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Stan-Bernhardt A, Glinkina L, Hulm A, Ochsenfeld C. Exploring Chemical Space Using Ab Initio Hyperreactor Dynamics. ACS CENTRAL SCIENCE 2024; 10:302-314. [PMID: 38435517 PMCID: PMC10906254 DOI: 10.1021/acscentsci.3c01403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/20/2023] [Accepted: 12/21/2023] [Indexed: 03/05/2024]
Abstract
In recent years, first-principles exploration of chemical reaction space has provided valuable insights into intricate reaction networks. Here, we introduce ab initio hyperreactor dynamics, which enables rapid screening of the accessible chemical space from a given set of initial molecular species, predicting new synthetic routes that can potentially guide subsequent experimental studies. For this purpose, different hyperdynamics derived bias potentials are applied along with pressure-inducing spherical confinement of the molecular system in ab initio molecular dynamics simulations to efficiently enhance reactivity under mild conditions. To showcase the advantages and flexibility of the hyperreactor approach, we present a systematic study of the method's parameters on a HCN toy model and apply it to a recently introduced experimental model for the prebiotic formation of glycinal and acetamide in interstellar ices, which yields results in line with experimental findings. In addition, we show how the developed framework enables the study of complicated transitions like the first step of a nonenzymatic DNA nucleoside synthesis in an aqueous environment, where the molecular fragmentation problem of earlier nanoreactor approaches is avoided.
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Affiliation(s)
- Alexandra Stan-Bernhardt
- Chair
of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstrasse 5, D-81377 München, Germany
| | - Liubov Glinkina
- Chair
of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstrasse 5, D-81377 München, Germany
| | - Andreas Hulm
- Chair
of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstrasse 5, D-81377 München, Germany
| | - Christian Ochsenfeld
- Chair
of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstrasse 5, D-81377 München, Germany
- Max
Planck Institute for Solid State Research, Heisenbergstrasse 1, D-70569 Stuttgart, Germany
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2
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Zhang Y, Xu C, Lan Z. Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations. J Chem Theory Comput 2023. [PMID: 38031422 DOI: 10.1021/acs.jctc.3c00752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023]
Abstract
We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.
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Affiliation(s)
- Yutai Zhang
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Chao Xu
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
| | - Zhenggang Lan
- Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety and MOE Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, School of Environment, South China Normal University, Guangzhou 510006, P. R. China
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3
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Krep L, Schmalz F, Solbach F, Komissarov L, Nevolianis T, Kopp WA, Verstraelen T, Leonhard K. A Reactive Molecular Dynamics Study of Chlorinated Organic Compounds. Part II: A ChemTraYzer Study of Chlorinated Dibenzofuran Formation and Decomposition Processes. Chemphyschem 2022; 24:e202200783. [PMID: 36511423 DOI: 10.1002/cphc.202200783] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 12/14/2022]
Abstract
In our two-paper series, we first present the development of ReaxFF CHOCl parameters using the recently published ParAMS parametrization tool. In this second part, we update the reactive Molecular Dynamics - Quantum Mechanics coupling scheme ChemTraYzer and combine it with our new ReaxFF parameters from Part I to study formation and decomposition processes of chlorinated dibenzofurans. We introduce a self-learning method for recovering failed transition-state searches that improves the overall ChemTraYzer transition-state search success rate by 10 percentage points to a total of 48 %. With ChemTraYzer, we automatically find and quantify more than 500 reactions using transition state theory and DFT. Among the discovered chlorinated dibenzofuran reactions are numerous reactions that are new to the literature. In three case studies, we discuss the set of reactions that are most relevant to the dibenzofuran literature: (i) bimolecular reactions of the chlorinated-dibenzofuran precursors phenoxy radical and 1,3,5-trichlorobenzene, (ii) dibenzofuran chlorination and pyrolysis, and (iii) oxidation of chlorinated dibenzofurans.
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Affiliation(s)
- Lukas Krep
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Felix Schmalz
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Florian Solbach
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Leonid Komissarov
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Ghent, Belgium
| | - Thomas Nevolianis
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Wassja A Kopp
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
| | - Toon Verstraelen
- Center for Molecular Modeling (CMM), Ghent University, Technologiepark-Zwijnaarde 46, B-9052, Ghent, Belgium
| | - Kai Leonhard
- Institute of Technical Thermodynamics, RWTH Aachen University, North Rhine-Westphalia, 52062, Aachen, Germany
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4
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Stan A, Esch BVD, Ochsenfeld C. Fully Automated Generation of Prebiotically Relevant Reaction Networks from Optimized Nanoreactor Simulations. J Chem Theory Comput 2022; 18:6700-6712. [PMID: 36270030 DOI: 10.1021/acs.jctc.2c00754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The nanoreactor approach first introduced by the group of Martı́nez [Wang et al. Nat. Chem. 2014, 6, 1044-1048] has recently attracted much attention because of its ability to accelerate the discovery of reaction pathways. Here, we provide a comprehensive study of various simulation parameters and present an alternative implementation for the reactivity-enhancing spherical constraint function, as well as for the detection of reaction events. In this context, a fully automated postsimulation evaluation procedure based on RDKit and NetworkX analysis is introduced. The chemical and physical robustness of the procedure is examined by investigating the reactivity of selected homogeneous systems. The optimized procedure is applied at the GFN2-xTB level of theory to a system composed of HCN molecules and argon atoms, acting as a buffer, yielding prebiotically plausible primary and secondary precursors for the synthesis of RNA. Furthermore, the formose reaction network is explored leading to numerous sugar precursors. The discovered compounds reflect experimental findings; however, new synthetic routes and a large collection of exotic, highly reactive molecules are observed, highlighting the predictive power of the nanoreactor approach for unraveling the reactive manifold.
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Affiliation(s)
- Alexandra Stan
- Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 7, D-81377 München, Germany
| | - Beatriz von der Esch
- Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 7, D-81377 München, Germany
| | - Christian Ochsenfeld
- Chair of Theoretical Chemistry, Department of Chemistry, University of Munich (LMU), Butenandtstr. 7, D-81377 München, Germany.,Max Planck Institute for Solid State Research, Heisenbergstr. 1, D-70569 Stuttgart, Germany
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5
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Zhang G, Li J, Liang X, Liu Z. Automated reaction mechanisms and kinetics with the nudged elastic band method-based AMK_Mountain and its description of the preliminary alkaline hydrolysis of nitrocellulose monomer. J Comput Chem 2022; 43:1513-1523. [PMID: 35567577 DOI: 10.1002/jcc.26891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 04/18/2022] [Accepted: 04/27/2022] [Indexed: 11/08/2022]
Abstract
To improve the transition state (TS) search capability in complex chemical environments, AMK_Mountain is constructed based on the automated reaction mechanisms and kinetics (AutoMeKin) package. AMK_Mountain does not distinguish the reaction type of the TSs, which is beneficial to obtaining a more comprehensive reaction mechanism. In this study, the first step of the alkaline hydrolysis process of nitrocellulose monomer was adopted as the research object, and 730 possible initial configurations are constructed and 22 TSs pass high-level calculations. Energy difference and interaction region indicator reveal that the first step of alkaline hydrolysis is mainly the combination of nitrogen-containing functional groups at the positions α and β with hydroxide anions, followed by the formation of nitric acid and the further loss of protons to form nitrate. Overall, in combination with GFN2 -xTB and ORCA, the AMK_Mountain technique provides a reliable method for the location of the TSs in complex environments.
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Affiliation(s)
- Guan Zhang
- School of Human Settlement and Civil Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Jin Li
- School of Human Settlement and Civil Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Xinxin Liang
- School of Human Settlement and Civil Engineering, Xi'an Jiaotong University, Xi'an, China
| | - Zongkuan Liu
- School of Human Settlement and Civil Engineering, Xi'an Jiaotong University, Xi'an, China
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Kroonblawd MP, Goldman N, Maiti A, Lewicki JP. Polymer degradation through chemical change: a quantum-based test of inferred reactions in irradiated polydimethylsiloxane. Phys Chem Chem Phys 2022; 24:8142-8157. [PMID: 35332907 DOI: 10.1039/d1cp05647f] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Chemical reaction schemes are key conceptual tools for interpreting the results of experiments and simulations, but often carry implicit assumptions that remain largely unverified for complicated systems. Established schemes for chemical damage through crosslinking in irradiated silicone polymers comprised of polydimethylsiloxane (PDMS) date to the 1950's and correlate small-molecule off-gassing with specific crosslink features. In this regard, we use a somewhat reductionist model to develop a general conditional probability and correlation analysis approach that tests these types of causal connections between proposed experimental observables to reexamine this chemistry through quantum-based molecular dynamics (QMD) simulations. Analysis of the QMD simulations suggests that the established reaction schemes are qualitatively reasonable, but lack strong causal connections under a broad set of conditions that would enable making direct quantitative connections between off-gassing and crosslinking. Further assessment of the QMD data uncovers a strong (but nonideal) quantitative connection between exceptionally hard-to-measure chain scission events and the formation of silanol (Si-OH) groups. Our analysis indicates that conventional notions of radiation damage to PDMS should be further qualified and not necessarily used ad hoc. In addition, our efforts enable independent quantum-based tests that can inform confidence in assumed connections between experimental observables without the burden of fully elucidating entire reaction networks.
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Affiliation(s)
- Matthew P Kroonblawd
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
| | - Nir Goldman
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
| | - Amitesh Maiti
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
| | - James P Lewicki
- Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA.
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7
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Chen X, Liu M, Gao J. CARNOT: a Fragment-Based Direct Molecular Dynamics and Virtual-Reality Simulation Package for Reactive Systems. J Chem Theory Comput 2022; 18:1297-1313. [PMID: 35129348 DOI: 10.1021/acs.jctc.1c01032] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Traditionally, the study of reaction mechanisms of complex reaction systems such as combustion has been performed on an individual basis by optimizations of transition structure and minimum energy path or by reaction dynamics trajectory calculations for one elementary reaction at a time. It is effective, but time-consuming, whereas important and unexpected processes could have been missed. In this article, we present a direct molecular dynamics (DMD) approach and a virtual-reality simulation program, CARNOT, in which plausible chemical reactions are simulated simultaneously at finite temperature and pressure conditions. A key concept of the present ab initio molecular dynamics method is to partition a large, chemically reactive system into molecular fragments that can be adjusted on the fly of a DMD simulation. The theory represents an extension of the explicit polarization method to reactive events, called ReX-Pol. We propose a highest-and-lowest adapted-spin approximation to define the local spins of individual fragments, rather than treating the entire system by a delocalized wave function. Consequently, the present ab initio DMD can be applied to reactive systems consisting of an arbitrarily varying number of closed and open-shell fragments such as free radicals, zwitterions, and separate ions found in combustion and other reactions. A graph-data structure algorithm was incorporated in CARNOT for the analysis of reaction networks, suitable for reaction mechanism reduction. Employing the PW91 density functional theory and the 6-31+G(d) basis set, the capabilities of the CARNOT program were illustrated by a combustion reaction, consisting of 28 650 atoms, and by reaction network analysis that revealed a range of mechanistic and dynamical events. The method may be useful for applications to other types of complex reactions.
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Affiliation(s)
- Xin Chen
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China
| | - Meiyi Liu
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China
| | - Jiali Gao
- Peking University Shenzhen Graduate School, Shenzhen, Guangdong 581055, China.,Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, Guangdong 581055, China.,Department of Chemistry and Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455, United States
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Baiardi A, Grimmel SA, Steiner M, Türtscher PL, Unsleber JP, Weymuth T, Reiher M. Expansive Quantum Mechanical Exploration of Chemical Reaction Paths. Acc Chem Res 2022; 55:35-43. [PMID: 34918903 DOI: 10.1021/acs.accounts.1c00472] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Quantum mechanical methods have been well-established for the elucidation of reaction paths of chemical processes and for the explicit dynamics of molecular systems. While they are usually deployed in routine manual calculations on reactions for which some insights are already available (typically from experiment), new algorithms and continuously increasing capabilities of modern computer hardware allow for exploratory open-ended computational campaigns that are unbiased and therefore enable unexpected discoveries. Highly efficient and even automated procedures facilitate systematic approaches toward the exploration of uncharted territory in molecular transformations and dynamics. In this work, we elaborate on such explorative approaches that range from reaction network explorations with (stationary) quantum chemical methods to explorative molecular dynamics and migrant wave packet dynamics. The focus is on recent developments that cover the following strategies. (i) Pruning search options for elementary reaction steps by heuristic rules based on the first-principles of quantum mechanics: Rules are required for reducing the combinatorial explosion of potentially reactive atom pairings, and rooting them in concepts derived from the electronic wave function makes them applicable to any molecular system. (ii) Enforcing reactive events by external biases: Inducing a reaction requires constraints that steer and direct elementary-step searches, which can be formulated in terms of forces, velocities, or supplementary potentials. (iii) Manual steering facilitated by interactive quantum mechanics: As ultrafast quantum chemical methods allow for real-time manual interactions with molecular systems, human-intuition-guided paths can be easily explored with suitable human-machine interfaces. (iv) New approaches for transition-state optimization with continuous curve representations can provide stable schemes to be driven in an automated way by allowing for an efficient tuning of the curve's parameters (instead of a manipulation of a collection of structures along the path), and (v) reactive molecular dynamics and direct wave packet propagation exploit the equations of motion of an underlying mechanical theory (usually, classical Newtonian mechanics or Schrödinger quantum mechanics). Explorative approaches are likely to replace the current state of the art in computational chemistry, because they reduce the human effort to be invested in reaction path elucidations, they are less prone to errors and bias-free, and they cover more extensive regions of the relevant configuration space. As a result, computational investigations that rely on these techniques are more likely to deliver surprising discoveries.
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Affiliation(s)
- Alberto Baiardi
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Stephanie A. Grimmel
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Miguel Steiner
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Paul L. Türtscher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Jan P. Unsleber
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Thomas Weymuth
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
| | - Markus Reiher
- Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland
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Martínez-Núñez E, Barnes GL, Glowacki DR, Kopec S, Peláez D, Rodríguez A, Rodríguez-Fernández R, Shannon RJ, Stewart JJP, Tahoces PG, Vazquez SA. AutoMeKin2021: An open-source program for automated reaction discovery. J Comput Chem 2021; 42:2036-2048. [PMID: 34387374 DOI: 10.1002/jcc.26734] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/16/2021] [Accepted: 07/27/2021] [Indexed: 01/10/2023]
Abstract
AutoMeKin2021 is an updated version of tsscds2018, a program for the automated discovery of reaction mechanisms (J. Comput. Chem. 2018, 39, 1922). This release features a number of new capabilities: rare-event molecular dynamics simulations to enhance reaction discovery, extension of the original search algorithm to study van der Waals complexes, use of chemical knowledge, a new search algorithm based on bond-order time series analysis, statistics of the chemical reaction networks, a web application to submit jobs, and other features. The source code, manual, installation instructions and the website link are available at: https://rxnkin.usc.es/index.php/AutoMeKin.
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Affiliation(s)
- Emilio Martínez-Núñez
- Department of Physical Chemistry, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - George L Barnes
- Department of Chemistry and Biochemistry, Siena College, Loudonville, New York, USA
| | - David R Glowacki
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | - Sabine Kopec
- Institut de Sciences Moléculaires d'Orsay, UMR 8214, Université Paris-Sud - Université Paris-Saclay, Orsay, France
| | - Daniel Peláez
- Institut de Sciences Moléculaires d'Orsay, UMR 8214, Université Paris-Sud - Université Paris-Saclay, Orsay, France
| | - Aurelio Rodríguez
- Galicia Supercomputing Center (CESGA), Santiago de Compostela, Spain
| | | | - Robin J Shannon
- Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol, UK
| | | | - Pablo G Tahoces
- Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Saulo A Vazquez
- Department of Physical Chemistry, University of Santiago de Compostela, Santiago de Compostela, Spain
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Rice BM, Mattson WD, Larentzos JP, Byrd EFC. Heuristics for chemical species identification in dense systems. J Chem Phys 2020; 153:064102. [DOI: 10.1063/5.0015664] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Affiliation(s)
- Betsy M. Rice
- US Army CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
| | - William D. Mattson
- US Army CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
| | - James P. Larentzos
- US Army CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
| | - Edward F. C. Byrd
- US Army CCDC Army Research Laboratory, Aberdeen Proving Ground, Aberdeen, Maryland 21005, USA
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