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Information Chemistry Lab
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The lab designs new materials and catalysts using data and artificial intelligence.
AboutAbout the lab
Finding new materials from data.
The Information Chemistry Lab researches the design of materials and catalysts using data science and artificial intelligence.
Until now, materials and catalysts have been designed through researchers' trial and error.
The lab collects experimental data, computational data, and data from published literature to build databases, and connects them to design through machine learning.
AI forms hypotheses, and robots run experiments to test them. The lab aims to automate the laboratory in this way.
Key terms
- Informatics
- An approach and set of methods for collecting and analyzing data in order to discover new things from it.
- Machine learning
- A method in which a computer finds rules in large amounts of data so that it can make predictions.
- Catalyst
- A substance that speeds up a chemical reaction without being changed itself.
ResearchResearch themes
The lab designs materials, catalysts, and chemical reactions by combining experiments, theory, computation, informatics, AI, and robotics. The materials it handles range widely from catalysts to biomaterials, and it also develops original AI models, from machine learning to artificial intelligence.
*Illustrative imageDesigning materials and catalysts with informatics
Until now, materials and catalysts have been designed through researchers' trial and error. The lab builds databases from its own high-throughput experiments and calculations together with data collected from published literature, and uses artificial intelligence, machine learning, and data visualization to design and understand materials and catalysts.
*Illustrative imageThe carbon cycle in space
The current focus is the carbon cycle in space. Using methane, the system captures and stores carbon dioxide, then hydrogenates it with hydrogen made from water to turn it back into methane. This closed carbon loop is a key process for sustainable space systems, such as Mars exploration and long-duration space missions.
*Illustrative imageAutomating experiments with AI and robots
The ultimate goal is laboratory automation. AI learns from experimental data to form hypotheses and design experiments, and robots carry them out to test them. The resulting data are analyzed automatically and fed back to improve the model further.
MembersFaculty
The members of the Information Chemistry Lab are faculty, researchers, visiting professors, and doctoral, master's, and undergraduate students.As of 2025
- ProfessorKeisuke Takahashi
- Assistant ProfessorLauren Takahashi
- Project Assistant ProfessorFernando Garcia-Escobar
VoicesStudent voices
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Industrial automation setup shows robotic arm and equipment for laboratory tasks” Shixart1985 / CC BY 2.0 (cropped)
- Designing materials and catalysts with informatics: “Network graph visualization showing bibliography and keywords” Ecpp / CC BY-SA 4.0 (cropped)
- The carbon cycle in space: “First High-Resolution Color Mosaic of Curiosity's Mastcam Images” NASA/JPL-Caltech/MSSS / Public domain (cropped)
- Automating experiments with AI and robots: “MIREA Laboratory Industry 4.0. Digital robotic manufacturing 6” Нечаева Лилия Александровна / CC BY-SA 4.0 (cropped)
- Related lab (Inorganic Chemistry Lab): “Lithium-Ion Cell cylindric” RudolfSimon / CC BY-SA 3.0 (cropped)
- Related lab (Quantum Chemistry Lab): “Benzene IBO” Alicesu / CC BY-SA 4.0 (cropped)
- Related lab (Organometallic Chemistry Lab): “Double vac line side view” Polimerek / CC BY-SA 3.0 (cropped)
- Related lab (Organic Reaction Theory Lab): “Lab on a Chip ( )” National Institute of Standards and Technology / Public domain (cropped)
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