Category: Recent 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 (…)
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) 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 (…)
Biomass to energy: a machine learning model for optimum gasification pathways
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
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 (…)
COFs for Photocatalysts
B. Mourino, K. M. Jablonka, A. Ortega-Guerrero, and B. Smit, In Search of Covalent Organic Framework Photocatalysts: A DFT-Based Screening Approach Adv. Funct. Mater. (2023) doi: 10.1002/adfm.202301594 Abstract Covalent organic frameworks (COFs) stand out as prospective organic-based photocatalysts given their intriguing optoelectronic properties, such as visible light absorption and high charge-carrier mobility. The “Clean, Uniform, (…)
Toward Superior Hydroisomerization Catalysts through Thermodynamic Optimization
J. E. Schmidt, B. Smit, C.-Y. Chen, D. Xie, and T. L. M. Maesen, Toward Superior Hydroisomerization Catalysts through Thermodynamic Optimization ACS Catal., 6710 (2023) doi: 10.1021/acscatal.3c00391 Abstract: The need to reduce the lifecycle greenhouse gas emissions of fuels and lubricants has renewed interest in hydroisomerization processes. Here it is shown how recognizing the signature of (…)
An Ecosystem for Digital Reticular Chemistry
K. M. Jablonka, A. S. Rosen, A. S. Krishnapriyan, and B. Smit, An Ecosystem for Digital Reticular Chemistry ACS Cent. Sci. (2023) doi: 10.1021/acscentsci.2c01177 Abstract: The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding (…)