Implementation of a Methodology for Crystal Structure Prediction Using Genetic Algorithms Integrated into the Python ASE Library
DOI:
https://doi.org/10.15407/ujpe71.6.554Keywords:
crystal structure prediction, genetic algorithm, energy landscape, crystalline silica polymorphs, SiO2, structure relaxation, ASE, GULPAbstract
This work is dedicated to the development and implementation of a methodology for crystal structure prediction using genetic algorithms integrated into the Python ASE library. Crystal structure prediction plays a critical role in materials science, chemistry, and nanotechnology, enabling the discovery of novel compounds with tailored properties. By combining the flexibility of ASE with the speed of classical relaxers and the accuracy of DFT-based methods, our approach significantly reduces computational costs while maintaining predictive reliability. The methodology was validated on polymorphs of silica (SiO2), where our system successfully recovered both global and local minima of the energy landscape. We also explore the integration of neural network relaxers such as MACE and AIMNet2 to further accelerate the search process. This study lays the groundwork for efficient, scalable, and accurate predictive modeling of crystalline materials.
References
1. D.G. Pettifor. Computer-aided materials design: bridging the gaps between physics, chemistry and engineering. Phys. Ed. 32, 164 (1997).
https://doi.org/10.1088/0031-9120/32/3/015
2. S.G. Louie, Y.H. Chan, F.H. da Jornada, Z. Li, D.Y. Qiu. Discovering and understanding materials through computation. Nat. Mater. 20, 728 (2021).
https://doi.org/10.1038/s41563-021-01015-1
3. T. Xie, J.C. Grossman. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120, 145301 (2018).
https://doi.org/10.1103/PhysRevLett.120.145301
4. S. Feng, H. Zhou, H. Dong. Using deep neural network with small dataset to predict material defects. Mater. Des. 162, 300 (2019).
https://doi.org/10.1016/j.matdes.2018.11.060
5. M. Jansen. The energy landscape concept and its implications for synthesis planning. Pure Appl. Chem. 86, 883 (2014).
https://doi.org/10.1515/pac-2014-0212
6. D.J. Wales, J.P. Doye. Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J. Phys. Chem. A 101, 5111 (1997).
https://doi.org/10.1021/jp970984n
7. B. Schaefer, S. Mohr, M. Amsler, S. Goedecker. Minima hopping guided path search: an efficient method for finding complex chemical reaction pathways. J. Chem. Phys. 140, 214102 (2014).
https://doi.org/10.1063/1.4878944
8. A. Barducci, M. Bonomi, M. Parrinello. Metadynamics. Wiley Interdiscip. Rev. Comput. Mol. Sci. 1, 826 (2011).
https://doi.org/10.1002/wcms.31
9. S. Kirkpatrick, C.D. Gelatt Jr, M.P. Vecchi. Optimization by simulated annealing. Science 220, 671 (1983).
https://doi.org/10.1126/science.220.4598.671
10. Y. Wang, J. Lv, L. Zhu, Y. Ma. CALYPSO: A method for crystal structure prediction. Comput. Phys. Commun. 183, 2063 (2012).
https://doi.org/10.1016/j.cpc.2012.05.008
11. Z. Falls, P. Avery, X. Wang, K.P. Hilleke, E. Zurek. The XtalOpt evolutionary algorithm for crystal structure prediction. J. Phys. Chem. C 125, 1601 (2021).
https://doi.org/10.1021/acs.jpcc.0c09531
12. A.H. Larsen, J.J. Mortensen, J. Blomqvist, I.E. Castelli, R. Christensen, M. Dulak, J. Friis, M.N. Groves, B. Hammer, C. Hargus, E.D. Hermes, P.C. Jennings, P.B. Jensen, J. Kermode, J.R. Kitchin et al. The atomic simulation environment-a Python library for working with atoms. J. Phys. Condens. Matter. 29, 273002 (2017).
https://doi.org/10.1088/1361-648X/aa680e
13. J.D. Gale, A.L. Rohl. The general utility lattice program (GULP). Mol. Simul. 29, 291 (2013).
https://doi.org/10.1080/0892702031000104887
14. P. Giannozzi et al. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys. Condens. Matter. 21, 395502 (2009).
https://doi.org/10.1088/0953-8984/21/39/395502
15. P. Hohenberg, W. Kohn. Inhomogeneous electron gas. Phys. Rev. 136, B864 (1964).
https://doi.org/10.1103/PhysRev.136.B864
16. W. Kohn, L.J. Sham. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133 (1965).
https://doi.org/10.1103/PhysRev.140.A1133
17. D.M. Deaven, K.M. Ho. Molecular geometry optimization with a genetic algorithm. Phys. Rev. Lett. 75, 288 (1995).
https://doi.org/10.1103/PhysRevLett.75.288
18. S. Lysgaard, D.D. Landis, T. Bligaard, T. Vegge. Genetic algorithm procreation operators for alloy nanoparticle catalysts. Top. Catal. 57, 33 (2014).
https://doi.org/10.1007/s11244-013-0160-9
19. P.B. Jensen, S. Lysgaard, U.J. Quaade, T. Vegge. Designing mixed metal halide ammines for ammonia storage using density functional theory and genetic algorithms. Phys. Chem. Chem. Phys. 16, 19732 (2014).
https://doi.org/10.1039/C4CP03133D
20. M. Van den Bossche, H. Gronbeck, B. Hammer. Tight-binding approximation-enhanced global optimization. J. Chem. Theor. Comput. 14, 2797 (2018).
https://doi.org/10.1021/acs.jctc.8b00039
21. P. Giannozzi et al. Advanced capabilities for materials modelling with Quantum ESPRESSO. J. Phys. Condens. Matter. 29, 465901 (2017).
https://doi.org/10.1088/1361-648X/aa8f79
22. P. Giannozzi et al. Quantum ESPRESSO toward the exascale. J. Chem. Phys. 152, 154105 (2020).
https://doi.org/10.1063/5.0005082
23. J.P. Perdew, K. Burke, M. Ernzerhof. Generalized gradient approximation made simple. Phys. Rev. Lett. 77, 3865 (1996).
https://doi.org/10.1103/PhysRevLett.77.3865
24. P.E. Blochl. Projector augmented-wave method. Phys. Rev. B 50, 17953 (1994).
https://doi.org/10.1103/PhysRevB.50.17953
25. K. Royce, C. Baars, H. Viles. Defining damage and susceptibility, with implications for mineral specimens and objects: introducing the mineral susceptibility database. Stud. Conserv. 68, 298 (2023).
https://doi.org/10.1080/00393630.2021.2015947
26. A. Jain, S.P. Ong, G. Hautier, W. Chen, W.D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K.A. Persson. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 1, 011002 (2013).
https://doi.org/10.1063/1.4812323
27. K. Momma, F. Izumi. VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data. J. Appl. Cryst. 44, 1272 (2011).
https://doi.org/10.1107/S0021889811038970
28. N.E. Zimmermann, A. Jain. Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity. RSC Adv. 10, 6063 (2020).
https://doi.org/10.1039/C9RA07755C
29. J.S. Smith, O. Isayev, A.E. Roitberg. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost. Chem. Sci. 8, 3192 (2017).
https://doi.org/10.1039/C6SC05720A
30. I. Batatia, D.P. Kovacs, G. Simm, C. Ortner, G. Csanyi. MACE: Higher order equivariant message passing neural networks for fast and accurate force fields. Adv. Neural Inf. Process 35, 11423 (2022).
https://doi.org/10.52202/068431-0830
31. R. Zubatyuk, J.S. Smith, J. Leszczynski, O. Isayev. Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network. Sci. Adv. 5, eaav6490 (2019).
Downloads
Published
Issue
Section
License
Copyright Agreement
License to Publish the Paper
Kyiv, Ukraine
The corresponding author and the co-authors (hereon referred to as the Author(s)) of the paper being submitted to the Ukrainian Journal of Physics (hereon referred to as the Paper) from one side and the Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine, represented by its Director (hereon referred to as the Publisher) from the other side have come to the following Agreement:
1. Subject of the Agreement.
The Author(s) grant(s) the Publisher the free non-exclusive right to use the Paper (of scientific, technical, or any other content) according to the terms and conditions defined by this Agreement.
2. The ways of using the Paper.
2.1. The Author(s) grant(s) the Publisher the right to use the Paper as follows.
2.1.1. To publish the Paper in the Ukrainian Journal of Physics (hereon referred to as the Journal) in original language and translated into English (the copy of the Paper approved by the Author(s) and the Publisher and accepted for publication is a constitutive part of this License Agreement).
2.1.2. To edit, adapt, and correct the Paper by approval of the Author(s).
2.1.3. To translate the Paper in the case when the Paper is written in a language different from that adopted in the Journal.
2.2. If the Author(s) has(ve) an intent to use the Paper in any other way, e.g., to publish the translated version of the Paper (except for the case defined by Section 2.1.3 of this Agreement), to post the full Paper or any its part on the web, to publish the Paper in any other editions, to include the Paper or any its part in other collections, anthologies, encyclopaedias, etc., the Author(s) should get a written permission from the Publisher.
3. License territory.
The Author(s) grant(s) the Publisher the right to use the Paper as regulated by sections 2.1.1–2.1.3 of this Agreement on the territory of Ukraine and to distribute the Paper as indispensable part of the Journal on the territory of Ukraine and other countries by means of subscription, sales, and free transfer to a third party.
4. Duration.
4.1. This Agreement is valid starting from the date of signature and acts for the entire period of the existence of the Journal.
5. Loyalty.
5.1. The Author(s) warrant(s) the Publisher that:
– he/she is the true author (co-author) of the Paper;
– copyright on the Paper was not transferred to any other party;
– the Paper has never been published before and will not be published in any other media before it is published by the Publisher (see also section 2.2);
– the Author(s) do(es) not violate any intellectual property right of other parties. If the Paper includes some materials of other parties, except for citations whose length is regulated by the scientific, informational, or critical character of the Paper, the use of such materials is in compliance with the regulations of the international law and the law of Ukraine.
6. Requisites and signatures of the Parties.
Publisher: Bogolyubov Institute for Theoretical Physics, National Academy of Sciences of Ukraine.
Address: Ukraine, Kyiv, Metrolohichna Str. 14-b.
Author: Electronic signature on behalf and with endorsement of all co-authors.
.png){kind=small}
