Materials Science

Enthought | Digital Transformation in Scientific R&D

Digital Transformation in Practice

May 1, 2024

There is much more to digital transformation than technology, and a holistic strategy is crucial for the journey.

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Enthought | LLMs in Materials Science and Chemistry R&D

Utilizing LLMs Today in Industrial Materials and Chemical R&D

Mar 25, 2024

Leveraging large language models (LLMs) in materials science and chemical R&D isn’t just a speculative venture for some AI future. There are two primary use cases that are ready for adoption in research labs today.

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Scientist leveraging AI

Top 10 AI Concepts Every Scientific R&D Leader Should Know

Feb 21, 2024

R&D leaders and scientists need a working understanding of key AI concepts so they can more effectively develop future-forward data strategies and lead the charge towards groundbreaking discoveries.

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Enthought | Why a Data Fabric is Essential in Modern R&D

Why A Data Fabric is Essential for Modern R&D

Nov 14, 2023

Scattered and siloed data is one of the top challenges slowing down scientific discovery and innovation today. What every R&D organization needs is a data fabric as part of their technology solution set.

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Enthought at ACS 2023 Fall Meeting

Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More

Aug 29, 2023

By Mike Heiber, Ph.D., Materials Informatics Manager Enthought, Materials Science Solutions The American Chemical Society (ACS) is a premier scientific organization with members all over the world from both academia and industry. Some of my team and I recently returned from their primary annual convening, the ACS 2023 Fall Meeting, held in San Francisco. I…

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How IT Contributes to Successful Science

Jul 26, 2023

With the increasing importance of AI and machine learning in science and engineering, it is critical that the leadership of R&D and IT groups at innovative companies are aligned. Inappropriate budgeting, policies, or vendor choices can unnecessarily block critical research programs; conversely an “anything goes” approach can squander valuable resources or leave an organization open to novel security threats.

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Enthought | Generative AI in Materials Science and Chemistry

From Data to Discovery: Exploring the Potential of Generative Models in Materials Informatics Solutions

Jun 30, 2023

Generative models can be used in many more areas than just language generation, with one particularly promising area: molecule generation for chemical product development.

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The Importance of Large Language Models in Science Even If You Don’t Work With Language

Jun 11, 2023

OpenAI’s ChatGPT, Google’s Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language. Users can describe what they want done and have the LLM “understand” and respond appropriately. 

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Enthought | Making the Most of Small Data in R&D

Making the Most of Small Data in Scientific R&D

Mar 11, 2023

For many traditional innovation-driven organizations, scientific data is generated to answer specific immediate research questions and then archived to protect IP, with little attention paid to the future value of reusing the data to answer other similar or tangential questions.

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Enthought | Scientific Data

Extracting Value from Scientific Data to Accelerate Discovery and Innovation

Feb 1, 2023

In the digital era, robust data tools are crucial for all companies and the science-driven industries like the life sciences, materials science, and chemistry are no exception.

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