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Discovery of tunable and soluble organic emitters for solid-state lasers with a self-driving laboratory

Organic lasers, known for their colour purity, are emerging as next-generation light emitters. Here, the authors use a self-driving laboratory to discover soluble red-shifted emitters and automate the search for solution-processable laser dyes.

Hyun Suk Park
Mahdi Mazaheri
Alán Aspuru-Guzik
ResearchOpen Access19 Feb 2026
Nature Communications

Volume: 17, P: 1-13
Understanding potential-dependent competition between electrocatalytic dinitrogen and proton reduction reactions

Practical electrochemical N₂ reduction reaction is challenged by competing side reactions. Here a combination of DFT and mikrokinetic modelling reveals the potential-dependent competition between electrochemical ammonia production and hydrogen evolution on a single-site iron catalyst embedded in N-doped graphene.

Changhyeok Choi
Geun Ho Gu
Yousung Jung
ResearchOpen Access16 Jul 2021
Nature Communications

Volume: 12, P: 1-11
Developing machine learning for heterogeneous catalysis with experimental and computational data

Machine learning aids heterogeneous catalysis research by linking performance to physicochemical controllable properties. This Review discusses experimental and computational high-throughput and machine learning approaches, comparing them by modelling method, features, dataset size, accuracy and reaction type.

Carlota Bozal-Ginesta
Sergio Pablo-García
Alán Aspuru-Guzik
Reviews18 Jul 2025
Nature Reviews Chemistry

Volume: 9, P: 601-616

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Discovery of tunable and soluble organic emitters for solid-state lasers with a self-driving laboratory

Understanding potential-dependent competition between electrocatalytic dinitrogen and proton reduction reactions

Developing machine learning for heterogeneous catalysis with experimental and computational data

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