Materials Science

3 Trends for Scientists To Watch in 2023

Dec 19, 2022

As a company that delivers Digital Transformation for Science, part of our job at Enthought is to understand the trends that will affect how our clients do their science. Below are three trends that caught our attention in 2022 that we predict will take center stage in 2023. ChatGPT This one just showed up on…

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Enthought | Configuring a Neural Network

Configuring a Neural Network Output Layer

May 18, 2023

Introduction If you have used TensorFlow before, you know how easy it is to create a simple neural network model using the Keras API. Just create an instance of the Sequential model class, add the number of desired layers and accompanying layer nodes, define the activation functions to be used by each layer, and compile…

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Enthought | Digital Transformation of the Materials Science R&D Lab

Digital Transformation of the Materials Science R&D Lab

Mar 31, 2022

“Digital transformation”, “machine learning”, and “artificial intelligence” are buzzwords heard in every industry, from the boardroom to the lab. We asked Dr. Michael Heiber, lead of Enthought’s Materials Informatics Acceleration Program, about what these technology trends mean for the future of materials and chemical labs and product development. Q: What are some of the top…

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Takeaways from SEMICON West 2021

Jan 13, 2022

SEMICON West 2021 lived up to its status as the signature conference for the extended microelectronics supply chain. Business and technology leaders, researchers, and analysts from across the semiconductor industry connected in-person and virtually for a 360 view of technological and market trends. Enthought Takeaways Authors: Michael Heiber, Application Engineer, Materials Science Solutions Group; Tim…

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Enthought’s Takeaways from SEMI SMC 2021

Nov 14, 2021

At this year’s SEMI Strategic Materials Conference, leaders in the semiconductor industry across the supply chain came together to discuss the big challenges and opportunities that are likely to emerge over the next 5 years.  Our Takeaways Authors: Michael Heiber, Application Engineer, Materials Science Solutions Group, Tim Diller, Director of Digital Transformation Services, Materials Science…

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Webinar Q&A: Accelerating Product Reformulation with Machine Learning

Oct 28, 2021

In our recent C&EN Webinar: Accelerating Consumer Products Reformulation with Machine Learning, we demonstrated how to leverage digital tools and technology to bring new products to market faster. The webinar was well attended by scientists, engineers, and business leaders across the product development spectrum eager to learn how these concepts can be applied to their…

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Machine Learning in Materials Science

Aug 10, 2021

The process of materials discovery is complex and iterative, requiring a level of expertise to be done effectively. Materials workflows that require human judgement present a specific challenge to the discovery process, which can be leveraged as an opportunity to introduce digital technologies.  In the lab, many tasks require manual data collection and judgment. And…

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Digital-centric R&D Laboratories

Feb 16, 2021

To have a transformative impact, labs must reinvent workflows through digital technologies and skills, adopting a strong data culture. Innovation through digital-centric systems confidently produces new materials that meet customer specifications orders of magnitude faster than before, enabling broader business transformation.  Authors: Chris Farrow, Ph.D., VP Materials Science Solutions and Michael Heiber, Manager, Materials Informatics…

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Up the ‘Digital Level’ of Your R+D Lab

Dec 8, 2020

A key role of materials and chemistry R&D researchers is to invert the primary function of their labs – that of creating materials from chemical structures, formulations and processes – to one of determining the inputs that will produce materials with the desired properties with minimal iteration. This process can be significantly accelerated by ‘leveling…

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Enthought at the 2020 Materials Research Society Conference

Nov 24, 2020

Machine learning classification model learns complex printability window for inkjet printed polymer films using data from automated formulation and printing system. Authors: Michael Heiber, Ph.D., Manager, Materials Informatics and Frank Longford, Ph.D., Scientific Software Developer The Materials Research Society (MRS) is a global community of materials researchers, built to promote the advancement of interdisciplinary materials…

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