materials-informatics
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Alex Wang Is Taking AI Out of the Demo and Into the Lab
After two decades helping enterprise technologies find their market, NobleAI's CEO is betting that scientific AI wins only when chemists and engineers can understand it, check it and use it in the flow of real work.
Ares Materials, Inc.
Ares Materials is a Plano, Texas advanced-materials company building AI-designed optical polymers for the display industry. Spun out of the University of Texas at Dallas in 2016, it uses thiol-click chemistry and a machine-learning platform called Pylux to formulate solvent-free, PFAS-free films, adhesives, coatings and cover windows for foldable phones, AR/VR devices and other flexible electronics - aiming for high optical clarity, mechanical durability and up to 90% lower CO2 emissions than incumbent materials.
David Arreaga
David Arreaga is the co-founder and CEO of Ares Materials, a Plano, Texas deep-tech company building AI-designed optical polymers for flexible and foldable electronics. A materials scientist with a Ph.D. from UT Dallas, he turned doctoral research on polymer substrates into a company whose Pylux cover-window and adhesive materials target the next generation of foldable displays. Ares pairs high-throughput experimentation with machine learning to shorten materials development from decades to years, and closed the first trench of a $12M Series B round in 2025.
NobleAI
NobleAI is a San Francisco software company that builds 'Science-Based AI' for chemical, materials and energy R&D. Its cloud VIP platform (Visualizations, Insights and Predictions) bakes physical laws and chemical properties into machine-learning models so they can make useful predictions from the sparse, messy datasets that industrial labs actually have - compressing R&D cycles from months to minutes. Founded in 2017 by physicist Matthew C. Levy, the company has raised roughly $40M from backers including Microsoft's M12, Chevron Technology Ventures, Sway Ventures and Dorilton, and counts chemical giants such as Solvay among its early partners.
Quantum Boost
Quantum Boost is a London-based deep-tech startup building an AI formulation assistant for the chemicals and materials industry. Its platform uses Bayesian optimization and machine learning to help R&D scientists design smarter experiments, cut the number of lab trials needed to hit a target, and surface insights from existing experimental data. The company claims its approach reaches formulation targets roughly 2-5x faster than traditional Design of Experiments (DoE), and serves teams working on inks, paints and coatings, pharmaceuticals, and specialty chemicals.