Sense of Agentic AI | Join us for a timely webinar on agentic AI in materials & chemistry R&D.
In recent years, the advancement of computational capabilities and artificial intelligence has profoundly impacted materials science and chemistry research and product development. Enthought is always exploring cutting-edge tools, and we are excited about an emerging technique in the Materials Informatics space that could take R&D to the next level.
What is Materials Informatics? Materials Informatics (MI) uses information science and computational science, including technologies like AI / ML, to improve materials development processes. MI is used to predict and identify novel materials and to optimize existing materials for innovative applications—faster and more reliably.
A new technique that is poised to transform the field is what we have coined the
AI Supermodel. While this technology is not yet mainstream, it promises to transform how R&D is done by enhancing predictive abilities in unprecedented ways.
Enthought recently hosted a live virtual briefing for Japanese companies on this topic, and we encourage you to watch the on-demand version of Next Generation Materials Informatics for more details. The main presentation is given in English, with Japanese subtitles, by US-based Enthought COO, Dr. Michael Connell.
At its core, research and development is about prediction. Predicting the unknown based on the known is essential to scientific discovery, innovation, product development. Predictions in materials science can range from atomic behaviors to large-scale material properties, depending on the goal. The better and faster researchers can predict, the more efficient and successful R&D efforts become.
Prediction in R&D is grounded in three main approaches:
AI Supermodels represent a transformative way of combining the three—intuition, theory, and data-driven statistics—allowing for faster, more accurate predictions and unlocking new potential in R&D with far fewer data points and faster product development times.
To understand AI Supermodels, below are the results of two real-world use cases that illustrate their potential:
Using an AI Supermodel, researchers at Los Alamos National Laboratories (LANL) combined their intuition, theoretical knowledge, and sparse experimental data to streamline the tedious and manual process of quantum sensor tuning. The AI Supermodel rapidly identified optimal parameter settings, achieving twice the performance of traditional methods while using only 1/100th of the data and in 1/1000th of the time. This breakthrough significantly reduced development time, allowing the researchers to focus on optimizing device performance rather than on labor-intensive manual adjustments.
By implementing an AI Supermodel, researchers at LANL also dramatically improved the predictive loop for X-ray Diffraction (XRD) analysis, a process that normally can take days or even months to yield usable results. The model not only performed as well as human experts on routine cases but also solved more complex cases that had previously resisted traditional analysis. Moreover, the AI Supermodel made these predictions in near real-time. It transformed the formerly slow, intuition-based processes into fast, reliable systems that advanced materials discovery, while also paving the way to innovations in materials R&D methods that were not previously possible.
AI algorithms can automate data analysis, literature reviews, and experimental design, significantly reducing the time and effort required for these activities. This enables scientists to dedicate their expertise and creativity to more critical tasks such as hypothesis generation, experimental interpretation, and innovative problem-solving.
As you can see, AI Supermodels represent a paradigm shift in the way predictions can be made in materials science and chemistry R&D. Unlike traditional models, these AI Supermodels can deliver actionable predictions with high precision even when limited empirical data is available.
AI Supermodels when applied to R&D:
AI Supermodels are more than a technological advancement for efficiency gains; they have the potential to fundamentally change how R&D is conducted. As this technology matures, early adopters stand to gain significant opportunities and competitive advantages. In the near future, AI Supermodels will likely become essential tools for R&D leaders, researchers, and product developers.
To connect to an Enthought Materials Informatics expert, please contact us.
For more details on this topic, please watch the on-demand virtual briefing: Next Generation Materials Informatics.
This article references topics presented by Dr. Michael Heiber at Enthought’s 2025 R&D Innovation Summit in Tokyo. Link to video below.Over the last...
This article was originally published on Forbes and can be foundhere. By Michael Connell, EdD | Chief Operating Officer, Enthought Inc. AI is...
The specialty chemicals and materials industry is undergoing a significant shift. For companies that have historically relied on the strength of...