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146 results on '"Colón, Yamil J."'

1. Considerations in the use of ML interaction potentials for free energy calculations

Author

Mendible, Orlando A., Whitmer, Jonathan K., and Colón, Yamil J.

Subjects
Physics - Chemical Physics, Condensed Matter - Materials Science, Computer Science - Machine Learning
Abstract

Machine learning potentials (MLPs) offer the potential to accurately model the energy and free energy landscapes of molecules with the precision of quantum mechanics and an efficiency similar to classical simulations. This research focuses on using equivariant graph neural networks MLPs due to their proven effectiveness in modeling equilibrium molecular trajectories. A key issue addressed is the capability of MLPs to accurately predict free energies and transition states by considering both the energy and the diversity of molecular configurations. We examined how the distribution of collective variables (CVs) in the training data affects MLP accuracy in determining the free energy surface (FES) of systems, using Metadynamics simulations for butane and alanine dipeptide (ADP). The study involved training forty-three MLPs, half based on classical molecular dynamics data and the rest on ab initio computed energies. The MLPs were trained using different distributions that aim to replicate hypothetical scenarios of sampled CVs obtained if the underlying FES of the system was unknown. Findings for butane revealed that training data coverage of key FES regions ensures model accuracy regardless of CV distribution. However, missing significant FES regions led to correct potential energy predictions but failed free energy reconstruction. For ADP, models trained on classical dynamics data were notably less accurate, while ab initio-based MLPs predicted potential energy well but faltered on free energy predictions. These results emphasize the challenge of assembling an all-encompassing training set for accurate FES prediction and highlight the importance of understanding the FES in preparing training data. The study points out the limitations of MLPs in free energy calculations, stressing the need for comprehensive data that encompasses the system's full FES for effective model training.

Published
2024

2. First-Principles Screening of Metal-Organic Frameworks for Entangled Photon Pair Generation

Author

Raj, Sanoj, Paiva, Simón, Fritz, Rubén, Herrera, Felipe, and Colón, Yamil J.

Subjects
Quantum Physics, Physics - Optics
Abstract

The transmission of strong laser light in nonlinear optical materials can generate output photons sources that carry quantum entanglement in multiple degrees of freedom, making this process a fundamentally important tool in optical quantum technology. However, the availability of efficient optical crystals for entangled light generation is severely limited in terms of diversity, thus reducing the prospects for the implementation of next-generation protocols in quantum sensing, communication and computing. To overcome this, we developed and implemented a multi-scale first-principles modeling technique for the computational discovery of novel nonlinear optical devices based on metal-organic framework (MOF) materials that can efficiently generate entangled light via spontaneous parametric down-conversion(SPDC). Using collinear degenerate type-I SPDC as a case study, we computationally screen a database of 114,373 synthesized MOF materials to establish correlations between the structure and chemical composition of MOFs with the brightness and coherence properties of entangled photon pairs. We identify a subset of 49 non-centrosymmetric mono-ligand MOF crystals with high chemical and optical stability that produce entangled photon pairs with intrinsic $G^{(2)}$ correlation times $\tau_c\sim 10-30$ fs and pair generation rates in the range $10^4-10^{8}$ s$^{-1}$mW$^{-1}$mm$^{-1}$ at 1064 nm. Conditions for optimal type-I phase matching are given for each MOF and relationships between pair brightness, crystal band gap and optical birefringence are discussed. Correlations between the optical properties of crystals and their constituent molecular ligands are also given. Our work paves the way for the computational design of MOF-based devices for optical quantum technology., Comment: Supplementary Material (16 pages, 14 figures, 2 table) at the end of manuscript. Github (https://github.com/snoozynooj/Type-I-SPDC)

Published
2023

3. Semi-Empirical Haken-Strobl Model for Molecular Spin Qubits

Author

Aruachan, Katy, Colón, Yamil J., Aravena, Daniel, and Herrera, Felipe

Subjects
Quantum Physics, Physics - Chemical Physics
Abstract

Understanding the physical processes that determine the relaxation $T_{1}$ and dephasing $T_2$ times of molecular spin qubits is critical for envisioned applications in quantum metrology and information processing. Recent spin-echo $T_1$ measurements of solid-state molecular spin qubits have stimulated the development of quantum mechanical models for predicting intrinsic spin qubit timescales using first-principles electronic structure methods. We develop an alternative semi-empirical approach to construct Redfield quantum master equations for molecular spin qubits using a stochastic Haken-Strobl model for a central spin with a fluctuating gyromagnetic tensor due to spin-lattice interaction and a fluctuating local magnetic field due to interactions with other lattice spins. Using a vanadium-based spin qubit as a case study, we compute qubit population and decoherence timescales as a function of temperature and magnetic field using a bath spectral density parametrized with a small number of $T_{1}$ measurements. The theory quantitatively agrees with experimental data over a range of conditions beyond those used to parametrize the model, demonstrating the generalization potential of the method. The ability of the model to describe the temperature dependence of the ratio $T_2/T_1$ is discussed and possible applications for designing novel molecule-based quantum magnetometers are suggested., Comment: 7 pages, 5 figures

Published
2023
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4. Transfer Learning Facilitates the Prediction of Polymer-Surface Adhesion Strength

Author

Shi, Jiale, Albreiki, Fahed, Colón, Yamil J., Srivastava, Samanvaya, and Whitmer, Jonathan K.

Subjects
Condensed Matter - Soft Condensed Matter
Abstract

Machine learning (ML) accelerates the exploration of material properties and their links to the structure of the underlying molecules. In previous work [J. Shi, M. J. Quevillon, P. H. A. Valen\c{c}a, and J. K. Whitmer, \textit{ACS Appl. Mater. Interfaces.}, 2022, 14, 32, 37161--37169], ML models were applied to predict the adhesive free energy of polymer--surface interactions with high accuracy from the knowledge of the sequence data, demonstrating successes in inverse-design of polymer sequence for known surface compositions. While the method was shown to be successful in designing polymers for a known surface, extensive datasets were needed for each specific surface in order to train the surrogate models. Ideally, one should be able to infer information about similar surfaces without having to regenerate a full complement of adhesion data for each new case. In the current work, we demonstrate a transfer learning (TL) technique using a deep neural network to improve the accuracy of ML models trained on small datasets by pre-training on a larger database from a related system and fine-tuning the weights of all layers with a small amount of additional data. The shared knowledge from the pre-trained model facilitates the prediction accuracy significantly on small datasets. We also explore the limits of database size on accuracy and the optimal tuning of network architecture and parameters for our learning tasks. While applied to a relatively simple coarse-grained (CG) polymer model, the general lessons of this study apply to detailed modeling studies and the broader problems of inverse materials design.

Published
2023

5. Model reduction for molecular diffusion in nanoporous media

Author

González, Gastón, Fritz, Rubén A., Colón, Yamil J., and Herrera, Felipe

Subjects
Physics - Chemical Physics, Condensed Matter - Materials Science
Abstract

Porous materials are widely used for applications in gas storage and separation. The diffusive properties of a variety of gases in porous media can be modeled using molecular dynamics simulations that can be computationally demanding depending on the pore geometry, complexity and amount of gas adsorbed. We explore a dimensionality reduction approach for estimating the self-diffusion coefficient of gases in simple pores using Langevin dynamics, such that the three-dimensional (3D) atomistic interactions that determine the diffusion properties of realistic systems can be reduced to an effective one-dimensional (1D) diffusion problem along the pore axis. We demonstrate the approach by modeling the transport of nitrogen molecules in single-walled carbon nanotubes of different radii, showing that 1D Langevin models can be parametrized with a few single-particle 3D atomistic simulations. The reduced 1D model predicts accurate diffusion coefficients over a broad range of temperatures and gas densities. Our work paves the way for studying the diffusion process of more general porous materials as zeolites or metal-organics frameworks with effective models of reduced complexity., Comment: 8 pages, 6 figures

Published
2022
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6. Machine learning identification of organic compounds using visible light

Author

Bikku, Thulasi, Fritz, Rubén A., Colón, Yamil J., and Herrera, Felipe

Subjects
Computer Science - Machine Learning, Physics - Chemical Physics, Physics - Optics
Abstract

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols or applications., Comment: 18 pages, 7 figures. Open database and python code. Version adds comparison with Raman classifiers (Table 1)

Published
2022
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7. Consistent and reproducible computation of the glass transition temperature from molecular dynamics simulations.

Author

Carmona Esteva, Fernando J., Zhang, Yong, Maginn, Edward J., and Colón, Yamil J.

Subjects
GLASS transition temperature, MOLECULAR dynamics, SUPERCONDUCTING transition temperature
Abstract

In many fields, from semiconductors for opto-electronic applications to ionic liquids (ILs) for separations, the glass transition temperature (Tg) of a material is a useful gauge for its potential use in practical settings. As a result, there is a great deal of interest in predicting Tg using molecular simulations. However, the uncertainty and variation in the trend shift method, a common approach in simulations to predict Tg, can be high. This is due to the need for human intervention in defining a fitting range for linear fits of density with temperature assumed for the liquid and glass phases across the simulated cooling. The definition of such fitting ranges then defines the estimate for the Tg as the intersection of linear fits. We eliminate this need for human intervention by leveraging the Shapiro–Wilk normality test and proposing an algorithm to define the fitting ranges and, consequently, Tg. Through this integration, we incorporate into our automated methodology that residuals must be normally distributed around zero for any fit, a requirement that must be met for any regression problem. Consequently, fitting ranges for realizing linear fits for each phase are statistically defined rather than visually inferred, obtaining an estimate for Tg without any human intervention. The method is also capable of finding multiple linear regimes across density vs temperature curves. We compare the predictions of our proposed method across multiple IL and semiconductor molecular dynamics simulation results from the literature and compare other proposed methods for automatically detecting Tg from density–temperature data. We believe that our proposed method would allow for more consistent predictions of Tg. We make this methodology available and open source through GitHub. [ABSTRACT FROM AUTHOR]

Published
2024
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10. Atomistic simulation of soft porous coordination polymers.

Author

Carpenter, James E. and Colón, Yamil J.

Subjects
*PORE size distribution, *MOLECULAR dynamics, *GAS absorption & adsorption, *POROSITY, *POROUS materials, *POROUS polymers
Abstract

Soft porous coordination polymers (SPCPs) are materials with exceptional potential because of their ability to incorporate the properties of nominally rigid porous materials like metal–organic frameworks (MOFs) and those of soft matter, such as polymers of intrinsic microporosity (PIMs). This combination could offer the gas adsorption properties of MOFs together with the mechanical stability and processability of PIMs, opening up a space of flexible, highly responsive adsorbing materials. In order to understand their structure and behavior, we present a process for the construction of amorphous SPCPs from secondary building blocks. We then use classical molecular dynamics simulations to characterize the resulting structures based on branch functionalities (f), pore size distributions (PSDs), and radial distribution functions and compare them to experimentally synthesized analogs. In the course of this comparison, we demonstrate that the pore structure of SPCPs is due to both pores intrinsic to the secondary building blocks, and intercolloid spacing between colloid particles. We also illustrate the differences in nanoscale structure based on linker length and flexibility, particularly in the PSDs, finding that stiff linkers tend to produce SPCPs with larger maximum pore sizes. [ABSTRACT FROM AUTHOR]

Published
2023
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11. Stochastic machine learning via sigma profiles to build a digital chemical space.

Author

Abranches, Dinis O., Maginn, Edward J., and Colón, Yamil J.

Subjects
MACHINE learning, STOCHASTIC learning models, DIGITAL technology, GAUSSIAN processes, NAVIGATION (Astronautics)
Abstract

This work establishes a different paradigm on digital molecular spaces and their efficient navigation by exploiting sigma profiles. To do so, the remarkable capability of Gaussian processes (GPs), a type of stochastic machine learning model, to correlate and predict physicochemical properties from sigma profiles is demonstrated, outperforming state-of-the-art neural networks previously published. The amount of chemical information encoded in sigma profiles eases the learning burden of machine learning models, permitting the training of GPs on small datasets which, due to their negligible computational cost and ease of implementation, are ideal models to be combined with optimization tools such as gradient search or Bayesian optimization (BO). Gradient search is used to efficiently navigate the sigma profile digital space, quickly converging to local extrema of target physicochemical properties. While this requires the availability of pretrained GP models on existing datasets, such limitations are eliminated with the implementation of BO, which can find global extrema with a limited number of iterations. A remarkable example of this is that of BO toward boiling temperature optimization. Holding no knowledge of chemistry except for the sigma profile and boiling temperature of carbon monoxide (the worst possible initial guess), BO finds the global maximum of the available boiling temperature dataset (over 1,000 molecules encompassing more than 40 families of organic and inorganic compounds) in just 15 iterations (i.e., 15 property measurements), cementing sigma profiles as a powerful digital chemical space for molecular optimization and discovery, particularly when little to no experimental data is initially available. [ABSTRACT FROM AUTHOR]

Published
2024
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24. How Reproducible are Surface Areas Calculated from the BET Equation? (Adv. Mater. 27/2022)

Author

Osterrieth, Johannes W. M., primary, Rampersad, James, additional, Madden, David, additional, Rampal, Nakul, additional, Skoric, Luka, additional, Connolly, Bethany, additional, Allendorf, Mark D., additional, Stavila, Vitalie, additional, Snider, Jonathan L., additional, Ameloot, Rob, additional, Marreiros, João, additional, Ania, Conchi, additional, Azevedo, Diana, additional, Vilarrasa‐Garcia, Enrique, additional, Santos, Bianca F., additional, Bu, Xian‐He, additional, Chang, Ze, additional, Bunzen, Hana, additional, Champness, Neil R., additional, Griffin, Sarah L., additional, Chen, Banglin, additional, Lin, Rui‐Biao, additional, Coasne, Benoit, additional, Cohen, Seth, additional, Moreton, Jessica C., additional, Colón, Yamil J., additional, Chen, Linjiang, additional, Clowes, Rob, additional, Coudert, François‐Xavier, additional, Cui, Yong, additional, Hou, Bang, additional, D'Alessandro, Deanna M., additional, Doheny, Patrick W., additional, Dincă, Mircea, additional, Sun, Chenyue, additional, Doonan, Christian, additional, Huxley, Michael Thomas, additional, Evans, Jack D., additional, Falcaro, Paolo, additional, Ricco, Raffaele, additional, Farha, Omar, additional, Idrees, Karam B., additional, Islamoglu, Timur, additional, Feng, Pingyun, additional, Yang, Huajun, additional, Forgan, Ross S., additional, Bara, Dominic, additional, Furukawa, Shuhei, additional, Sanchez, Eli, additional, Gascon, Jorge, additional, Telalović, Selvedin, additional, Ghosh, Sujit K., additional, Mukherjee, Soumya, additional, Hill, Matthew R., additional, Sadiq, Muhammed Munir, additional, Horcajada, Patricia, additional, Salcedo‐Abraira, Pablo, additional, Kaneko, Katsumi, additional, Kukobat, Radovan, additional, Kenvin, Jeff, additional, Keskin, Seda, additional, Kitagawa, Susumu, additional, Otake, Ken‐ichi, additional, Lively, Ryan P., additional, DeWitt, Stephen J. A., additional, Llewellyn, Phillip, additional, Lotsch, Bettina V., additional, Emmerling, Sebastian T., additional, Pütz, Alexander M., additional, Martí‐Gastaldo, Carlos, additional, Padial, Natalia M., additional, García‐Martínez, Javier, additional, Linares, Noemi, additional, Maspoch, Daniel, additional, Suárez del Pino, Jose A., additional, Moghadam, Peyman, additional, Oktavian, Rama, additional, Morris, Russel E., additional, Wheatley, Paul S., additional, Navarro, Jorge, additional, Petit, Camille, additional, Danaci, David, additional, Rosseinsky, Matthew J., additional, Katsoulidis, Alexandros P., additional, Schröder, Martin, additional, Han, Xue, additional, Yang, Sihai, additional, Serre, Christian, additional, Mouchaham, Georges, additional, Sholl, David S., additional, Thyagarajan, Raghuram, additional, Siderius, Daniel, additional, Snurr, Randall Q., additional, Goncalves, Rebecca B., additional, Telfer, Shane, additional, Lee, Seok J., additional, Ting, Valeska P., additional, Rowlandson, Jemma L., additional, Uemura, Takashi, additional, Iiyuka, Tomoya, additional, van der Veen, Monique A., additional, Rega, Davide, additional, Van Speybroeck, Veronique, additional, Rogge, Sven M. J., additional, Lamaire, Aran, additional, Walton, Krista S., additional, Bingel, Lukas W., additional, Wuttke, Stefan, additional, Andreo, Jacopo, additional, Yaghi, Omar, additional, Zhang, Bing, additional, Yavuz, Cafer T., additional, Nguyen, Thien S., additional, Zamora, Felix, additional, Montoro, Carmen, additional, Zhou, Hongcai, additional, Kirchon, Angelo, additional, and Fairen‐Jimenez, David, additional

Published
2022
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27. How Reproducible are Surface Areas Calculated from the BET Equation?

Author

Universidad de Alicante. Departamento de Química Inorgánica, Osterrieth, Johannes W. M., Rampersad, James, Madden, David G., Rampal, Nakul, Skoric, Luka, Connolly, Bethany M., Allendorf, Mark D., Stavila, Vitalie, Snider, Jonathan L., Ameloot, Rob, Marreiros, João, Coudert, François-Xavier, Cui, Yong, Hou, Bang, D'Alessandro, Deanna M., Doheny, Patrick W., Dincă, Mircea, Sun, Chenyue, Doonan, Christian, Huxley, Michael Thomas, Evans, Jack D., Bara, Dominic, Falcaro, Paolo, Ricco, Raffaele, Farha, Omar, Idrees, Karam B., Islamoglu, Timur, Feng, Pingyun, Yang, Huajun, Forgan, Ross S., Furukawa, Shuhei, Sanchez, Eli, Gascon, Jorge, Telalović, Selvedin, Ghosh, Sujit K., Mukherjee, Soumya, Hill, Matthew R., Sadiq, Muhammed Munir, Horcajada, Patricia, DeWitt, Stephen J. A., Salcedo-Abraira, Pablo, Kaneko, Katsumi, Kukobat, Radovan, Kenvin, Jeff, Keskin, Seda, Kitagawa, Susumu, Otake, Ken-ichi, Lively, Ryan P., Llewellyn, Phillip L., Lotsch, Bettina V., Emmerling, Sebastian T., Pütz, Alexander M., Martí-Gastaldo, Carlos, Padial, Natalia M., Garcia-Martinez, Javier, Linares, Noemi, Maspoch, Daniel, Rosseinsky, Matthew J., Suárez del Pino, Jose A., Moghadam, Peyman Z., Oktavian, Rama, Morris, Russel E., Wheatley, Paul S., Navarro, Jorge, Petit, Camille, Danaci, David, Katsoulidis, Alexandros P., Schröder, Martin, Han, Xue, Yang, Sihai, Serre, Christian, Mouchaham, Georges, Sholl, David S., Thyagarajan, Raghuram, Siderius, Daniel, van der Veen, Monique A., Snurr, Randall Q., Goncalves, Rebecca B., Telfer, Shane, Lee, Seok J., Ting, Valeska P., Rowlandson, Jemma L., Uemura, Takashi, Iiyuka, Tomoya, Rega, Davide, Van Speybroeck, Veronique, Rogge, Sven M.J., Lamaire, Aran, Walton, Krista S., Bingel, Lukas W., Wuttke, Stefan, Andreo, Jacopo, Yaghi, Omar, Ania, Conchi O., Zhang, Bing, Yavuz, Cafer T., Nguyen, Thien S., Zamora, Félix, Montoro, Carmen, Zhou, Hongcai, Kirchon, Angelo, Fairen-Jimenez, David, Azevedo, Diana, Vilarrasa-García, Enrique, Santos, Bianca F., Bu, Xian-He, Chang, Ze, Bunzen, Hana, Champness, Neil R., Griffin, Sarah L., Chen, Banglin, Lin, Rui-Biao, Coasne, Benoit, Cohen, Seth, Moreton, Jessica C., Colón, Yamil J., Chen, Linjiang, Clowes, Rob, Universidad de Alicante. Departamento de Química Inorgánica, Osterrieth, Johannes W. M., Rampersad, James, Madden, David G., Rampal, Nakul, Skoric, Luka, Connolly, Bethany M., Allendorf, Mark D., Stavila, Vitalie, Snider, Jonathan L., Ameloot, Rob, Marreiros, João, Coudert, François-Xavier, Cui, Yong, Hou, Bang, D'Alessandro, Deanna M., Doheny, Patrick W., Dincă, Mircea, Sun, Chenyue, Doonan, Christian, Huxley, Michael Thomas, Evans, Jack D., Bara, Dominic, Falcaro, Paolo, Ricco, Raffaele, Farha, Omar, Idrees, Karam B., Islamoglu, Timur, Feng, Pingyun, Yang, Huajun, Forgan, Ross S., Furukawa, Shuhei, Sanchez, Eli, Gascon, Jorge, Telalović, Selvedin, Ghosh, Sujit K., Mukherjee, Soumya, Hill, Matthew R., Sadiq, Muhammed Munir, Horcajada, Patricia, DeWitt, Stephen J. A., Salcedo-Abraira, Pablo, Kaneko, Katsumi, Kukobat, Radovan, Kenvin, Jeff, Keskin, Seda, Kitagawa, Susumu, Otake, Ken-ichi, Lively, Ryan P., Llewellyn, Phillip L., Lotsch, Bettina V., Emmerling, Sebastian T., Pütz, Alexander M., Martí-Gastaldo, Carlos, Padial, Natalia M., Garcia-Martinez, Javier, Linares, Noemi, Maspoch, Daniel, Rosseinsky, Matthew J., Suárez del Pino, Jose A., Moghadam, Peyman Z., Oktavian, Rama, Morris, Russel E., Wheatley, Paul S., Navarro, Jorge, Petit, Camille, Danaci, David, Katsoulidis, Alexandros P., Schröder, Martin, Han, Xue, Yang, Sihai, Serre, Christian, Mouchaham, Georges, Sholl, David S., Thyagarajan, Raghuram, Siderius, Daniel, van der Veen, Monique A., Snurr, Randall Q., Goncalves, Rebecca B., Telfer, Shane, Lee, Seok J., Ting, Valeska P., Rowlandson, Jemma L., Uemura, Takashi, Iiyuka, Tomoya, Rega, Davide, Van Speybroeck, Veronique, Rogge, Sven M.J., Lamaire, Aran, Walton, Krista S., Bingel, Lukas W., Wuttke, Stefan, Andreo, Jacopo, Yaghi, Omar, Ania, Conchi O., Zhang, Bing, Yavuz, Cafer T., Nguyen, Thien S., Zamora, Félix, Montoro, Carmen, Zhou, Hongcai, Kirchon, Angelo, Fairen-Jimenez, David, Azevedo, Diana, Vilarrasa-García, Enrique, Santos, Bianca F., Bu, Xian-He, Chang, Ze, Bunzen, Hana, Champness, Neil R., Griffin, Sarah L., Chen, Banglin, Lin, Rui-Biao, Coasne, Benoit, Cohen, Seth, Moreton, Jessica C., Colón, Yamil J., Chen, Linjiang, and Clowes, Rob

Abstract

Porosity and surface area analysis play a prominent role in modern materials science. At the heart of this sits the Brunauer–Emmett–Teller (BET) theory, which has been a remarkably successful contribution to the field of materials science. The BET method was developed in the 1930s for open surfaces but is now the most widely used metric for the estimation of surface areas of micro- and mesoporous materials. Despite its widespread use, the calculation of BET surface areas causes a spread in reported areas, resulting in reproducibility problems in both academia and industry. To prove this, for this analysis, 18 already-measured raw adsorption isotherms were provided to sixty-one labs, who were asked to calculate the corresponding BET areas. This round-robin exercise resulted in a wide range of values. Here, the reproducibility of BET area determination from identical isotherms is demonstrated to be a largely ignored issue, raising critical concerns over the reliability of reported BET areas. To solve this major issue, a new computational approach to accurately and systematically determine the BET area of nanoporous materials is developed. The software, called “BET surface identification” (BETSI), expands on the well-known Rouquerol criteria and makes an unambiguous BET area assignment possible.

Published
2022

28. How Reproducible are Surface Areas Calculated from the BET Equation?

Author

European Commission, European Research Council, University of Cambridge, Trinity College Cambridge, National Nuclear Security Administration (US), Department of Energy (US), Alexander von Humboldt Foundation, Center for Advancing Electronics Dresden, Science and Engineering Research Board (India), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Universidad de Alicante, Research Foundation - Flanders, Engineering and Physical Sciences Research Council (UK), National Research Foundation of Korea, Indonesia Endowment Fund for Education, National Institute of Standards and Technology (US), Osterrieth, Johannes W. M., Rampersad, James, Madden, David, Rampal, Nakul, Skoric, Luka, Connolly, Bethany, Allendorf, Mark D., Stavila, Vitalie, Snider, Jonathan L., Ameloot, Rob, Marreiros, João, Bara, Dominic, Furukawa, Shuhei, Sánchez, Eli, Gascón, Jorge, Telalović, Selvedin, Ghosh, Sujit K., Mukherjee, Soumya, Hill, Matthew R., Sadiq, Muhammed Munir, Horcajada, Patricia, DeWitt, Stephen J. A., Salcedo Abraira, Pablo, Kaneko, Katsumi, Kukobat, Radovan, Kenvin, Jeff, Keskin, Seda, Kitagawa, Susumu, Otake, Ken-Ichi, Lively, Ryan P., Llewellyn, Phillip, Lotsch, Bettina V., Emmerling, Sebastian T., Pütz, Alexander M., Martí-Gastaldo, Carlos, Padial, Natalia M., García-Martínez, Javier, Linares, Noemí, Maspoch, Daniel, Rosseinsky, Matthew J., Suárez, José Antonio, Moghadam, Peyman, Oktavian, Rama, Morris, Russell E., Wheatley, Paul S., Navarro, Jorge, Petit, Camille, Danaci, David, Katsoulidis, Alexandros P., Schröder, Martin, Han, Xue, Yang, Sihai, Serre, Christian, Mouchaham, Georges, Sholl, David S., Thyagarajan, Raghuram, Siderius, Daniel, Veen, Monique A. van der, Snurr, Randall Q., Goncalves, Rebecca B., Telfer, Shane, Lee, Seok J., Ting, Valeska P., Rowlandson, Jemma L., Uemura, Takashi, Iiyuka, Tomoya, Rega, Davide, Speybroeck, Veronique van, Rogge, Sven M. J., Lamaire, Aran, Walton, Krista S., Bingel, Lukas W., Wuttke, Stefan, Andreo, Jacopo, Yaghi, Omar, Ania, Conchi O., Zhang, Bing, Yavuz, Cafer T., Nguyen, Thien S., Zamora, Félix, Montoro, Carmen, Zhou, Hongcai, Kirchon, Angelo, Fairen-Jiménez, David, Azevedo, Diana, Vilarrasa-García, E., Santos, Bianca S., Bu, Xian-He, Chang, Ze, Bunzen, Hana, Champness, Neil R., Griffin, Sarah L., Chen, Banglin, Lin, Rui-Biao, Coasne, Benoit, Cohen, Seth, Moreton, Jessica C., Colón, Yamil J., Chen, Linjiang, Clowes, Rob, Coudert, François Xavier, Cui, Yong, Hou, Bang, D'Alessandro, Deanna M., Doheny, Patrick W., Dincă, Mircea, Sun, Chenyue, Doonan, Christian J., Huxley, Michael Thomas, Evans, Jack D., Falcaro, Paolo, Ricco, Raffaele, Farha, Omar, Idrees, Karam B., Islamoglu, Timur, Feng, Pingyun, Chang, Huajun, Forgan, Ross S., European Commission, European Research Council, University of Cambridge, Trinity College Cambridge, National Nuclear Security Administration (US), Department of Energy (US), Alexander von Humboldt Foundation, Center for Advancing Electronics Dresden, Science and Engineering Research Board (India), Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Universidad de Alicante, Research Foundation - Flanders, Engineering and Physical Sciences Research Council (UK), National Research Foundation of Korea, Indonesia Endowment Fund for Education, National Institute of Standards and Technology (US), Osterrieth, Johannes W. M., Rampersad, James, Madden, David, Rampal, Nakul, Skoric, Luka, Connolly, Bethany, Allendorf, Mark D., Stavila, Vitalie, Snider, Jonathan L., Ameloot, Rob, Marreiros, João, Bara, Dominic, Furukawa, Shuhei, Sánchez, Eli, Gascón, Jorge, Telalović, Selvedin, Ghosh, Sujit K., Mukherjee, Soumya, Hill, Matthew R., Sadiq, Muhammed Munir, Horcajada, Patricia, DeWitt, Stephen J. A., Salcedo Abraira, Pablo, Kaneko, Katsumi, Kukobat, Radovan, Kenvin, Jeff, Keskin, Seda, Kitagawa, Susumu, Otake, Ken-Ichi, Lively, Ryan P., Llewellyn, Phillip, Lotsch, Bettina V., Emmerling, Sebastian T., Pütz, Alexander M., Martí-Gastaldo, Carlos, Padial, Natalia M., García-Martínez, Javier, Linares, Noemí, Maspoch, Daniel, Rosseinsky, Matthew J., Suárez, José Antonio, Moghadam, Peyman, Oktavian, Rama, Morris, Russell E., Wheatley, Paul S., Navarro, Jorge, Petit, Camille, Danaci, David, Katsoulidis, Alexandros P., Schröder, Martin, Han, Xue, Yang, Sihai, Serre, Christian, Mouchaham, Georges, Sholl, David S., Thyagarajan, Raghuram, Siderius, Daniel, Veen, Monique A. van der, Snurr, Randall Q., Goncalves, Rebecca B., Telfer, Shane, Lee, Seok J., Ting, Valeska P., Rowlandson, Jemma L., Uemura, Takashi, Iiyuka, Tomoya, Rega, Davide, Speybroeck, Veronique van, Rogge, Sven M. J., Lamaire, Aran, Walton, Krista S., Bingel, Lukas W., Wuttke, Stefan, Andreo, Jacopo, Yaghi, Omar, Ania, Conchi O., Zhang, Bing, Yavuz, Cafer T., Nguyen, Thien S., Zamora, Félix, Montoro, Carmen, Zhou, Hongcai, Kirchon, Angelo, Fairen-Jiménez, David, Azevedo, Diana, Vilarrasa-García, E., Santos, Bianca S., Bu, Xian-He, Chang, Ze, Bunzen, Hana, Champness, Neil R., Griffin, Sarah L., Chen, Banglin, Lin, Rui-Biao, Coasne, Benoit, Cohen, Seth, Moreton, Jessica C., Colón, Yamil J., Chen, Linjiang, Clowes, Rob, Coudert, François Xavier, Cui, Yong, Hou, Bang, D'Alessandro, Deanna M., Doheny, Patrick W., Dincă, Mircea, Sun, Chenyue, Doonan, Christian J., Huxley, Michael Thomas, Evans, Jack D., Falcaro, Paolo, Ricco, Raffaele, Farha, Omar, Idrees, Karam B., Islamoglu, Timur, Feng, Pingyun, Chang, Huajun, and Forgan, Ross S.

Abstract

Porosity and surface area analysis play a prominent role in modern materials science. At the heart of this sits the Brunauer–Emmett–Teller (BET) theory, which has been a remarkably successful contribution to the field of materials science. The BET method was developed in the 1930s for open surfaces but is now the most widely used metric for the estimation of surface areas of micro- and mesoporous materials. Despite its widespread use, the calculation of BET surface areas causes a spread in reported areas, resulting in reproducibility problems in both academia and industry. To prove this, for this analysis, 18 already-measured raw adsorption isotherms were provided to sixty-one labs, who were asked to calculate the corresponding BET areas. This round-robin exercise resulted in a wide range of values. Here, the reproducibility of BET area determination from identical isotherms is demonstrated to be a largely ignored issue, raising critical concerns over the reliability of reported BET areas. To solve this major issue, a new computational approach to accurately and systematically determine the BET area of nanoporous materials is developed. The software, called “BET surface identification” (BETSI), expands on the well-known Rouquerol criteria and makes an unambiguous BET area assignment possible.

Published
2022

29. Free energy of metal-organic framework self-assembly.

Author

Colón, Yamil J., Guo, Ashley Z., Antony, Lucas W., Hoffmann, Kyle Q., and de Pablo, Juan J.

Subjects
*METAL-organic frameworks
Abstract

Metal-organic frameworks (MOFs) represent an important class of materials. Careful selection of building blocks allows for tailoring of the properties of the resulting framework. The self-assembly process, however, is not understood, and without detailed knowledge of the underlying molecular mechanism, it is difficult to anticipate whether a particular design can be realized, or whether the material adopts a metastable, kinetically arrested state. We present a detailed examination of early-stage self-assembly pathways of the MOF-5. Enhanced sampling techniques are used to model a self-assembly in an explicit solvent (dimethylformamide, DMF). We identify several free energy barriers encountered during the assembly of the final MOF, which arise from structural rearrangements preceding MOF formation and from disrupted MOF-solvent interactions as formation proceeds. In all cases considered here, MOFs exhibit favorable entropic gains during the assembly. More generally, the strategy presented provides a step toward the experimental design characterizing the formation of ordered frameworks and possible sources of polymorphism. [ABSTRACT FROM AUTHOR]

Published
2019
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34. Multiscale structural control of linked metal–organic polyhedra gel by aging-induced linkage-reorganization

Author

Wang, Zaoming, primary, Villa Santos, Christian, additional, Legrand, Alexandre, additional, Haase, Frederik, additional, Hara, Yosuke, additional, Kanamori, Kazuyoshi, additional, Aoyama, Takuma, additional, Urayama, Kenji, additional, Doherty, Cara M., additional, Smales, Glen J., additional, Pauw, Brian R., additional, Colón, Yamil J., additional, and Furukawa, Shuhei, additional

Published
2021
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39. The role of molecular modelling and simulation in the discovery and deployment of metal-organic frameworks for gas storage and separation

Author

Sturluson, Arni, primary, Huynh, Melanie T., additional, Kaija, Alec R., additional, Laird, Caleb, additional, Yoon, Sunghyun, additional, Hou, Feier, additional, Feng, Zhenxing, additional, Wilmer, Christopher E., additional, Colón, Yamil J., additional, Chung, Yongchul G., additional, Siderius, Daniel W., additional, and Simon, Cory M., additional

Published
2019
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42. SSAGES: Software Suite for Advanced General Ensemble Simulations

Author

Sidky, Hythem, primary, Colón, Yamil J., additional, Helfferich, Julian, additional, Sikora, Benjamin J., additional, Bezik, Cody, additional, Chu, Weiwei, additional, Giberti, Federico, additional, Guo, Ashley Z., additional, Jiang, Xikai, additional, Lequieu, Joshua, additional, Li, Jiyuan, additional, Moller, Joshua, additional, Quevillon, Michael J., additional, Rahimi, Mohammad, additional, Ramezani-Dakhel, Hadi, additional, Rathee, Vikramjit S., additional, Reid, Daniel R., additional, Sevgen, Emre, additional, Thapar, Vikram, additional, Webb, Michael A., additional, Whitmer, Jonathan K., additional, and de Pablo, Juan J., additional

Published
2018
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44. The effect of co-adsorbed solvent molecules on H2 binding to metal alkoxides.

Author

Colón, Yamil J. and Snurr, Randall Q.

Abstract

The introduction of metal alkoxides has been proposed as an attractive option to enhance hydrogen binding energies in porous materials such as metal–organic frameworks (MOFs) for room-temperature hydrogen storage applications. The presence of residual solvent molecules from MOF synthesis can, however, affect the performance of these functional groups. We perform quantum chemical calculations to predict solvent binding energies onto the metal-alkoxides and the temperatures required to drive off the solvent molecules and successfully activate porous materials with these moieties. Calculations are performed for Li, Mg, Zn, Cu, and Ni alkoxides and chloroform (CHCl3), dimethylformamide (DMF), ethanol, methanol, and water solvent molecules. We identify CHCl3 as a promising solvent that can be removed from these alkoxides at mild temperatures, whereas DMF binds strongly to the metal alkoxides and removal would require temperatures above the present upper bound of thermal stability in MOFs. As a second objective, we calculated the binding energies of hydrogen to metal alkoxide–solvent complexes to explore the effect of any solvent molecules that cannot be removed. [ABSTRACT FROM AUTHOR]

Published
2019
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45. Expanding the Design Space of Polymer–Metal Organic Framework (MOF) Gels by Understanding Polymer–MOF Interactions

Author

Verma, Prince, Bannon, Mark S., Kuenen, Mara K., Raj, Sanoj, Dhakal, Ankit, Stone, Kevin, Nichols, Asa W., Machan, Charles W., Colón, Yamil J., Letteri, Rachel A., and Giri, Gaurav

Abstract

The fabrication of polymer-MOF composite gels holds great potential to provide emergent properties for drug delivery, environmental remediation, and catalysis. To leverage the full potential of these composites, we investigated how the presence and chemistry of polymers impact MOF formation within the composites and, in turn, how MOFs impact polymer gelation. We show that polymers with a high density of strongly metal-binding carboxylic acids inhibit MOF formation; however, reducing the density of carboxylic acids or substituting them with weaker metal-binding hydroxyl groups permits both MOF formation and gelation within composites. Preparing composites with poly(ethylene glycol) (PEG), which does not bind MOF zirconium (Zr)-oxo clusters, and observing gelation suggests that MOFs can entrap polymer chains to create cross-links in addition to cross-linking them through polymer-Zr-oxo interactions. Both simulations and experiments show composite hydrogels formed with poly(vinyl alcohol) (PVA) to be more stable than those made with PEG, which can reptate through MOF pores upon heating. We demonstrate the generalizability of this composite formation process across different Zr-based MOFs (UiO-66, NU-901, UiO-67, and MOF-525) and by spin-coating gels into conformable films. PVA-UiO-66 composite hydrogels demonstrated high sorption and sustained release of methylene blue relative to the polymer alone (3× loading, 28× slower release), and PVA-MOF-525 composite hydrogels capably sorb the therapeutic peptide Angiotensin 1–7. By understanding the influence of polymer-MOF interactions on the structure and properties of composite gels, this work informs and expands the design space of this emerging class of materials.

Published
2024
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46. Absorption of Carbon Dioxide in Two Binary Mixtures of Ionic Liquids

Author

Pinto, Alicia M., Rodríguez, Héctor, Colón, Yamil J., Arce, Alberto, Arce, Alberto, and Soto, Ana

Abstract

Ionic liquids are the basis of a potential alternative to conventional processes based on aqueous amines currently used for carbon dioxide capture. Mixtures of ionic liquids offer an additional degree of tailoring over the intrinsic tunable properties of “single” ionic liquids, while totally maintaining the ionic liquid character. In this work, two mixtures of mutually miscible ionic liquids have been investigated, namely, 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide + 1-ethyl-3-methylimidazolium ethylsulfate and 1-ethyl-3-methylimidazolium bis(trifluoromethanesulfonyl)imide + 1-butyl-3-ethylimidazolium ethylsulfate. Their ability to absorb carbon dioxide in the pressure range up to ca. 16 bar, at 298.2 K, was determined at different composition ratios of the mixtures; their thermal phase-transition temperatures and physical properties (density, viscosity, and surface tension) were determined over the entire compositional range, at atmospheric pressure and the same temperature. The absorption data were adequately correlated by the NRTL (Non-Random Two-Liquid) model, whereas different equations were successful in satisfactorily correlating the physical properties of the mixtures and in predicting them from knowledge of the properties of the pure ionic liquid components.

Published
2024
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