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3rd Edition of Understanding Molecular Simulation
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.
Cover of Digital Discovery
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
An ecosystem for reticular chemistry generated using artificial intelligence.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.2c01177acscentsci.2c01177Download
Latest publications
Inverse design of metal-organic frameworks for direct air capture of CO2
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 gas storage (…)
Leveraging large language models for predictive chemistry
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/s42256-023-00788-1Abstract: 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 incorporate (…)
Predicting Ion Diffusion from the Shape of Potential Energy Landscapes
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.3c01005Abstract: 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 the (…)