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Editorial ARTICLE

Front. Chem., 20 March 2020 | https://doi.org/10.3389/fchem.2020.00198

Editorial: Application of Optimization Algorithms in Chemistry

  • 1Coimbra Chemistry Centre (CQC), Department of Chemistry, University of Coimbra, Coimbra, Portugal
  • 2Departmento de Química Física, Facultade de Química, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
  • 3Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States

Editorial on the Research Topic
Application of Optimization Algorithms in Chemistry

Molecular structure optimization, fitting potential energy functions to ab initio and experimental data, and spectral assignment are among the hardest optimization tasks in molecular sciences. These are fundamental problems in chemistry, but they can also be relevant in molecular physics and biochemistry. In past decades, several methodologies have been proposed to help in the above mentioned tasks, and some of them are already incorporated into computational tools, such as GMIN (Wales and Scheraga, 1999; Wales, 2010), Gradient Embedded Genetic Algorithm or GEGA (Alexandrova and Boldyrev, 2005), OGOLEM (Hartke, 1993; Dieterich and Hartke, 2017), Birmingham Cluster Genetic Algorithm or BCGA (Johnston, 2003; Shayeghi et al., 2015), Evolutionary Algorithm for Molecular Clusters or EA_MOL (Llanio-Trujillo et al., 2011; Marques and Pereira, 2011), Global Reaction Route Mapping or GRRM (Ohno and Maeda, 2006, 2019), Automated Mechanisms and Kinetics or AutoMeKin (Martínez-Núñez, 2015a,b; Martínez-Núñez, 2020), and Genetic Algorithm fitting or GAFit (Rodríguez-Fernández et al., 2017, 2020). Most of these computational programs are interfaced with well-known packages that perform electronic-structure calculations and, hence, allow for a direct assessment of the semi-empirical, density functional theory (DFT) or ab initio energy of the system during the optimization process. Another relevant methodology to explore low-energy landscapes is the parallel-tempering Monte Carlo technique, which has been also applied in the calculation of thermodynamic properties.

Global geometry optimization studies are, now, being extended to systems of increasing complexity. In particular, global optimization algorithms have been applied to a great diversity of chemical systems, including atomic and molecular clusters as well as colloidal aggregates and biomolecules. Nonetheless, optimization work needs, in general, a large number of computational resources and, hence, improvements in algorithms to relieve the burden. Major challenges are concerned with the treatment of systems with increasing size and incorporating higher levels of theory in the molecular model. Also, multi-component aggregates pose an important combinatorial problem and require novel optimization strategies. Although the use of state-of-the-art spectroscopic techniques to probe the structure of clusters has allowed for close collaborative work involving computational and experimental achievements, there is still room for greater improvement in this effort. In particular, comparisons between theoretical and experimental spectroscopic data will benefit from significant improvements in algorithms devoted for the spectral assignment.

Pursuing those purposes, we believe the collection of papers for the present Research Topic illustrates the broad scope of computational strategies for global optimization applications in chemistry. All contributions describe optimization strategies for a great diversity of chemical systems.

Basin-hopping (BH) is able to generate a coarse-grained mapping of a potential energy surface (PES) in terms of local minima, which can then be used to gain insights into molecular dynamics and thermodynamic properties as pointed out by Zhou et al. in their contribution. These authors also show how unsupervised machine learning tools can be employed to enhance BH searches, which result in more efficient identification of local minima and transition states connecting them.

Jana et al. employ a particle swarm optimization (PSO) method to search for small Cn clusters. PSO is another useful algorithm for a stochastic search in multidimensional space. The method has proven efficient in hard optimization problems compared with traditional methods.

Hernández-Rojas and Calvo also employ BH method, this time to predict low-energy structures of adamantane clusters by using both coarse-grained and atomistic potential models. Although coarse-grained models are appealing for the complex clusters that are studied, the comparison with atomistic potentials shows that some relevant structural details are not captured by the former.

As for seeking conformational minima of flexible acyclic molecules, Ferro-Costas and Fernández-Ramos propose an algorithm that combines a systematic variation of torsion angles with a Monte Carlo search. This methodology has been applied to calculate multi-structural partition functions of several alcohols ranging from n-propanol to n-heptanol and was also tested with the amino acid L-serine.

Panadés-Barrueta et al. put forward a fully automated method to generate highly-accurate semiempirical potential energy surfaces. They use global optimization techniques and automated PES sampling algorithms to refine specific reaction parameters of semi-empirical Hamiltonians, which can be subsequently employed in quantum dynamics studies.

In turn, Wang et al. carry out a microsolvation study of Na+ with water by applying a genetic algorithm combined with density functional theory to obtain low-energy structures of the clusters. Also, a new genetic algorithm is proposed by Silva et al. for the prediction of structures of nanoparticles. This work explores the efficacy of new evolutionary operators to treat Lennard-Jones and carbon clusters.

Khatun et al. develop a global optimizer which grows the cluster by adding atoms one by one. The method is tested by studying transition-metal clusters and binary and ternary nanoalloys of such elements.

Cova and Pais review and discuss deep learning strategies for optimizing the prediction of chemical patterns, which includes accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Funding

The Coimbra Chemistry Centre (CQC) was supported by the Fundação para a Ciência e a Tecnologia (FCT), Portugal, through Project UID/QUI/00313/2019.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We thank all the authors who participated in this collection of articles, as well as all the reviewers for their relevant contributions to the Research Topic.

References

Alexandrova, A. N., and Boldyrev, I. (2005). Search for the Lin0/+1/-1 (n = 5–7) lowest-energy structures using the ab Initio gradient embedded genetic algorithm (GEGA). Elucidation of the chemical bonding in the lithium clusters. J. Chem. Theory Comput. 1, 566–580. doi: 10.1021/ct050093g

CrossRef Full Text

Dieterich, J. M., and Hartke, B. (2017). OGOLEM: Framework for GA-Based Global Optimization. Available online at: https://www.ogolem.org/ (accessed March, 2020).

Google Scholar

Hartke, B. (1993). Global geometry optimization of clusters using genetic algorithms. J. Phys. Chem. 97, 9973–9976. doi: 10.1021/j100141a013

CrossRef Full Text | Google Scholar

Johnston, R. L. (2003). Evolving better nanoparticles: genetic algorithms for optimising cluster geometries. Dalton Trans. 4193. doi: 10.1039/B305686D

CrossRef Full Text | Google Scholar

Llanio-Trujillo, J. L., Marques, J. M. C., and Pereira, F. B. (2011). An evolutionary algorithm for the global optimization of molecular clusters: application to water, benzene, and benzene cation. J. Phys. Chem. A 115, 2130–2138. doi: 10.1021/jp1117695

PubMed Abstract | CrossRef Full Text | Google Scholar

Marques, J. M. C., and Pereira, F. B. (2011). EA_MOL: Evolutionary Algorithm for the Global Minimum Search of Molecular Clusters. Available online at: https://apps.uc.pt/mypage/faculty/qtmarque/en/software/ (accessed February, 2020).

Martínez-Núñez, E. (2015a). An automated method to find transition states using chemical dynamics simulations. J. Comput. Chem. 36, 222–234. doi: 10.1002/jcc.23790

PubMed Abstract | CrossRef Full Text | Google Scholar

Martínez-Núñez, E. (2015b). An automated transition state search using classical trajectories initialized at multiple minima. Phys. Chem. Chem. Phys. 17:14912. doi: 10.1039/C5CP02175H

PubMed Abstract | CrossRef Full Text | Google Scholar

Martínez-Núñez, E., Barnes, G. L., Glowacki, D. R., Kopec, S., Pelaez-Ruiz, D., Rodriguez, A., et al. (2020). AutoMeKin: Automated Mechanisms and Kinetics. Available online at: https://rxnkin.usc.es/index.php/AutoMeKin (accessed January, 2020).

Google Scholar

Ohno, K., and Maeda, S. (2006). Global reaction route mapping on potential energy surfaces of formaldehyde, formic acid, and their metal substituted analogues. J. Phys. Chem. A 110, 8933–8941. doi: 10.1021/jp061149l

PubMed Abstract | CrossRef Full Text | Google Scholar

Ohno, K., and Maeda, S. (2019). Distribution of GRRM. Available online at: https://iqce.jp/GRRM/index_e.shtml (accessed February, 2020).

Rodríguez-Fernández, R., Pereira, F. B., Marques, J. M. C., Martínez-Núñez, E., and Vázquez, S. A. (2017). GAFit: a general-purpose, user-friendly program for fitting potential energy surfaces based on genetic algorithms. Comput. Phys. Commun. 217:89. doi: 10.1016/j.cpc.2017.02.008

CrossRef Full Text | Google Scholar

Rodríguez-Fernández, R., Pereira, F. P., Marques, J. M. C., Vázquez-Rodríguez, S., and Martinez-Nunez, E. (2020). GAFit, version 1.6. Available online at: https://rxnkin.usc.es/index.php/GAFit (accessed January, 2020).

Shayeghi, A., Götz, D., Davis, J. B. A., Schäfer, R., and Johnston, R. L. (2015). Pool-BCGA: a parallelised generation-free genetic algorithm for the ab initio global optimisation of nanoalloy clusters. Phys. Chem. Chem. Phys. 17, 2104–2112. doi: 10.1039/C4CP04323E

PubMed Abstract | CrossRef Full Text | Google Scholar

Wales, D. J. (2010). GMIN: A Program for Finding Global Minima and Calculating Thermodynamic Properties From Basin-Sampling. Available online at: http://www-wales.ch.cam.ac.uk/GMIN/ (accessed January, 2020).

Wales, D. J., and Scheraga, H. A. (1999). Global optimization of clusters, crystals, and biomolecules. Science 285, 1368–1372. doi: 10.1126/science.285.5432.1368

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: molecular geometry search, fitting potential energy functions, spectral assignment, global optimization algorithms, energy landscapes

Citation: Marques JMC, Martínez-Núñez E and Hase WL (2020) Editorial: Application of Optimization Algorithms in Chemistry. Front. Chem. 8:198. doi: 10.3389/fchem.2020.00198

Received: 22 February 2020; Accepted: 04 March 2020;
Published: 20 March 2020.

Edited and reviewed by: Sam P. De Visser, University of Manchester, United Kingdom

Copyright © 2020 Marques, Martínez-Núñez and Hase. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jorge M. C. Marques, qtmarque@ci.uc.pt