We believe that new materials will unlock the most important growth frontier of the 21st century and are critical to addressing climate change, the energy transition, and the future of computing.
We bring deep domain expertise from a range of scientific and business backgrounds, and we live at the intersection of materials science, applied AI, software engineering, and data products.
We created Atomic Data Sciences in order to close the gap between in-silico and in-vivo advanced materials by elevating real-world materials characterization data. Our goal is to reduce the decades-long lead times currently needed to bring new advanced materials to market.
We're materials scientists, data scientists, and technologists brought together by a shared vision to boost real-world advanced materials development using the toolsets of modern computing.
Founder & CEO
— Materials Science, ML
Dartmouth, University of Pennsylvania
Chris is a computational materials scientist with experience applying multiscale models, AI/ML, and high-performance computing to research problems across condensed matter physics, materials science, chemistry, and biophysics.
Chris founded Atomic Data Sciences to bridge the gap in using high-value materials characterization data to develop advanced materials based on his personal experience working with the limitations of simulated datasets and experimental collaborators. He draws inspiration for productizing datasets from his prior experience developing data analytics products for electricity markets.
CTO
— Materials Science, ML
Penn State, UC Berkeley/Lawrence Berkeley National Laboratory
Jason is a computational materials scientist with experience applying quantum-based modeling, AI/ML, and high-performance computing to research problems across condensed matter physics, materials science, and chemistry.
Jason brings expertise from his work as a scientist and software engineer at Lawrence Berkeley National Laboratory. There he helped lead the development of the Materials Project - the world's most popular open-source platform for computed data and software tools related to advanced materials analysis.
Product Strategy, Business Development
Dartmouth
Tim brings broad experience from strategic business development, technical market due diligence, market research, and GTM strategy for B2B products.
Tim previously worked as a Principal with Sterling Associates where he led market diligence for the take-private of large InfoSec companies and market analysis efforts, and as a strategic consultant advising businesses across healthcare, architecture, and apparel.
Product Engineering, Design
University of Toronto
Spencer brings experience from full-stack engineering at large enterprise tech companies and early startups, and experimental life sciences research in cancer immunology.
Spencer previously worked as a founding engineer at Otto Intelligence where he led UI/UX prototyping and product development, and as a software engineer at Trulioo where he developed backend data source integrations.
Scientific Advisor
— Materials Science, ML
University of Missouri, University of Pennsylvania, MIT
Nathan brings experience developing and productizing machine learning models applied to materials science and biology in the context of industry and scientific research.
Nathan is a machine learning scientist at Prescient Design · Genentech where he applies machine learning to accelerate the design of materials and biomolecules, and previously did academic research on these topics at the Lincoln Laboratory at MIT.