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43 results on '"Truhlar, Donald G."'

1. Semiclassical Nonadiabatic Molecular Dynamics Using Linearized Pair-Density Functional Theory

Author

Hennefarth, Matthew R., Truhlar, Donald G., and Gagliardi, Laura

Subjects
Physics - Chemical Physics
Abstract

Nonadiabatic molecular dynamics is an effective method for modeling nonradiative decay in electronically excited molecules. Its accuracy depends strongly on the quality of the potential energy surfaces, and its affordability for long direct-dynamic simulations with adequate ensemble averaging depends strongly on the cost of the required electronic structure calculations. Linearized pair-density functional theory (L-PDFT) is a recently developed post-self-consistent field multireference method that can model potential energy surfaces with an accuracy similar to expensive multireference perturbation theories but at a computational cost similar to the underlying multiconfiguration self-consistent field method. Here we integrate the SHARC dynamics and PySCF electronic structure code to utilize L-PDFT for electronically nonadiabatic calculations and use the combined programs to study the photoisomerization reaction of cis-azomethane. We show that L-PDFT is able to successfully simulate the photoisomerization and yields results similar to the more expensive extended multi-state complete active space second-order perturbation theory. This shows that L-PDFT can model internal conversion, and it demonstrates its promise for broader photodynamics applications., Comment: 8 pages, 5 figures

Published
2024

2. Analytic Nuclear Gradients for Complete Active Space Linearized Pair-Density Functional Theory

Author

Hennefarth, Matthew R., Hermes, Matthew R., Truhlar, Donald G., and Gagliardi, Laura

Subjects
Physics - Chemical Physics
Abstract

Accurately modeling photochemical reactions is difficult due to the presence of conical intersections and locally avoided crossings as well as the inherently multiconfigurational character of excited states. As such, one needs a multi-state method that incorporates state interaction in order to accurately model the potential energy surface at all nuclear coordinates. The recently developed linearized pair-density functional theory (L-PDFT) is a multi-state extension of multiconfiguration PDFT, and it has been shown to be a cost-effective post-MCSCF method (as compared to more traditional and expensive multireference many-body perturbation methods or multireference configuration interaction methods) that can accurately model potential energy surfaces in regions of strong nuclear-electronic coupling in addition to accurately predicting Franck-Condon vertical excitations. In this paper, we report the derivation of analytic gradients for L-PDFT and their implementation in the PySCF-forge software, and we illustrate the utility of these gradients for predicting ground- and excited-state equilibrium geometries and adiabatic excitation energies for formaldehyde, s-trans-butadiene, phenol, and cytosine., Comment: 24 pages, 3 figures

Published
2024
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4. High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

Author

Rosen, Andrew S, Fung, Victor, Huck, Patrick, O’Donnell, Cody T, Horton, Matthew K, Truhlar, Donald G, Persson, Kristin A, Notestein, Justin M, and Snurr, Randall Q

Subjects
Inorganic Chemistry, Chemical Sciences, Bioengineering, Affordable and Clean Energy, Theoretical and computational chemistry, Materials engineering, Condensed matter physics
Abstract

With the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

Published
2022

7. The OpenMolcas Web: A Community-Driven Approach to Advancing Computational Chemistry

Author

Fundación la Caixa, European Commission, Ministerio de Ciencia e Innovación (España), Department of Energy (US), Swiss National Science Foundation, Ministero dell'Istruzione, dell'Università e della Ricerca, Carl Trygger Foundation, Royal Society (UK), Independent Research Fund Denmark, German Research Foundation, Generalitat de Catalunya, Olle Engkvist Foundation, National Centre for Research and Development (Poland), National Science Foundation (US), Academy of Finland, Knut and Alice Wallenberg Foundation, Swedish Research Council, Université de Nantes, Japan Society for the Promotion of Science, Università degli Studi di Siena, Novo Nordisk Foundation, Argonne National Laboratory (US), National University of Singapore, Air Force Office of Scientific Research (US), Linköping University, Centre National de la Recherche Scientifique (France), Li Manni, Giovanni [0000-0002-3666-3880], Fdez Galván, Ignacio [0000-0002-0684-7689], #NODATA#, Aleotti, Flavia [0000-0002-7176-5305], Aquilante, Francesco [0000-0003-4422-3938], Autschbach, Jochen [0000-0001-9392-877X], Avagliano, Davide [0000-0001-5539-9731], Baiardi, Alberto [0000-0001-9112-8664], Bao, Jie J. [0000-0003-0197-3405], Battaglia, Stefano [0000-0002-5082-2681], Blanco-González, Alejandro [0000-0001-7379-2588], Bokarev, Sergey I. [0000-0003-0779-5013], Broer, Ria [0000-0002-5437-9509], Cacciari, Roberto [0000-0001-8290-4297], Calio, Paul B. [0000-0001-8385-2628], Carlson, Rebecca K. [0000-0002-1710-5816], Carvalho Couto, Rafael [0000-0003-4020-0923], Cerdán, L. [0000-0002-7174-2453], Chibotaru, Liviu F. [0000-0003-1556-0812], Chilton, Nicholas F. [0000-0002-8604-0171], Church, Jonathan Richard [0000-0001-5986-1305], Conti, Irene [0000-0001-7982-4480], Coriani, Sonia [0000-0002-4487-897X], Cuéllar-Zuquin, Juliana [0000-0002-0127-579X], Daoud, Razan E. [0000-0001-7034-0770], Dattani, Nike [0000-0001-5667-3632], Decleva, Piero [0000-0002-7322-887X], de Graaf, Coen [0000-0001-8114-6658], Delcey, Mickaël G. [0000-0001-9883-3569], De Vico, Luca [0000-0002-2821-5711], Dobrautz, Werner [0000-0001-6479-1874], Dong, Sijia S. [0000-0001-8182-6522], Feng, Rulin [0000-0002-9812-3804], Ferré, Nicolas [0000-0002-5583-8834], Filatov Gulak, Michael [0000-0002-1541-739X], Gagliardi, Laura [0000-0001-5227-1396], Garavelli, Marco [0000-0002-0796-289X], González, Leticia [0000-0001-5112-794X], Guo, Meiyuan [0000-0003-2474-6264], Hennefarth, Matthew R. [0000-0002-6601-2253], Hermes, Matthew R. [0000-0001-7807-2950], Huix-Rotllant, Miquel [0000-0002-2131-7328], Jaiswal, Vishal Kumar [0000-0002-5090-7984], Kaliakin, Danil S. [0000-0002-9354-8248], King, Daniel S. [0000-0003-0208-5274], Larsson, Ernst D. [0000-0001-7655-2993], Lehtola, Susi [0000-0001-6296-8103], Lepetit, Marie-Bernadette [0000-0001-8730-4282], Lischka, Hans [0000-0002-5656-3975], Lundberg, Marcus [0000-0002-1312-1202], Mai, Sebastian [0000-0001-5327-8880], Marquetand, Philipp [0000-0002-8711-1533], Merritt, Isabella C. D. [0000-0002-7965-7798], Nenov, Artur [0000-0003-3071-5341], Nguyen, Vu Ha Anh [0000-0002-4177-0656], Nishimoto, Yoshio [0000-0001-5581-4712], Oakley, Meagan S. [0000-0001-5072-7572], Olivucci, Massimo [0000-0002-8247-209X], Oppel, Markus [0000-0001-6363-2310], Padula, Daniele [0000-0002-7171-7928], Pandharkar, Riddhish [0000-0003-4086-4308], Phung, Quan Manh [0000-0001-8205-5328], Plasser, Felix [0000-0003-0751-148X], Reiher, Markus [0000-0002-9508-1565], Rivalta, Ivan [0000-0002-1208-602X], Roca-Sanjuán, Daniel [0000-0001-6495-2770], Romig, Thies [0000-0002-9424-2398], Safari, Arta Anushirwan [0000-0003-4511-8902], Sánchez-Mansilla, Aitor [0000-0002-2601-190X], Sand, Andrew M. [0000-0002-7166-2066], Schapiro, Igor [0000-0001-8536-6869], Scott, Thais R. [0000-0002-5746-5517], Segarra-Martí, Javier [0000-0002-2076-3406], Segatta, Francesco [0000-0003-4150-6676], Sergentu, Dumitru-Claudiu [0000-0001-6570-5245], Shepard, Ron [0000-0002-0272-0824], Shu, Yinan [0000-0002-8371-0221], Sørensen, Lasse Kragh [0000-0002-1931-1867], Tenorio, Bruno Nunes Cabral [0000-0002-9702-998X], Truhlar, Donald G. [0000-0002-7742-7294], Ungur, Liviu [0000-0001-5015-4225], Vacher, Morgane [0000-0001-9418-6579], Veryazov, Valera [0000-0002-0172-7047], Li Manni, Giovanni, Fdez Galván, Ignacio, Alavi, Ali, Aleotti, Flavia, Aquilante, Francesco, Autschbach, Jochen, Avagliano, Davide, Baiardi, Alberto, Bao, Jie J., Battaglia, Stefano, Birnoschi, Letitia, Blanco-González, Alejandro, Bokarev, Sergey I., Broer, Ria, Cacciari, Roberto, Calio, Paul B., Carlson, Rebecca K., Carvalho Couto, Rafael, Cerdán, L., Chibotaru, Liviu F., Chilton, Nicholas F., Church, Jonathan Richard, Conti, Irene, Coriani, Sonia, Cuéllar-Zuquin, Juliana, Daoud, Razan E., Dattani, Nike, Decleva, Piero, de Graaf, Coen, Delcey, Mickaël G., De Vico, Luca, Dobrautz, Werner, Dong, Sijia S., Feng, Rulin, Ferré, Nicolas, Filatov Gulak, Michael, Gagliardi, Laura, Garavelli, Marco, González, Leticia, Guan, Yafu, Guo, Meiyuan, Hennefarth, Matthew R., Hermes, Matthew R., Hoyer, Chad E., Huix-Rotllant, Miquel, Jaiswal, Vishal Kumar, Kaiser, Andy, Kaliakin, Danil S., Khamesian, Marjan, King, Daniel S., Kochetov, Vladislav, Krośnicki, Marek, Kumaar, Arpit Arun, Larsson, Ernst D., Lehtola, Susi, Lepetit, Marie-Bernadette, Lischka, Hans, López Ríos, Pablo, Lundberg, Marcus, Ma, Dongxia, Mai, Sebastian, Marquetand, Philipp, Merritt, Isabella C. D., Montorsi, Francesco, Mörchen, Maximilian, Nenov, Artur, Nguyen, Vu Ha Anh, Nishimoto, Yoshio, Oakley, Meagan S., Olivucci, Massimo, Oppel, Markus, Padula, Daniele, Pandharkar, Riddhish, Phung, Quan Manh, Plasser, Felix, Raggi, Gerardo, Rebolini, Elisa, Reiher, Markus, Rivalta, Ivan, Roca-Sanjuán, Daniel, Romig, Thies, Safari, Arta Anushirwan, Sánchez-Mansilla, Aitor, Sand, Andrew M., Schapiro, Igor, Scott, Thais R., Segarra-Martí, Javier, Segatta, Francesco, Sergentu, Dumitru-Claudiu, Sharma, Prachi, Shepard, Ron, Shu, Yinan, Staab, Jakob K., Straatsma, Tjerk P., Sørensen, Lasse Kragh, Tenorio, Bruno Nunes Cabral, Truhlar, Donald G., Ungur, Liviu, Vacher, Morgane, Veryazov, Valera, Voß, Torben Arne, Weser, Oskar, Wu, Dihua, Yang, Xuchun, Yarkony, David, Zhou, Chen, Zobel, J Patrick, Lindh, Roland, Fundación la Caixa, European Commission, Ministerio de Ciencia e Innovación (España), Department of Energy (US), Swiss National Science Foundation, Ministero dell'Istruzione, dell'Università e della Ricerca, Carl Trygger Foundation, Royal Society (UK), Independent Research Fund Denmark, German Research Foundation, Generalitat de Catalunya, Olle Engkvist Foundation, National Centre for Research and Development (Poland), National Science Foundation (US), Academy of Finland, Knut and Alice Wallenberg Foundation, Swedish Research Council, Université de Nantes, Japan Society for the Promotion of Science, Università degli Studi di Siena, Novo Nordisk Foundation, Argonne National Laboratory (US), National University of Singapore, Air Force Office of Scientific Research (US), Linköping University, Centre National de la Recherche Scientifique (France), Li Manni, Giovanni [0000-0002-3666-3880], Fdez Galván, Ignacio [0000-0002-0684-7689], #NODATA#, Aleotti, Flavia [0000-0002-7176-5305], Aquilante, Francesco [0000-0003-4422-3938], Autschbach, Jochen [0000-0001-9392-877X], Avagliano, Davide [0000-0001-5539-9731], Baiardi, Alberto [0000-0001-9112-8664], Bao, Jie J. [0000-0003-0197-3405], Battaglia, Stefano [0000-0002-5082-2681], Blanco-González, Alejandro [0000-0001-7379-2588], Bokarev, Sergey I. [0000-0003-0779-5013], Broer, Ria [0000-0002-5437-9509], Cacciari, Roberto [0000-0001-8290-4297], Calio, Paul B. [0000-0001-8385-2628], Carlson, Rebecca K. [0000-0002-1710-5816], Carvalho Couto, Rafael [0000-0003-4020-0923], Cerdán, L. [0000-0002-7174-2453], Chibotaru, Liviu F. [0000-0003-1556-0812], Chilton, Nicholas F. [0000-0002-8604-0171], Church, Jonathan Richard [0000-0001-5986-1305], Conti, Irene [0000-0001-7982-4480], Coriani, Sonia [0000-0002-4487-897X], Cuéllar-Zuquin, Juliana [0000-0002-0127-579X], Daoud, Razan E. [0000-0001-7034-0770], Dattani, Nike [0000-0001-5667-3632], Decleva, Piero [0000-0002-7322-887X], de Graaf, Coen [0000-0001-8114-6658], Delcey, Mickaël G. [0000-0001-9883-3569], De Vico, Luca [0000-0002-2821-5711], Dobrautz, Werner [0000-0001-6479-1874], Dong, Sijia S. [0000-0001-8182-6522], Feng, Rulin [0000-0002-9812-3804], Ferré, Nicolas [0000-0002-5583-8834], Filatov Gulak, Michael [0000-0002-1541-739X], Gagliardi, Laura [0000-0001-5227-1396], Garavelli, Marco [0000-0002-0796-289X], González, Leticia [0000-0001-5112-794X], Guo, Meiyuan [0000-0003-2474-6264], Hennefarth, Matthew R. [0000-0002-6601-2253], Hermes, Matthew R. [0000-0001-7807-2950], Huix-Rotllant, Miquel [0000-0002-2131-7328], Jaiswal, Vishal Kumar [0000-0002-5090-7984], Kaliakin, Danil S. [0000-0002-9354-8248], King, Daniel S. [0000-0003-0208-5274], Larsson, Ernst D. [0000-0001-7655-2993], Lehtola, Susi [0000-0001-6296-8103], Lepetit, Marie-Bernadette [0000-0001-8730-4282], Lischka, Hans [0000-0002-5656-3975], Lundberg, Marcus [0000-0002-1312-1202], Mai, Sebastian [0000-0001-5327-8880], Marquetand, Philipp [0000-0002-8711-1533], Merritt, Isabella C. D. [0000-0002-7965-7798], Nenov, Artur [0000-0003-3071-5341], Nguyen, Vu Ha Anh [0000-0002-4177-0656], Nishimoto, Yoshio [0000-0001-5581-4712], Oakley, Meagan S. [0000-0001-5072-7572], Olivucci, Massimo [0000-0002-8247-209X], Oppel, Markus [0000-0001-6363-2310], Padula, Daniele [0000-0002-7171-7928], Pandharkar, Riddhish [0000-0003-4086-4308], Phung, Quan Manh [0000-0001-8205-5328], Plasser, Felix [0000-0003-0751-148X], Reiher, Markus [0000-0002-9508-1565], Rivalta, Ivan [0000-0002-1208-602X], Roca-Sanjuán, Daniel [0000-0001-6495-2770], Romig, Thies [0000-0002-9424-2398], Safari, Arta Anushirwan [0000-0003-4511-8902], Sánchez-Mansilla, Aitor [0000-0002-2601-190X], Sand, Andrew M. [0000-0002-7166-2066], Schapiro, Igor [0000-0001-8536-6869], Scott, Thais R. [0000-0002-5746-5517], Segarra-Martí, Javier [0000-0002-2076-3406], Segatta, Francesco [0000-0003-4150-6676], Sergentu, Dumitru-Claudiu [0000-0001-6570-5245], Shepard, Ron [0000-0002-0272-0824], Shu, Yinan [0000-0002-8371-0221], Sørensen, Lasse Kragh [0000-0002-1931-1867], Tenorio, Bruno Nunes Cabral [0000-0002-9702-998X], Truhlar, Donald G. [0000-0002-7742-7294], Ungur, Liviu [0000-0001-5015-4225], Vacher, Morgane [0000-0001-9418-6579], Veryazov, Valera [0000-0002-0172-7047], Li Manni, Giovanni, Fdez Galván, Ignacio, Alavi, Ali, Aleotti, Flavia, Aquilante, Francesco, Autschbach, Jochen, Avagliano, Davide, Baiardi, Alberto, Bao, Jie J., Battaglia, Stefano, Birnoschi, Letitia, Blanco-González, Alejandro, Bokarev, Sergey I., Broer, Ria, Cacciari, Roberto, Calio, Paul B., Carlson, Rebecca K., Carvalho Couto, Rafael, Cerdán, L., Chibotaru, Liviu F., Chilton, Nicholas F., Church, Jonathan Richard, Conti, Irene, Coriani, Sonia, Cuéllar-Zuquin, Juliana, Daoud, Razan E., Dattani, Nike, Decleva, Piero, de Graaf, Coen, Delcey, Mickaël G., De Vico, Luca, Dobrautz, Werner, Dong, Sijia S., Feng, Rulin, Ferré, Nicolas, Filatov Gulak, Michael, Gagliardi, Laura, Garavelli, Marco, González, Leticia, Guan, Yafu, Guo, Meiyuan, Hennefarth, Matthew R., Hermes, Matthew R., Hoyer, Chad E., Huix-Rotllant, Miquel, Jaiswal, Vishal Kumar, Kaiser, Andy, Kaliakin, Danil S., Khamesian, Marjan, King, Daniel S., Kochetov, Vladislav, Krośnicki, Marek, Kumaar, Arpit Arun, Larsson, Ernst D., Lehtola, Susi, Lepetit, Marie-Bernadette, Lischka, Hans, López Ríos, Pablo, Lundberg, Marcus, Ma, Dongxia, Mai, Sebastian, Marquetand, Philipp, Merritt, Isabella C. D., Montorsi, Francesco, Mörchen, Maximilian, Nenov, Artur, Nguyen, Vu Ha Anh, Nishimoto, Yoshio, Oakley, Meagan S., Olivucci, Massimo, Oppel, Markus, Padula, Daniele, Pandharkar, Riddhish, Phung, Quan Manh, Plasser, Felix, Raggi, Gerardo, Rebolini, Elisa, Reiher, Markus, Rivalta, Ivan, Roca-Sanjuán, Daniel, Romig, Thies, Safari, Arta Anushirwan, Sánchez-Mansilla, Aitor, Sand, Andrew M., Schapiro, Igor, Scott, Thais R., Segarra-Martí, Javier, Segatta, Francesco, Sergentu, Dumitru-Claudiu, Sharma, Prachi, Shepard, Ron, Shu, Yinan, Staab, Jakob K., Straatsma, Tjerk P., Sørensen, Lasse Kragh, Tenorio, Bruno Nunes Cabral, Truhlar, Donald G., Ungur, Liviu, Vacher, Morgane, Veryazov, Valera, Voß, Torben Arne, Weser, Oskar, Wu, Dihua, Yang, Xuchun, Yarkony, David, Zhou, Chen, Zobel, J Patrick, and Lindh, Roland

Abstract

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.

Published
2023

10. Supervised learning of a chemistry functional with damped dispersion

Author

Yiwei, Liu, Cheng, Zhang, Liu, Zhonghua, Truhlar, Donald G., Wang, Ying, and He, Xiao

Subjects
Computer Networks and Communications, Computer Science (miscellaneous), Computer Science Applications
Abstract

Kohn–Sham density functional theory is widely used in chemistry, but no functional can accurately predict the whole range of chemical properties, although recent progress by some doubly hybrid functionals comes close. Here, we optimized a singly hybrid functional called CF22D with higher across-the-board accuracy for chemistry than most of the existing non-doubly hybrid functionals by using a flexible functional form that combines a global hybrid meta-nonseparable gradient approximation that depends on density and occupied orbitals with a damped dispersion term that depends on geometry. We optimized this energy functional by using a large database and performance-triggered iterative supervised training. We combined several databases to create a very large, combined database whose use demonstrated the good performance of CF22D on barrier heights, isomerization energies, thermochemistry, noncovalent interactions, radical and nonradical chemistry, small and large systems, simple and complex systems and transition-metal chemistry.

Published
2022

12. Parametrically Managed Activation Functions for Improved Global Potential Energy Surfaces for Six Coupled 5A′ States and Fourteen Coupled 3A′ States of O + O2

Author

Shu, Yinan, Akher, Farideh Badichi, Guo, Hua, and Truhlar, Donald G.

Abstract

We report new potential energy surfaces for six coupled 5A′ states and 14 coupled 3A′ states of O3. The new surfaces are created by parametrically managed diabatization by deep neural network (PM-DDNN). The PM-DDNN method uses calculated adiabatic potential energy surfaces to discover and fit an underlying adiabatic-equivalent set of diabatic surfaces and their couplings and obtains the fit to the adiabatic surfaces by diagonalization of the diabatic potential energy matrix (DPEM). The procedure yields the adiabatic surfaces and their gradients, as well as the DPEM and its gradient. If desired one can also compute the nonadiabatic coupling due to the transformation. The present work improves on previous work by using a new coordinate to guide the decay of the neural network contribution to the many-body fit to the whole DPEM. The main objective was to obtain smoother potentials than the previous ones with better suitability for dynamics calculations, and this was achieved. Furthermore, we obtained suitably small deviations from the input reference data. For the six coupled 5A′ surfaces, the 60,366 data below 10 eV are fit with a mean unsigned error (MUE) of 49 meV, and for the 14 coupled 3A′ surfaces, the 76,733 data below 10 eV are fit with an MUE of 28 meV. The data below 5 eV fit even more accurately with MUEs of 37 meV (5A′) and 20 meV (3A′).

Published
2024
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14. Automatically Constructed and Selected Active Spaces with Multiconfigurational Pair-Density Functional Theory

Author

King, Daniel S., Truhlar, Donald G., and Gagliardi, Laura

Abstract

Contains supporting information for "Automatically Constructed and Selected Active Spaces with Multiconfigurational Pair-Density Functional Theory". Orbitals and density matrices for converged DMRG calculations with example scripts. Read "README.txt" for example code.

Published
2023
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17. Elucidating the photodissociation fingerprint and quantifying the determination of organic hydroperoxides in gas-phase autoxidation

Author

Hu, Zhihong, primary, Di, Qimei, additional, Liu, Bingzhi, additional, Li, Yanbo, additional, He, Yunrui, additional, Zhu, Qingbo, additional, Xu, Qiang, additional, Dagaut, Philippe, additional, Hansen, Nils, additional, Sarathy, S. Mani, additional, Xing, Lili, additional, Truhlar, Donald G., additional, and Wang, Zhandong, additional

Published
2023
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19. The OpenMolcas Web : A Community-Driven Approach to Advancing Computational Chemistry

Author

Manni, Giovanni Li, Fernández Galván, Ignacio, Alavi, Ali, Aleotti, Flavia, Aquilante, Francesco, Autschbach, Jochen, Avagliano, Davide, Baiardi, Alberto, Bao, Jie J., Battaglia, Stefano, Birnoschi, Letitia, Blanco-Gonzalez, Alejandro, Bokarev, Sergey I., Broer, Ria, Cacciari, Roberto, Calio, Paul B., Carlson, Rebecca K., Couto, Rafael Carvalho, Cerdan, Luis, Chibotaru, Liviu F., Chilton, Nicholas F., Church, Jonathan Richard, Conti, Irene, Coriani, Sonia, Cuellar-Zuquin, Juliana, Daoud, Razan E., Dattani, Nike, Decleva, Piero, de Graaf, Coen, Delcey, Mickael G., De Vico, Luca, Dobrautz, Werner, Dong, Sijia S., Feng, Rulin, Ferre, Nicolas, Filatov (Gulak), Michael, Gagliardi, Laura, Garavelli, Marco, Gonzalez, Leticia, Guan, Yafu, Guo, Meiyuan, Hennefarth, Matthew R., Hermes, Matthew R., Hoyer, Chad E., Huix-Rotllant, Miquel, Jaiswal, Vishal Kumar, Kaiser, Andy, Kaliakin, Danil S., Khamesian, Marjan, King, Daniel S., Kochetov, Vladislav, Krosnicki, Marek, Kumaar, Arpit Arun, Larsson, Ernst D., Lehtola, Susi, Lepetit, Marie-Bernadette, Lischka, Hans, Rios, Pablo Lopez, Lundberg, Marcus, Ma, Dongxia, Mai, Sebastian, Marquetand, Philipp, Merritt, Isabella C. D., Montorsi, Francesco, Morchen, Maximilian, Nenov, Artur, Nguyen, Vu Ha Anh, Nishimoto, Yoshio, Oakley, Meagan S., Olivucci, Massimo, Oppel, Markus, Padula, Daniele, Pandharkar, Riddhish, Phung, Quan Manh, Plasser, Felix, Raggi, Gerardo, Rebolini, Elisa, Reiher, Markus, Rivalta, Ivan, Roca-Sanjuan, Daniel, Romig, Thies, Safari, Arta Anushirwan, Sanchez-Mansilla, Aitor, Sand, Andrew M., Schapiro, Igor, Scott, Thais R., Segarra-Marti, Javier, Segatta, Francesco, Sergentu, Dumitru-Claudiu, Sharma, Prachi, Shepard, Ron, Shu, Yinan, Staab, Jakob K., Straatsma, Tjerk P., Sorensen, Lasse Kragh, Tenorio, Bruno Nunes Cabral, Truhlar, Donald G., Ungur, Liviu, Vacher, Morgane, Veryazov, Valera, Voss, Torben Arne, Weser, Oskar, Wu, Dihua, Yang, Xuchun, Yarkony, David, Zhou, Chen, Zobel, J. Patrick, Lindh, Roland, Manni, Giovanni Li, Fernández Galván, Ignacio, Alavi, Ali, Aleotti, Flavia, Aquilante, Francesco, Autschbach, Jochen, Avagliano, Davide, Baiardi, Alberto, Bao, Jie J., Battaglia, Stefano, Birnoschi, Letitia, Blanco-Gonzalez, Alejandro, Bokarev, Sergey I., Broer, Ria, Cacciari, Roberto, Calio, Paul B., Carlson, Rebecca K., Couto, Rafael Carvalho, Cerdan, Luis, Chibotaru, Liviu F., Chilton, Nicholas F., Church, Jonathan Richard, Conti, Irene, Coriani, Sonia, Cuellar-Zuquin, Juliana, Daoud, Razan E., Dattani, Nike, Decleva, Piero, de Graaf, Coen, Delcey, Mickael G., De Vico, Luca, Dobrautz, Werner, Dong, Sijia S., Feng, Rulin, Ferre, Nicolas, Filatov (Gulak), Michael, Gagliardi, Laura, Garavelli, Marco, Gonzalez, Leticia, Guan, Yafu, Guo, Meiyuan, Hennefarth, Matthew R., Hermes, Matthew R., Hoyer, Chad E., Huix-Rotllant, Miquel, Jaiswal, Vishal Kumar, Kaiser, Andy, Kaliakin, Danil S., Khamesian, Marjan, King, Daniel S., Kochetov, Vladislav, Krosnicki, Marek, Kumaar, Arpit Arun, Larsson, Ernst D., Lehtola, Susi, Lepetit, Marie-Bernadette, Lischka, Hans, Rios, Pablo Lopez, Lundberg, Marcus, Ma, Dongxia, Mai, Sebastian, Marquetand, Philipp, Merritt, Isabella C. D., Montorsi, Francesco, Morchen, Maximilian, Nenov, Artur, Nguyen, Vu Ha Anh, Nishimoto, Yoshio, Oakley, Meagan S., Olivucci, Massimo, Oppel, Markus, Padula, Daniele, Pandharkar, Riddhish, Phung, Quan Manh, Plasser, Felix, Raggi, Gerardo, Rebolini, Elisa, Reiher, Markus, Rivalta, Ivan, Roca-Sanjuan, Daniel, Romig, Thies, Safari, Arta Anushirwan, Sanchez-Mansilla, Aitor, Sand, Andrew M., Schapiro, Igor, Scott, Thais R., Segarra-Marti, Javier, Segatta, Francesco, Sergentu, Dumitru-Claudiu, Sharma, Prachi, Shepard, Ron, Shu, Yinan, Staab, Jakob K., Straatsma, Tjerk P., Sorensen, Lasse Kragh, Tenorio, Bruno Nunes Cabral, Truhlar, Donald G., Ungur, Liviu, Vacher, Morgane, Veryazov, Valera, Voss, Torben Arne, Weser, Oskar, Wu, Dihua, Yang, Xuchun, Yarkony, David, Zhou, Chen, Zobel, J. Patrick, and Lindh, Roland

Abstract

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.

Published
2023
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20. The OpenMolcas Web:A Community-Driven Approach to Advancing Computational Chemistry

Author

Li Manni, Giovanni, Fdez. Galván, Ignacio, Alavi, Ali, Aleotti, Flavia, Aquilante, Francesco, Autschbach, Jochen, Avagliano, Davide, Baiardi, Alberto, Bao, Jie J., Battaglia, Stefano, Birnoschi, Letitia, Blanco-González, Alejandro, Bokarev, Sergey I., Broer, Ria, Cacciari, Roberto, Calio, Paul B., Carlson, Rebecca K., Carvalho Couto, Rafael, Cerdán, Luis, Chibotaru, Liviu F., Chilton, Nicholas F., Church, Jonathan Richard, Conti, Irene, Coriani, Sonia, Cuéllar-Zuquin, Juliana, Daoud, Razan E., Dattani, Nike, Decleva, Piero, de Graaf, Coen, Delcey, Mickaël G., De Vico, Luca, Dobrautz, Werner, Dong, Sijia S., Feng, Rulin, Ferré, Nicolas, Filatov(Gulak), Michael, Gagliardi, Laura, Garavelli, Marco, González, Leticia, Guan, Yafu, Guo, Meiyuan, Hennefarth, Matthew R., Hermes, Matthew R., Hoyer, Chad E., Huix-Rotllant, Miquel, Jaiswal, Vishal Kumar, Kaiser, Andy, Kaliakin, Danil S., Khamesian, Marjan, King, Daniel S., Kochetov, Vladislav, Krośnicki, Marek, Kumaar, Arpit Arun, Larsson, Ernst D., Lehtola, Susi, Lepetit, Marie Bernadette, Lischka, Hans, López Ríos, Pablo, Lundberg, Marcus, Ma, Dongxia, Mai, Sebastian, Marquetand, Philipp, Merritt, Isabella C.D., Montorsi, Francesco, Mörchen, Maximilian, Nenov, Artur, Nguyen, Vu Ha Anh, Nishimoto, Yoshio, Oakley, Meagan S., Olivucci, Massimo, Oppel, Markus, Padula, Daniele, Pandharkar, Riddhish, Phung, Quan Manh, Plasser, Felix, Raggi, Gerardo, Rebolini, Elisa, Reiher, Markus, Rivalta, Ivan, Roca-Sanjuán, Daniel, Romig, Thies, Safari, Arta Anushirwan, Sánchez-Mansilla, Aitor, Sand, Andrew M., Schapiro, Igor, Scott, Thais R., Segarra-Martí, Javier, Segatta, Francesco, Sergentu, Dumitru Claudiu, Sharma, Prachi, Shepard, Ron, Shu, Yinan, Staab, Jakob K., Straatsma, Tjerk P., Sørensen, Lasse Kragh, Tenorio, Bruno Nunes Cabral, Truhlar, Donald G., Ungur, Liviu, Vacher, Morgane, Veryazov, Valera, Voß, Torben Arne, Weser, Oskar, Wu, Dihua, Yang, Xuchun, Yarkony, David, Zhou, Chen, Zobel, J. Patrick, Lindh, Roland, Li Manni, Giovanni, Fdez. Galván, Ignacio, Alavi, Ali, Aleotti, Flavia, Aquilante, Francesco, Autschbach, Jochen, Avagliano, Davide, Baiardi, Alberto, Bao, Jie J., Battaglia, Stefano, Birnoschi, Letitia, Blanco-González, Alejandro, Bokarev, Sergey I., Broer, Ria, Cacciari, Roberto, Calio, Paul B., Carlson, Rebecca K., Carvalho Couto, Rafael, Cerdán, Luis, Chibotaru, Liviu F., Chilton, Nicholas F., Church, Jonathan Richard, Conti, Irene, Coriani, Sonia, Cuéllar-Zuquin, Juliana, Daoud, Razan E., Dattani, Nike, Decleva, Piero, de Graaf, Coen, Delcey, Mickaël G., De Vico, Luca, Dobrautz, Werner, Dong, Sijia S., Feng, Rulin, Ferré, Nicolas, Filatov(Gulak), Michael, Gagliardi, Laura, Garavelli, Marco, González, Leticia, Guan, Yafu, Guo, Meiyuan, Hennefarth, Matthew R., Hermes, Matthew R., Hoyer, Chad E., Huix-Rotllant, Miquel, Jaiswal, Vishal Kumar, Kaiser, Andy, Kaliakin, Danil S., Khamesian, Marjan, King, Daniel S., Kochetov, Vladislav, Krośnicki, Marek, Kumaar, Arpit Arun, Larsson, Ernst D., Lehtola, Susi, Lepetit, Marie Bernadette, Lischka, Hans, López Ríos, Pablo, Lundberg, Marcus, Ma, Dongxia, Mai, Sebastian, Marquetand, Philipp, Merritt, Isabella C.D., Montorsi, Francesco, Mörchen, Maximilian, Nenov, Artur, Nguyen, Vu Ha Anh, Nishimoto, Yoshio, Oakley, Meagan S., Olivucci, Massimo, Oppel, Markus, Padula, Daniele, Pandharkar, Riddhish, Phung, Quan Manh, Plasser, Felix, Raggi, Gerardo, Rebolini, Elisa, Reiher, Markus, Rivalta, Ivan, Roca-Sanjuán, Daniel, Romig, Thies, Safari, Arta Anushirwan, Sánchez-Mansilla, Aitor, Sand, Andrew M., Schapiro, Igor, Scott, Thais R., Segarra-Martí, Javier, Segatta, Francesco, Sergentu, Dumitru Claudiu, Sharma, Prachi, Shepard, Ron, Shu, Yinan, Staab, Jakob K., Straatsma, Tjerk P., Sørensen, Lasse Kragh, Tenorio, Bruno Nunes Cabral, Truhlar, Donald G., Ungur, Liviu, Vacher, Morgane, Veryazov, Valera, Voß, Torben Arne, Weser, Oskar, Wu, Dihua, Yang, Xuchun, Yarkony, David, Zhou, Chen, Zobel, J. Patrick, and Lindh, Roland

Abstract

The developments of the open-source OpenMolcas chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes OpenMolcas can address, while showing that OpenMolcas is an attractive platform for state-of-the-art atomistic computer simulations.

Published
2023

28. Analytic gradients for compressed multistate pair-density functional theory

Author

Bao, Jie J., Hermes, Matthew R., Scott, Thais R., Sand, Andrew M., Lindh, Roland, Gagliardi, Laura, Truhlar, Donald G., Bao, Jie J., Hermes, Matthew R., Scott, Thais R., Sand, Andrew M., Lindh, Roland, Gagliardi, Laura, and Truhlar, Donald G.

Abstract

Photochemical reactions often involve states that are closely coupled due to near degeneracies, for example by proximity to conical intersections. Therefore, a multistate method is used to accurately describe these states; for example, ordinary perturbation theory is replaced by quasidegenerate perturbation theory. Multiconfiguration pair-density functional theory (MC-PDFT) provides an efficient way to approximate the full dynamical correlation energy of strongly correlated systems, and we recently proposed compressed multistate pair-density functional theory (CMS-PDFT) to treat closely coupled states. In the present paper, we report the implementation of analytic gradients for CMS-PDFT in both OpenMolcas and PySCF, and we illustrate the use of these gradients by applying the method to the excited states of formaldehyde and phenol.

Published
2022
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30. Elucidating the photodissociation fingerprint and quantifying the determination of organic hydroperoxides in gas-phase autoxidation.

Author

Zhihong Hu, Qimei Di, Bingzhi Liu, Yanbo Li, Yunrui He, Qingbo Zhu, Qiang Xu, Dagaut, Philippe, Hansen, Nils, Sarathy, S. Mani, Lili Xing, Truhlar, Donald G., and Zhandong Wang

Subjects
OXIDATION, HYDROPEROXIDES, PHOTODISSOCIATION, CHEMICAL reactions, CHEMICAL models, FLUIDIZED-bed combustion
Abstract

Hydroperoxides are formed in the atmospheric oxidation of volatile organic compounds, in the combustion autoxidation of fuel, in the cold environment of the interstellar medium, and also in some catalytic reactions. They play crucial roles in the formation and aging of secondary organic aerosols and in fuel autoignition. However, the concentration of organic hydroperoxides is seldom measured, and typical estimates have large uncertainties. In this work, we developed a mild and environmental- friendly method for the synthesis of alkyl hydroperoxides (ROOH) with various structures, and we systematically measured the absolute photoionization cross-sections (PICSs) of the ROOHs using synchrotron vacuum ultraviolet-photoionization mass spectrometry (SVUV-PIMS). A chemical titration method was combined with an SVUV-PIMS measurement to obtain the PICS of 4-hydroperoxy-2-pentanone, a typical molecule for combustion and atmospheric autoxidation ketohydroperoxides (KHPs). We found that organic hydroperoxide cations are largely dissociated by loss of OOH. This fingerprint was used for the identification and accurate quantification of the organic peroxides, and it can therefore be used to improve models for autoxidation chemistry. The synthesis method and photoionization dataset for organic hydroperoxides are useful for studying the chemistry of hydroperoxides and the reaction kinetics of the hydroperoxy radicals and for developing and evaluating kinetic models for the atmospheric autoxidation and combustion autoxidation of the organic compounds. [ABSTRACT FROM AUTHOR]

Published
2023
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31. Diabatic States of Molecules

Author

Shu, Yinan, primary, Varga, Zoltan, additional, Kanchanakungwankul, Siriluk, additional, Zhang, Linyao, additional, and Truhlar, Donald G., additional

Published
2022
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32. Electronic structure of strongly correlated systems: recent developments in multiconfiguration pair-density functional theory and multiconfiguration nonclassical-energy functional theory

Author

Zhou, Chen, primary, Hermes, Matthew R., additional, Wu, Dihua, additional, Bao, Jie J., additional, Pandharkar, Riddhish, additional, King, Daniel S., additional, Zhang, Dayou, additional, Scott, Thais R., additional, Lykhin, Aleksandr O., additional, Gagliardi, Laura, additional, and Truhlar, Donald G., additional

Published
2022
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34. Parametrically Managed Activation Function for Fitting a Neural Network Potential with Physical Behavior Enforced by a Low-Dimensional Potential

Author

Akher, Farideh Badichi, Shu, Yinan, Varga, Zoltan, Bhaumik, Suman, and Truhlar, Donald G.

Abstract

Machine-learned representations of potential energy surfaces generated in the output layer of a feedforward neural network are becoming increasingly popular. One difficulty with neural network output is that it is often unreliable in regions where training data is missing or sparse. Human-designed potentials often build in proper extrapolation behavior by choice of functional form. Because machine learning is very efficient, it is desirable to learn how to add human intelligence to machine-learned potentials in a convenient way. One example is the well-understood feature of interaction potentials that they vanish when subsystems are too far separated to interact. In this article, we present a way to add a new kind of activation function to a neural network to enforce low-dimensional constraints. In particular, the activation function depends parametrically on all of the input variables. We illustrate the use of this step by showing how it can force an interaction potential to go to zero at large subsystem separations without either inputting a specific functional form for the potential or adding data to the training set in the asymptotic region of geometries where the subsystems are separated. In the process of illustrating this, we present an improved set of potential energy surfaces for the 14 lowest 3A′ states of O3. The method is more general than this example, and it may be used to add other low-dimensional knowledge or lower-level knowledge to machine-learned potentials. In addition to the O3example, we present a greater-generality method called parametrically managed diabatization by deep neural network (PM-DDNN) that is an improvement on our previously presented permutationally restrained diabatization by deep neural network (PR-DDNN).

Published
2024
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35. Multiconfiguration Pair-Density Functional Theory and Complete Active Space Second Order Perturbation Theory. Bond Dissociation Energies of FeC, NiC, FeS, NiS, FeSe, and NiSe

Author

Sharkas, Kamal, Gagliardi, Laura, and Truhlar, Donald G.

Abstract

We investigate the performance of multiconfiguration pair-density functional theory (MC-PDFT) and complete active space second-order perturbation theory for computing the bond dissociation energies of the diatomic molecules FeC, NiC, FeS, NiS, FeSe, and NiSe, for which accurate experimental data have become recently available [Matthew, D. J.; Tieu, E.; Morse, M. D. J. Chem. Phys. 2017, 146, 144310–144320]. We use three correlated participating orbital (CPO) schemes (nominal, moderate, and extended) to define the active spaces, and we consider both the complete active space (CAS) and the separated-pair (SP) schemes to specify the configurations included for a given active space. We found that the moderate SP-PDFT scheme with the tPBE on-top density functional has the smallest mean unsigned error (MUE) of the methods considered. This level of theory provides a balanced treatment of the static and dynamic correlation energies for the studied systems. This is encouraging because the method is low in cost even for much more complicated systems.

Published
2024
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36. Small Representative Databases for Testing and Validating Density Functionals and Other Electronic Structure Methods

Author

Shu, Yinan, Zhu, Zhaohan, Kanchanakungwankul, Siriluk, and Truhlar, Donald G.

Abstract

Broad and diverse sets of accurate data provide useful metrics for assessing the performance of new theoretical methods. However, assessing methods against large databases can be an arduous task. Here, we present 17 representative energetic databases, defined as small databases whose errors and error spreads are representative of larger databases and which therefore can serve as efficient benchmarks for developing and testing electronic structure methods and density functionals. In 15 cases, the representative databases have 6 entries while being representative of larger databases with 14–107 entries, and in the other two cases, they have 14 entries while being representative of larger databases with 418–455 entries. The mean unsigned error (MUE) of 100 electronic structure methods on a given representative database is typically within about 8% of the MUE on its parent database, and the root-mean-square error (RMSE) is typically within about 11% of the RMSE on the parent database. Thus, the representative databases are quite successful in indicating accuracy while maintaining good diversity. The databases include both main-group and transition-metal compounds and reactions, and they include bond energies, reaction energies, barrier heights, noncovalent interactions, ionization potentials, and absolute energies.

Published
2024
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37. Hyper Open-Shell Excited Spin States of Transition-Metal Compounds: FeF2, FeF2···Ethane, and FeF2···Ethylene

Author

Verma, Pragya, Varga, Zoltan, and Truhlar, Donald G.

Abstract

Spin-state energetics are important for understanding properties that involve more than one spin state, for example, catalysis occurring on two or more potential energy surfaces corresponding to different electronic spins. Very often, multiple-spin processes involve transition-metal compounds, and therefore, it is important to understand the electronic structure and energetics of such compounds in different spin states. In this work, we benchmark relative spin-state energies of FeF2with respect to the quintet ground spin state using both single-configurational and multiconfigurational methods, and we examine how they are affected by the binding of ethane and ethylene to the iron center. We also benchmark the binding energies of the complexes. The single-configurational methods used in this work are the Hartree–Fock method, 32 exchange–correlation functionals, and the CCSD(T) coupled-cluster method in both restricted and unrestricted formalisms. The multiconfigurational methods that have been used are CASSCF, CASPT2, CASPT3, MRCI, MRCI+Q, and MR-ACPF. The spin-state splitting energies depend on the functional chosen, and of the 32 exchange–correlation functionals investigated here, we find that for the septet and spin-projected triplet states of FeF2the M06 functional is the best when compared to our best estimates from multireference calculations. If all nine excitation energies are considered, where there are three excited spin states (singlet, triplet, and septet) for each of the three systems (FeF2, FeF2···ethane, and FeF2···ethylene), the three best-performing functionals are HLE16, SOGGA11-X, and M06-2X. We find that the binding of ethane perturbs the relative spin-state energy of FeF2by only a small amount, but the stronger binding of ethylene has a larger effect. For the spin-state splitting energies of FeF2using single-reference CCSD(T), we find that the predicted results depend very strongly on precisely how the calculations are done, in particular, on the spin-restricted or spin-unrestricted character of the SCF reference state, which can differ even by around 50 kcal/mol for the SCF reference state and the subsequent CCSD(T) calculations. Upon analyzing the wave functions of both the spin-restricted or spin-unrestricted formalisms, we find that the lowest-energy singlet and triplet states of the complexes, just like FeF2in isolation, can have more unpaired electrons than they are usually assumed to have, i.e., they can be hyper open-shell electronic configurations, and this can significantly lower the energy.

Published
2024
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38. Dual-Functional Tamm–Dancoff Approximation with Self-Interaction-Free Orbitals: Vertical Excitation Energies and Potential Energy Surfaces near an Intersection Seam

Author

Shu, Yinan, Parker, Kelsey A., and Truhlar, Donald G.

Abstract

Recently we have developed the dual-functional Tamm–Dancoff approximation (DF-TDA) method. DF-TDA is an alternative to linear-response time-dependent density functional theory (LR-TDDFT) with the advantage of providing a correct double-cone topology of S1/S0conical intersections. In the DF-TDA method, we employ different functionals, which are denoted G and F, for orbital optimization and Hamiltonian construction. We use the notation DF-TDA/G:F. In the current work, we propose that G be the same as F except for having 100% Hartree–Fock exchange. We use the notation F100 to denote functional F with this modification. A motivation for this is that functionals with 100% Hartree–Fock exchange are one-electron self-interaction-free. Here we validate the use of F100/M06 to compute vertical excitation energies and the global potential energy surface of ammonia near a conical intersection to further validate the F100 method for photochemical problems.

Published
2024
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39. Computational Design of Functionalized Metal–Organic Framework Nodes for Catalysis

Author

Bernales, Varinia, Ortuño, Manuel A., Truhlar, Donald G., Cramer, Christopher J., and Gagliardi, Laura

Abstract

Recent progress in the synthesis and characterization of metal–organic frameworks (MOFs) has opened the door to an increasing number of possible catalytic applications. The great versatility of MOFs creates a large chemical space, whose thorough experimental examination becomes practically impossible. Therefore, computational modeling is a key tool to support, rationalize, and guide experimental efforts. In this outlook we survey the main methodologies employed to model MOFs for catalysis, and we review selected recent studies on the functionalization of their nodes. We pay special attention to catalytic applications involving natural gas conversion.

Published
2024
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40. Parametrically Managed Activation Functions for Improved Global Potential Energy Surfaces for Six Coupled 5 A ' States and Fourteen Coupled 3 A ' States of O + O 2 .

Author

Shu Y, Akher FB, Guo H, and Truhlar DG

Abstract

We report new potential energy surfaces for six coupled 5 A ' states and 14 coupled 3 A ' states of O 3 . The new surfaces are created by parametrically managed diabatization by deep neural network (PM-DDNN). The PM-DDNN method uses calculated adiabatic potential energy surfaces to discover and fit an underlying adiabatic-equivalent set of diabatic surfaces and their couplings and obtains the fit to the adiabatic surfaces by diagonalization of the diabatic potential energy matrix (DPEM). The procedure yields the adiabatic surfaces and their gradients, as well as the DPEM and its gradient. If desired one can also compute the nonadiabatic coupling due to the transformation. The present work improves on previous work by using a new coordinate to guide the decay of the neural network contribution to the many-body fit to the whole DPEM. The main objective was to obtain smoother potentials than the previous ones with better suitability for dynamics calculations, and this was achieved. Furthermore, we obtained suitably small deviations from the input reference data. For the six coupled 5 A ' surfaces, the 60,366 data below 10 eV are fit with a mean unsigned error (MUE) of 49 meV, and for the 14 coupled 3 A ' surfaces, the 76,733 data below 10 eV are fit with an MUE of 28 meV. The data below 5 eV fit even more accurately with MUEs of 37 meV ( 5 A ') and 20 meV ( 3 A ').

Published
2024
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41. Unimolecular Reactions of E -Glycolaldehyde Oxide and Its Reactions with One and Two Water Molecules.

Author

Sun Y, Long B, and Truhlar DG

Abstract

The kinetics of Criegee intermediates are important for atmospheric modeling. However, the quantitative kinetics of Criegee intermediates are still very limited, especially for those with hydroxy groups. Here, we calculate rate constants for the unimolecular reaction of E -glycolaldehyde oxide [ E -hydroxyethanal oxide, E -(CH 2 OH)CHOO], for its reactions with H 2 O and (H 2 O) 2 , and for the reaction of the E -(CH 2 OH)CHOO…H 2 O complex with H 2 O. For the highest level of electronic structure, we use W3X-L//CCSD(T)-F12a/cc-pVDZ-F12 for the unimolecular reaction and the reaction with water and W3X-L//DF-CCSD(T)-F12b/jun-cc-pVDZ for the reaction with 2 water molecules. For the dynamics, we use a dual-level strategy that combines conventional transition state theory with the highest level of electronic structure and multistructural canonical variational transition state theory with small-curvature tunneling with a validated density functional for the electronic structure. This dynamical treatment includes high-frequency anharmonicity, torsional anharmonicity, recrossing effects, and tunneling. We find that the unimolecular reaction of E -(CH 2 OH)CHOO depends on both temperature and pressure. The calculated results show that E -(CH 2 OH)CHOO…H 2 O + H 2 O is the dominant entrance channel, while previous investigations only considered Criegee intermediates + (H 2 O) 2 . In addition, we find that the atmospheric lifetime of E -(CH 2 OH)CHOO with respect to 2 water molecules is particularly short with a value of 1.71 × 10 -6 s at 0 km, which is about 2 orders of magnitude shorter than those usually assumed for Criegee intermediate reactions with water dimer. We also find that the OH group in E -(CH 2 OH)CHOO enhances its reactivity., (Copyright © 2023 Yan Sun et al.)

Published
2023
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42. Chemical Bonding in Isoelectronic NdO 2 and SmO 2 2 .

Author

Ning J and Truhlar DG

Abstract

Neodymium dioxide (NdO 2 ) and samarium dioxide cation (SmO 2 2+ ) are isoelectronic molecules. Here we used calculations of the spin-orbit-free wave functions to study and compare their geometries, spin states, and bonding. We used Kohn-Sham density functional theory with the B97-1 exchange-correlation functional to optimize the geometries and found that the two molecules have different ground spin states and structures. NdO 2 favors a linear ONdO triplet structure, and SmO 2 2+ favors a linear SmOO 2+ quintet structure. We then used state-averaged complete-active-space self-consistent-field (SA-CASSCF) calculations to investigate the bonding characteristics of NdO 2 and SmO 2 2+ in various geometries. We found that in NdOO, one electron is transferred from Nd to O, while in SmO 2 2+ , there is no electron transfer between Sm and O. The SA-CASSCF calculation also shows that ONdO has a stronger bonding orbital between a 4f orbital of Nd and a p z orbital of oxygen atoms. We compared three multireference methods, namely, extended multistate complete active space second-order perturbation theory (XMS-CASPT2), extended multistate pair-density functional theory (XMS-PDFT), and compressed multistate pair-density functional theory (CMS-PDFT), for calculating the spin-orbit-free energies of various isomers of both molecules. We found that although XMS-PDFT and CMS-PDFT are at the same cost level as SA-CASSCF, they give results with the same accuracy as given for the much more demanding XMS-CASPT2 calculation. Between the two multistate PDFT methods, CMS-PDFT is better at giving good degeneracies for states that should be degenerate.

Published
2023
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43. Reactive and Nonreactive Collisions between NO(X 2 Π) and O( 3 P) under Hyperthermal Conditions.

Author

Lu D, Truhlar DG, and Guo H

Abstract

Quasiclassical trajectory calculations are performed for hyperthermal collisions between NO(X 2 Π) and O( 3 P) on recently developed potential energy surfaces for the lowest doublet and quartet states of the NO 2 system. Three product channels are investigated, and their branching fractions are in reasonably good agreement with the recent crossed molecular beam study at 84 kcal/mol of collision energy. The dominant inelastic channel has a strong forward scattering bias and a high translational energy distribution with limited internal excitation in the scattered NO. The exchange channel has significantly higher NO internal excitation and is also forward biased. The abstraction channel producing internally excited O 2 has the smallest branching fraction and a broader angular distribution also with a forward peak. The angular and translational energy distributions in the three channels are consistent with experiment, but the agreement is not always quantitative. The sources of the differences are discussed. Finally, the impact of NO vibrational excitation on the reactive channels and the corresponding rate coefficients are reported.

Published
2022
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