Adsorption in Pyrene-Based Metal–Organic Frameworks
M. J. Pougin, N. P. Domingues, F. P. Uran, A. Ortega-Guerrero, C. P. Ireland, J. Espín, W. Lee Queen, and B. Smit, Adsorption in Pyrene-Based Metal–Organic Frameworks: The Role of Pore Structure and Topology ACS Appl. Mater. Interfaces (2024) doi: 10.1021/acsami.4c05527 Abstact: Pore topology and chemistry play crucial roles in the adsorption characteristics of metal–organic frameworks (…)
A novel PEG-mediated approach to entrap hemoglobin (Hb) within ZIF-8 nanoparticles
C. Coll-Satue, M. Rubio-Huertas, A. Ducrot, E. Norkute, X. Liu, F. M. Ebrahim, B. Smit, P. W. Thulstrup, and L. Hosta-Rigau, A novel PEG-mediated approach to entrap hemoglobin (Hb) within ZIF-8 nanoparticles: Balancing crystalline structure, Hb content and functionality Biomater. Adv, 213953 (2024) DOI: 10.1016/j.bioadv.2024.213953 Abstract: Hemoglobin (Hb)-based oxygen carriers are investigated as a potential alternative (…)
Deep learning-based recommendation system for MOFs
X. Zhang, K. M. Jablonka, and B. Smit, Deep learning-based recommendation system for metal–organic frameworks (MOFs), Digit Discov 3 (7), 1410 (2024) DOI: 10.1039/D4DD00116H Abstract: This work presents a recommendation system for metal–organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds MOFs (…)
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 (…)
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/s42256-023-00788-1 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
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
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
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
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
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