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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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5. 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

6. 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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7. 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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9. 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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12. 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

13. 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

14. 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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17. Active learning of alchemical adsorption simulations; towards a universal adsorption model.

Author

Osaro E, Fajardo-Rojas F, Cooper GM, Gómez-Gualdrón D, and Colón YJ

Abstract

Adsorption is a fundamental process studied in materials science and engineering because it plays a critical role in various applications, including gas storage and separation. Understanding and predicting gas adsorption within porous materials demands comprehensive computational simulations that are often resource intensive, limiting the identification of promising materials. Active learning (AL) methods offer an effective strategy to reduce the computational burden by selectively acquiring critical data for model training. Metal-organic frameworks (MOFs) exhibit immense potential across various adsorption applications due to their porous structure and their modular nature, leading to diverse pore sizes and chemistry that serve as an ideal platform to develop adsorption models. Here, we demonstrate the efficacy of AL in predicting gas adsorption within MOFs using "alchemical" molecules and their interactions as surrogates for real molecules. We first applied AL separately to each MOF, reducing the training dataset size by 57.5% while retaining predictive accuracy. Subsequently, we amalgamated the refined datasets across 1800 MOFs to train a multilayer perceptron (MLP) model, successfully predicting adsorption of real molecules. Furthermore, by integrating MOF features into the AL framework using principal component analysis (PCA), we navigated MOF space effectively, achieving high predictive accuracy with only a subset of MOFs. Our results highlight AL's efficiency in reducing dataset size, enhancing model performance, and offering insights into adsorption phenomenon in large datasets of MOFs. This study underscores AL's crucial role in advancing computational material science and developing more accurate and less data intensive models for gas adsorption in porous materials., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Published
2024
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