Smit, Berend

Carbon capture and storage (CCS): the way forward

Bui, M., Adjiman, C. S., Bardow, A., Anthony, E. J., Boston, A., Brown, S., Fennell, P. S., Fuss, S., Galindo, A., Hackett, L. A., et al. (2018) · Energy & Environmental Science

Carbon Dioxide Capture TechnologiesCO2 Sequestration and Geologic Interactions

Cited by 4,374

Carbon Dioxide Capture: Prospects for New Materials

D'Alessandro, D. M., Smit, B., & Long, J. R. (2010) · Angewandte Chemie International Edition

Carbon Dioxide Capture TechnologiesMetal-Organic Frameworks: Synthesis and Applications

Cited by 4,046

Data-driven design of metal–organic frameworks for wet flue gas CO2 capture

Boyd, P. G., Chidambaram, A., García-Díez, E., Ireland, C. P., Daff, T. D., Bounds, R., Gładysiak, A., Schouwink, P., Moosavi, S. M., Maroto-Valer, M. M., et al. (2019) · Nature

Metal-Organic Frameworks: Synthesis and ApplicationsCarbon Dioxide Capture Technologies

Cited by 885

Understanding the diversity of the metal-organic framework ecosystem

Moosavi, S. M., Nandy, A., Jablonka, K. M., Ongari, D., Janet, J. P., Boyd, P. G., Lee, Y., Smit, B., & Kulik, H. J. (2020) · Nature Communications

Metal-Organic Frameworks: Synthesis and ApplicationsMachine Learning in Materials Science

Cited by 622

Why Clays Swell

Hensen, E. J. M., & Smit, B. (2002) · The Journal of Physical Chemistry B

Clay minerals and soil interactionsElectrostatics and Colloid Interactions

Cited by 406

Leveraging large language models for predictive chemistry

Jablonka, K. M., Schwaller, P., Ortega-Guerrero, A., & Smit, B. (2024) · Nature Machine Intelligence

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 378

Capturing chemical intuition in synthesis of metal-organic frameworks

Moosavi, S. M., Chidambaram, A., Talirz, L., Haranczyk, M., Stylianou, K. C., & Smit, B. (2019) · Nature Communications

Machine Learning in Materials ScienceMetal-Organic Frameworks: Synthesis and Applications

Cited by 257

14 examples of how LLMs can transform materials science and chemistry: A reflection on a large language model hackathon

Jablonka, K. M., Ai, Q., Al-Feghali, A., Badhwar, S., Bocarsly, J. D., Bran, A. M., Bringuier, S., Brinson, L. C., Choudhary, K., Circi, D., et al. (2023) · Digital Discovery

Machine Learning in Materials ScienceArtificial Intelligence Applications

Cited by 218

In silico Design of Porous Polymer Networks: High-Throughput Screening for Methane Storage Materials

Martin, R. L., Simon, C. M., Smit, B., & Haranczyk, M. (2014) · Journal of the American Chemical Society

Metal-Organic Frameworks: Synthesis and ApplicationsCovalent Organic Framework Applications

Cited by 170

Large-Scale Computational Screening of Zeolites for Ethane/Ethene Separation

Kim, J., Lin, L.-C., Martin, R. L., Swisher, J. A., Haranczyk, M., & Smit, B. (2012) · Langmuir

Zeolite Catalysis and SynthesisMembrane Separation and Gas Transport

Cited by 104

Text Mining Metal–Organic Framework Papers

Park, S., Kim, B., Choi, S., Boyd, P. G., Smit, B., & Kim, J. (2018) · Journal of Chemical Information and Modeling

Machine Learning in Materials ScienceMetal-Organic Frameworks: Synthesis and Applications

Cited by 69

Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet

Witman, M., Gidon, D., Graves, D. B., Smit, B., & Mesbah, A. (2019) · Plasma Sources Science and Technology

Atmospheric-Pressure Plasma ApplicationsReinforcement Learning in Robotics

Cited by 46