Chris has more than 20 years of experience in materials science, chemistry, and software development, and has been instrumental in creating and advancing Enthought’s materials science platform. In this interview, Chris shares his insights on the latest trends in materials science, the role of machine learning and AI in materials data analytics and the benefits of a unified data platform for materials innovation.

Please brief our audience about Enthought and give us an overview of its standout solutions.

Enthought is a globally recognized leader in scientific computing, providing specialized solutions that accelerate scientific innovation across various industries. We partner with science-driven companies in the electronic, semiconductor, materials design, manufacturing, pharmaceutical, biotechnology, energy, and consumer goods industries.

Our transformative solutions, from AI-assisted interpretations of subsurface seismic data to quantum simulations for material informatics and ML models for cancer therapeutics, have helped businesses achieve breakthrough discoveries in record time. Scientists all over the world also use Edge, our cloud-native platform that serves as a central hub for all their R&D data, analysis, and application needs. We also have programs, like our Materials Informatics Acceleration Program, that upskill scientists with the new digital skills needed to leverage technology to make new discoveries.

What are the core values on which Enthought is formed and what is the mission of the organization?

Enthought’s mission is to help companies fully realize their business objectives and gain competitive advantages by digitally transforming their R&D organizations, from idea generation to custom software to empowering teams. Ultimately we aim to help companies answer the question, “What could be accomplished if your scientists could spend 100% of their time advancing their discoveries?” We have a deep understanding of the complexities of science-driven processes and scientific data as well as advanced computing techniques.

We also bring a unique data-centric approach that encourages R&D leaders to think differently about how they conduct science and expand what’s possible in the lab. This approach and understanding allows us to conceptualize and deliver solutions in a way other firms cannot.

Being a thought leader, how do you strategize to bring to light Enthought’s mission and vision?

First and foremost, we want to make an impact with our work. Given the historical hype around AI and ML, this is critical. You can only go so long building models and demonstrations before someone asks what it’s good for. Additionally, we’re driven to empower scientists. 90% of our global technical team have advanced STEM degrees, with 65% holding Ph.D.’s. So we understand scientists’ challenges, their goals, and the pressures they face. Our solutions are developed by scientists for scientists, while focused on what brings value to the enterprise.

As far as markets, we partner with companies of all sizes and stages, from Fortune 500 to startups, in science and innovation-driven industries like materials science, pharma, and chemistry. We build custom solutions built around their niche so they can do more, accomplish bigger things. I mentioned batteries earlier. The battery industry is highly scientific, experiencing tremendous development and commercial activity, and needs digitalization. We also help companies leverage new technologies like material informatics (MI), which is poised to change how materials and chemicals R&D is performed.

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