Author: Berend Smit

Inverse design of metal-organic frameworks for direct air capture of CO2

Recent Publications

H. Park, S. Majumdar, X. Zhang, J. Kim, and B. Smit, Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning Digit Discov  (2024) doi: 10.1039/D4DD00010B   Abstract: The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like (…)

Leveraging large language models for predictive chemistry

Recent Publications

K. M. Jablonka, P. Schwaller, A. Ortega-Guerrero, and B. Smit, Leveraging large language models for predictive chemistry Nat Mach Intel  (2024) doi: 10.1038/s42256-023-00788-1 Abstract:  Machine learning has transformed many fields and has recently found applications in chemistry and materials science. The small datasets commonly found in chemistry sparked the development of sophisticated machine learning approaches that (…)

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Recent Publications

H. Gustafsson, M. Kozdra, B. Smit, S. Barthel, and A. Mace, Predicting Ion Diffusion from the Shape of Potential Energy Landscapes J. Chem. Theory Comput. (2023) DOI: 10.1021/acs.jctc.3c01005 Abstract: We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of (…)

Examples of How LLMs Can Transform Materials Science and Chemistry

Recent Publications

K. M. Jablonka, Q. Ai, A. Al-Feghali, S. Badhwar, J. D. Bocarsly, A. M. Bran, S. Bringuier, L. C. Brinson, K. Choudhary, D. Circi, S. Cox, W. de Jong, M. Evans, N. Gastellu, J. Genzling, M. V. Gil, A. Gupta, Z. Hong, A. Imran, S. Kruschwitz, A. Labarre, J. Lála, T. Liu, S. Ma, S. (…)

3rd Edition of Understanding Molecular Simulation

News

D. Frenkel and B. Smit, Understanding Molecular Simulations: from Algorithms to Applications, 3rd ed. (Academic Press, San Diego, 2023) A copy can be obtained from the Elsevier website https://shop.elsevier.com/books/understanding-molecular-simulation/frenkel/9780323902922, and with the promo code “CHEM30” one can get a 30% reduction.   

3rd Edition of Understanding Molecular Simulation

Recent Publications

D. Frenkel and B. Smit, Understanding Molecular Simulations: from Algorithms to Applications, 3rd ed. (Academic Press, San Diego, 2023) doi: 10.1016/C2009-0-63921-0 Understanding Molecular Simulation explains molecular simulation from a chemical-physics and statistical-mechanics perspective. It highlights how physical concepts are used to develop better algorithms and expand the range of applicability of simulations. Understanding Molecular Simulation is (…)

Cover of Digital Discovery

News

M. V. Gil, K. M. Jablonka, S. García, C. Pevida, and B. Smit, Biomass to energy: A machine learning model for optimum gasification pathways Digital Discovery 2, 929 (2023) http://dx.doi.org/10.1039/D3DD00079F

Cover of ACS Central Science

News

K. M. Jablonka, A. S. Rosen, A. S. Krishnapriyan, and B. Smit, An Ecosystem for Digital Reticular Chemistry ACS Cent Sci 9 (4), 563 (2023) http://dx.doi.org/10.1021/acscentsci.2c01177

Biomass to energy: a machine learning model for optimum gasification pathways

Recent Publications

M. V. Gil, K. M. Jablonka, S. Garcia, C. Pevida, and B. Smit, Biomass to energy: a machine learning model for optimum gasification pathways Digital Discovery  (2023) doi: 10.1039/D3DD00079F Abstract: Biomass is a highly versatile renewable resource for decarbonizing energy systems. Gasification is a promising conversion technology that can transform biomass into multiple energy carriers (…)

Generating Adsorption Isotherms to Screen Materials for Carbon Capture

Recent Publications

E. Moubarak, S. M. Moosavi, C. Charalambous, S. Garcia, and B. Smit, A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture Ind. Eng. Chem. Res.  (2023) doi: 10.1021/acs.iecr.3c01358 Abstract: To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict (…)