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44 results on '"Guan, Yafu"'

1. Accurate diabatization based on combined-hyperbolic-inverse-power-representation: 1,2 2A′ states of BeH2+.

Author

Guan, Yafu, Chen, Qun, and Varandas, António J. C.

Subjects
*SPIN-orbit interactions, *QUANTUM scattering, *POTENTIAL energy, *TOPOLOGY, *PERMUTATIONS
Abstract

A diabatic potential energy matrix (DPEM) for the two lowest states of BeH 2 + has been constructed using the combined-hyperbolic-inverse-power-representation (CHIPR) method. By imposing symmetry constraints on the coefficients of polynomials, the complete nuclear permutation inversion symmetry is correctly preserved in the CHIPR functional form. The symmetrized CHIPR functional form is then used in the diabatization by ansatz procedure. The ab initio energies are reproduced with satisfactory accuracy. In addition, the CHIPR-based DPEM also reproduces the local topology of a conical intersection. Future work will focus on a complete four-state diabatic representation with emphasis on the long-range interactions and spin–orbit couplings, which will enable accurate quantum scattering calculations for the Be+(2P) + H2 → BeH+(X1Σ+) + H(2S) reaction. [ABSTRACT FROM AUTHOR]

Published
2024
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4. Permutation invariant polynomial neural network based diabatic ansatz for the (E + A) × (e + a) Jahn–Teller and Pseudo-Jahn–Teller systems.

Author

Guan, Yafu, Yarkony, David R., and Zhang, Dong H.

Subjects
*POLYNOMIALS, *PERMUTATIONS, *ELECTRONIC structure, *TOPOLOGY, *SYMMETRY
Abstract

In this work, the permutation invariant polynomial neural network (PIP-NN) approach is employed to construct a quasi-diabatic Hamiltonian for system with non-Abelian symmetries. It provides a flexible and compact NN-based diabatic ansatz from the related approach of Williams, Eisfeld, and co-workers. The example of H 3 + is studied, which is an (E + A) × (e + a) Jahn–Teller and Pseudo-Jahn–Teller system. The PIP-NN diabatic ansatz is based on the symmetric polynomial expansion of Viel and Eisfeld, the coefficients of which are expressed with neural network functions that take permutation-invariant polynomials as input. This PIP-NN-based diabatic ansatz not only preserves the correct symmetry but also provides functional flexibility to accurately reproduce ab initio electronic structure data, thus resulting in excellent fits. The adiabatic energies, energy gradients, and derivative couplings are well reproduced. A good description of the local topology of the conical intersection seam is also achieved. Therefore, this diabatic ansatz completes the PIP-NN based representation of DPEM with correct symmetries and will enable us to diabatize even more complicated systems with complex symmetries. [ABSTRACT FROM AUTHOR]

Published
2022
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6. 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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7. 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

8. 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

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

Author

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

Published
2023
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10. Enabling complete multichannel nonadiabatic dynamics: A global representation of the two-channel coupled, 1,21A and 13A states of NH3 using neural networks.

Author

Wang, Yuchen, Guan, Yafu, Guo, Hua, and Yarkony, David R.

Subjects
*POTENTIAL energy surfaces, *SPIN-orbit interactions, *POTENTIAL energy, *COUPLES, *SURFACE properties
Abstract

Global coupled three-state two-channel potential energy and property/interaction (dipole and spin–orbit coupling) surfaces for the dissociation of NH3(Ã) into NH + H2 and NH2 + H are reported. The permutational invariant polynomial-neural network approach is used to simultaneously fit and diabatize the electronic Hamiltonian by fitting the energies, energy gradients, and derivative couplings of the two coupled lowest-lying singlet states as well as fitting the energy and energy gradients of the lowest-lying triplet state. The key issue in fitting property matrix elements in the diabatic basis is that the diabatic surfaces must be smooth, that is, the diabatization must remove spikes in the original adiabatic property surfaces attributable to the switch of electronic wavefunctions at the conical intersection seam. Here, we employ the fit potential energy matrix to transform properties in the adiabatic representation to a quasi-diabatic representation and remove the discontinuity near the conical intersection seam. The property matrix elements can then be fit with smooth neural network functions. The coupled potential energy surfaces along with the dipole and spin–orbit coupling surfaces will enable more accurate and complete treatment of optical transitions, as well as nonadiabatic internal conversion and intersystem crossing. [ABSTRACT FROM AUTHOR]

Published
2021
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12. Neural network based quasi-diabatic Hamiltonians with symmetry adaptation and a correct description of conical intersections.

Author

Guan, Yafu, Guo, Hua, and Yarkony, David R.

Subjects
*SYMMETRY, *ARTIFICIAL neural networks, *PHYSIOLOGICAL adaptation
Abstract

In a previous paper, we have demonstrated that artificial neural networks (NNs) can be used to generate quasidiabatic Hamiltonians (Hd) that are capable of representing adiabatic energies, energy gradients, and derivative couplings. In this work, two additional issues are addressed. First, symmetry-adapted functions such as permutation invariant polynomials are introduced to account for complete nuclear permutation inversion symmetry. Second, a partially diagonalized representation is introduced to facilitate a better description of near degeneracy points. The diabatization of 1, 21A states of NH3 is used as an example. The NN fitting results are compared to that of a previous fitting with symmetry adapted polynomials. [ABSTRACT FROM AUTHOR]

Published
2019
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20. Vibrational energy levels of the S0 and S1 states of formaldehyde using an accurate ab initio based global diabatic potential energy matrix.

Author

Xie, Changjian, Guan, Yafu, Yarkony, David R., and Guo, Hua

Subjects
*FORMALDEHYDE, *POTENTIAL energy, *POTENTIAL energy surfaces, *KINETIC energy
Abstract

Low-lying vibrational energy levels of both the ground (S0) and first excited singlet (S1) states of formaldehyde have been determined using an exact kinetic energy operator and adiabatic potential energy surfaces derived from a recently developed full-dimensional global diabatic potential energy matrix (DPEM) based on a neural network representation of high level ab initio data. The agreement with available experimental band origins provides strong evidence in support of the accuracy of the DPEM, which can be used in the future for understanding the internal conversion of H2CO(S1) and the subsequent roaming dynamics on its S0 state. [ABSTRACT FROM AUTHOR]

Published
2021
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26. A fundamental invariant-neural network representation of quasi-diabatic Hamiltonians for the two lowest states of H3.

Author

Yin, Zhengxi, Braams, Bastiaan J., Guan, Yafu, Fu, Bina, and Zhang, Dong H.

Abstract

The fundamental invariant neural network (FI-NN) approach is developed to represent coupled potential energy surfaces in quasidiabatic representations with two-dimensional irreducible representations of the complete nuclear permutation and inversion (CNPI) group. The particular symmetry properties of the diabatic potential energy matrix of H3 for the 1A′ and 2A′ electronic states were resolved arising from the E symmetry in the D3h point group. This FI-NN framework with symmetry adaption is used to construct a new quasidiabatic representation of H3, which reproduces accurately the ab initio energies and derivative information with perfect symmetry behaviors and extremely small fitting errors. The quantum dynamics results on the new FI-NN diabatic PESs give rise to accurate oscillation patterns in the product state-resolved differential cross sections. These results strongly support the accuracy and efficiency of the FI-NN approach to construct reliable diabatic representations with complicated symmetry problems. [ABSTRACT FROM AUTHOR]

Published
2021
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32. Neural Network Based Quasi-diabatic Representation for S0and S1States of Formaldehyde

Author

Guan, Yafu, Xie, Changjian, Guo, Hua, and Yarkony, David R.

Abstract

A neural network based quasi-diabatic potential energy matrix Hdthat describes the photodissociation of formaldehyde involving the two lowest singlet states S0and S1is constructed. It has strict complete nuclear permutation inversion symmetry encoded and can reproduce high level ab initioelectronic structure data, including energies, energy gradients, and derivative couplings, with excellent accuracy. It has been fully saturated in the configuration space to cover all possible reaction pathways with a trajectory-guided point sampling approach. This Hdwill not only enable the accurate full-dimensional dynamic simulations of the photodissociation of formaldehyde involving S0and S1but also provide a crucial ingredient for incorporating spin–orbit couplings into a diabatic framework, thus ultimately enabling the study of both internal conversion and intersystem crossing in formaldehyde on the same footing.

Published
2020
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33. Two-state diabatic potential energy surfaces of ClH2 based on nonadiabatic couplings with neural networks.

Author

Yin, Zhengxi, Guan, Yafu, Fu, Bina, and Zhang, Dong H.

Abstract

A general neural network (NN)-fitting procedure based on nonadiabatic couplings is proposed to generate coupled two-state diabatic potential energy surfaces (PESs) with conical intersections. The elements of the diabatic potential energy matrix (DPEM) can be obtained directly from a combination of the NN outputs in principle. Instead, to achieve higher accuracy, the adiabatic-to-diabatic transformation (ADT) angle (mixing angle) for each geometry is first solved from the NN outputs, followed by individual NN fittings of the three terms of the DPEM, which are calculated from the ab initio adiabatic energies and solved mixing angles. The procedure is applied to construct a new set of two-state diabatic potential energy surfaces of ClH2. The ab initio data including adiabatic energies and derivative couplings are well reproduced. Furthermore, the current diabatization procedure can describe well the vicinity of conical intersections in high potential energy regions, which are located in the T-shaped (C2v) structure of Cl–H2. The diabatic quantum dynamical results on diabatic PESs show large differences as compared with the adiabatic results in high collision energy regions, suggesting the significance of nonadiabatic processes in conical intersection regions at high energies. [ABSTRACT FROM AUTHOR]

Published
2019
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34. Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2A′ states of LiFH.

Author

Guan, Yafu, Zhang, Dong H., Guo, Hua, and Yarkony, David R.

Abstract

An analytic quasi-diabatic representation of ab initio electronic structure data is key to the accurate quantum mechanical description of non-adiabatic chemical processes. In this work, a general neural network (NN) fitting procedure is proposed to generate coupled quasi-diabatic Hamiltonians (Hd) that are capable of representing adiabatic energies, energy gradients, and derivative couplings over a wide range of geometries. The quasi-diabatic representation for LiFH is used as a testing example. The fitting data including adiabatic energies, energy gradients and interstate couplings are obtained from a previously fitted analytical quasi-diabatic potential energy matrix, and are well reproduced by the NN fitting. Most importantly, the NN fitting also yields quantum dynamic results that reproduce those on the original LiFH diabatic Hamiltonian, demonstrating the ability of NN to generate highly accurate quasi-diabatic Hamiltonians. [ABSTRACT FROM AUTHOR]

Published
2019
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35. Observation of the geometric phase effect in the H + HD → H2 + D reaction.

Author

Yuan, Daofu, Guan, Yafu, Chen, Wentao, Zhao, Hailin, Yu, Shengrui, Luo, Chang, Tan, Yuxin, Xie, Ting, Wang, Xingan, Sun, Zhigang, Zhang, Dong H., and Yang, Xueming

Subjects
*HYDROGEN-deuterium exchange, *GEOMETRIC quantum phases, *POTENTIAL energy surfaces, *ADIABATIC processes
Abstract

Theory has established the importance of geometric phase (GP) effects in the adiabatic dynamics of molecular systems with a conical intersection connecting the ground- and excited-state potential energy surfaces, but direct observation of their manifestation in chemical reactions remains a major challenge. Here, we report a high-resolution crossed molecular beams study of the H + HD→H2 + D reaction at a collision energy slightly above the conical intersection. Velocity map ion imaging revealed fast angular oscillations in product quantum state–resolved differential cross sections in the forward scattering direction for H2 products at specific rovibrational levels. The experimental results agree with adiabatic quantum dynamical calculations only when the GP effect is included. [ABSTRACT FROM AUTHOR]

Published
2018
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36. Construction of reactive potential energy surfaces with Gaussian process regression: active data selection.

Author

Guan, Yafu, Yang, Shuo, and Zhang, Dong H.

Subjects
*POTENTIAL energy, *GAUSSIAN processes, *POLYATOMIC molecules, *ACCURACY, *QUANTUM scattering
Abstract

Gaussian process regression (GPR) is an efficient non-parametric method for constructing multi-dimensional potential energy surfaces (PESs) for polyatomic molecules. Since not only the posterior mean but also the posterior variance can be easily calculated, GPR provides a well-established model for active learning, through which PESs can be constructed more efficiently and accurately. We propose a strategy of active data selection for the construction of PESs with emphasis on low energy regions. Through three-dimensional (3D) example of H3, the validity of this strategy is verified. The PESs for two prototypically reactive systems, namely, H + H2O ↔ H2 + OH reaction and H + CH4 ↔ H2 + CH3 reaction are reconstructed. Only 920 and 4000 points are assembled to reconstruct these two PESs respectively. The accuracy of the GP PESs is not only tested by energy errors but also validated by quantum scattering calculations. [ABSTRACT FROM AUTHOR]

Published
2018
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37. Extending the Representation of Multistate Coupled Potential Energy Surfaces To Include Properties Operators Using Neural Networks: Application to the 1,21A States of Ammonia

Author

Guan, Yafu, Guo, Hua, and Yarkony, David R.

Abstract

Fitting coupled adiabatic potential energy surfaces using coupled diabatic states enables, for accessible systems, nonadiabatic dynamics to be performed with unprecedented accuracy, when compared with on-the-fly dynamics. On-the-fly dynamics has advantages, not the least of which is the ability to compute molecular properties including electric dipole moments, transition dipole moments, and spin–orbit couplings. The availability of these terms extends the range of processes that can be treated with on-the-fly methods. In this work we use the example of fitting electric dipole and transition dipole moments of the 1,21A states of ammonia to show how to bring these advantages to the fit-coupled-surface method using a diabatic representation.

Published
2020
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38. Application of Clustering Algorithms to Partitioning Configuration Space in Fitting Reactive Potential Energy Surfaces

Author

Guan, Yafu, Yang, Shuo, and Zhang, Donghui

Abstract

Large number of energy points add great difficulty to construct reactive potential energy surfaces (PES). To alleviate this, exemplar-based clustering is applied to partition the configuration space into several smaller parts. The PES of each part can be constructed easily and the global PES is obtained by connecting all the PESs of small parts. This Divide and Conquer strategy is first demonstrated in the fitting of PES for OH3 with gaussian process regression (GPR) and further applied to construct PESs for CH5 and O+\CH4 with artificial neural networks (NN). The accuracy of PESs is tested by fitting errors and direct comparisons with previous PESs in dynamically important regions. As for OH3 and CH5, quantum scattering calculations further validate the global accuracy of newly fitted PESs. The results suggest that partitioning the configuration space by clustering provides a simple and useful method for the construction of PESs for systems that requires large number of energy points.

Published
2024
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39. Enabling a Unified Description of Both Internal Conversion and Intersystem Crossing in Formaldehyde: A Global Coupled Quasi-Diabatic Hamiltonian for Its S 0 , S 1 , and T 1 States.

Author

Guan Y, Xie C, Guo H, and Yarkony DR

Abstract

In our recent work, a diabatic Hamiltonian that couples the S 0 and S 1 states of formaldehyde was constructed using a robust fitting-and-diabatizing procedure with artificial neural networks, which is capable of representing adiabatic energies, energy gradients, and derivative couplings over a wide range of geometries including seams of conical intersection. In this work, based on the diabatization of S 0 and S 1 , the spin-orbit couplings between singlet states (S 0 , S 1 ) and triplet state T 1 are also determined in the same diabatic representation. The diabatized spin-orbit couplings are then fit with a symmetrized neural-network functional form. The ab initio spin-orbit couplings are well reproduced in large configuration space. Together with the neural-network-based potential energy surface for T 1 , the full quasi-diabatic Hamiltonian for the S 0 , S 1 , and T 1 states is completed, enabling a unified description of both internal conversion and intersystem crossing in formaldehyde. The vibrational levels on the three adiabatic states are found to be in good agreement with known experimental band origins.

Published
2021
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40. Neural Network Based Quasi-diabatic Representation for S 0 and S 1 States of Formaldehyde.

Author

Guan Y, Xie C, Guo H, and Yarkony DR

Abstract

A neural network based quasi-diabatic potential energy matrix H d that describes the photodissociation of formaldehyde involving the two lowest singlet states S 0 and S 1 is constructed. It has strict complete nuclear permutation inversion symmetry encoded and can reproduce high level ab initio electronic structure data, including energies, energy gradients, and derivative couplings, with excellent accuracy. It has been fully saturated in the configuration space to cover all possible reaction pathways with a trajectory-guided point sampling approach. This H d will not only enable the accurate full-dimensional dynamic simulations of the photodissociation of formaldehyde involving S 0 and S 1 but also provide a crucial ingredient for incorporating spin-orbit couplings into a diabatic framework, thus ultimately enabling the study of both internal conversion and intersystem crossing in formaldehyde on the same footing.

Published
2020
Full Text
View/download PDF

41. Extending the Representation of Multistate Coupled Potential Energy Surfaces To Include Properties Operators Using Neural Networks: Application to the 1,2 1 A States of Ammonia.

Author

Guan Y, Guo H, and Yarkony DR

Abstract

Fitting coupled adiabatic potential energy surfaces using coupled diabatic states enables, for accessible systems, nonadiabatic dynamics to be performed with unprecedented accuracy, when compared with on-the-fly dynamics. On-the-fly dynamics has advantages, not the least of which is the ability to compute molecular properties including electric dipole moments, transition dipole moments, and spin-orbit couplings. The availability of these terms extends the range of processes that can be treated with on-the-fly methods. In this work we use the example of fitting electric dipole and transition dipole moments of the 1,2 1 A states of ammonia to show how to bring these advantages to the fit-coupled-surface method using a diabatic representation.

Published
2020
Full Text
View/download PDF

42. Two-state diabatic potential energy surfaces of ClH 2 based on nonadiabatic couplings with neural networks.

Author

Yin Z, Guan Y, Fu B, and Zhang DH

Abstract

A general neural network (NN)-fitting procedure based on nonadiabatic couplings is proposed to generate coupled two-state diabatic potential energy surfaces (PESs) with conical intersections. The elements of the diabatic potential energy matrix (DPEM) can be obtained directly from a combination of the NN outputs in principle. Instead, to achieve higher accuracy, the adiabatic-to-diabatic transformation (ADT) angle (mixing angle) for each geometry is first solved from the NN outputs, followed by individual NN fittings of the three terms of the DPEM, which are calculated from the ab initio adiabatic energies and solved mixing angles. The procedure is applied to construct a new set of two-state diabatic potential energy surfaces of ClH2. The ab initio data including adiabatic energies and derivative couplings are well reproduced. Furthermore, the current diabatization procedure can describe well the vicinity of conical intersections in high potential energy regions, which are located in the T-shaped (C2v) structure of Cl-H2. The diabatic quantum dynamical results on diabatic PESs show large differences as compared with the adiabatic results in high collision energy regions, suggesting the significance of nonadiabatic processes in conical intersection regions at high energies.

Published
2019
Full Text
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43. Representation of coupled adiabatic potential energy surfaces using neural network based quasi-diabatic Hamiltonians: 1,2 2 A' states of LiFH.

Author

Guan Y, Zhang DH, Guo H, and Yarkony DR

Abstract

An analytic quasi-diabatic representation of ab initio electronic structure data is key to the accurate quantum mechanical description of non-adiabatic chemical processes. In this work, a general neural network (NN) fitting procedure is proposed to generate coupled quasi-diabatic Hamiltonians (H d ) that are capable of representing adiabatic energies, energy gradients, and derivative couplings over a wide range of geometries. The quasi-diabatic representation for LiFH is used as a testing example. The fitting data including adiabatic energies, energy gradients and interstate couplings are obtained from a previously fitted analytical quasi-diabatic potential energy matrix, and are well reproduced by the NN fitting. Most importantly, the NN fitting also yields quantum dynamic results that reproduce those on the original LiFH diabatic Hamiltonian, demonstrating the ability of NN to generate highly accurate quasi-diabatic Hamiltonians.

Published
2019
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44. Energy-Transfer Kinetics Driven by Midinfrared Amplified Spontaneous Emission after Two-Photon Excitation from Xe (s 0 ) to the Xe (6p[1/2] 0 ) State.

Author

He S, Guan Y, Liu D, Xia X, Gai B, Hu S, Guo J, Sang F, and Jin Y

Abstract

In optically pumped laser systems, rare gas lasers (RGLs) are a field of great interest for researchers. Gas laser regimes with metastable Ne, Ar, and Kr atoms have been investigated, while studies of RGLs based on metastable Xe are sparse. In this work, when a strong excitation laser (2.92 mJ/pulse, 7.44 × 10 5 W/cm 2 ) was applied to excite Xe atoms from the ground state to the 6p[1/2] 0 state, an interesting phenomenon emerged: An intense fluorescence of 980 nm (6p[1/2] 1 -6s[3/2] 2 ) was produced. However, when the energy of excitation laser was decreased to 0.50 mJ/pulse (1.27 × 10 5 W/cm 2 ), the fluorescence of 980 nm became very weak. Besides, lifetime and decay rate constant of the 6p[1/2] 0 state under the condition of E = 2.92 mJ are significantly different from either those measured by other groups or those of E = 0.50 mJ. These phenomena indicate that the high energy of excitation laser should trigger some new kinetic mechanisms. Further works identified that the new kinetic mechanism is the MIR ASE of 3408 nm (6p[1/2] 0 -6s'[1/2] 1 ). The mechanisms are proposed as follows. Substantial 6p[1/2] 0 atoms are produced by laser excitation. Then, the ASE of 3408 nm (6p[1/2] 0 -6s'[1/2] 1 ) is quickly produced to populate substantial 6s'[1/2] 1 atoms. The 6s'[1/2] 1 atoms can readily arrive at the 6p[1/2] 1 states through collision by virtue of the small energy difference (84 cm -1 ) and high collision rate constant of the transition from the 6s'[1/2] 1 state to the 6p[1/2] 1 state. As a result, the intense fluorescence of 980 nm is generated.

Published
2017
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