Adaptive Exploration and Optimization of Materials Crystal Structures

Published Online:https://doi.org/10.1287/ijds.2023.0028

References

  • Ba S, Joseph VR (2018) Maxpro: Maximum projection designs. R package version 4.1-2. Accessed January 15, 2023, https://cran.r-project.org/web/packages/MaxPro.Google Scholar
  • Bartók AP, Payne MC, Kondor R, Csányi G (2010) Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons. Phys. Rev. Lett. 104(13):136403.Google Scholar
  • Basudhar A, Missoum S (2008) Adaptive explicit decision functions for probabilistic design and optimization using support vector machines. Comput. Struc. 86(19–20):1904–1917.Google Scholar
  • Batra R, Tran HD, Kim C, Chapman J, Chen L, Chandrasekaran A, Ramprasad R (2019) General atomic neighborhood fingerprint for machine learning-based methods. J. Phys. Chem. C. 123(25):15859–15866.Google Scholar
  • Behler J, Parrinello M (2007) Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 98(14):146401.Google Scholar
  • Berry RS (1993) Potential surfaces and dynamics: What clusters tell us. Chem. Rev. 93(7):2379–2394.Google Scholar
  • Carnell R (2016), ‘Package ‘lhs”, CRAN. Accessed January 15, 2023, https://cran.r-project.org/web/packages/lhs.Google Scholar
  • Chen J, Zhu G, Yuan C, Huang Y (2020) Semi-supervised embedding learning for high-dimensional bayesian optimization. Preprint, submitted May 29, https://arxiv.org/abs/2005.14601.Google Scholar
  • Chen W, Fuge M (2017) Beyond the known: Detecting novel feasible domains over an unbounded design space. J. Mech. Des. 139(11):111405.Google Scholar
  • d’Avezac M, Luo J-W, Chanier T, Zunger A (2012) Genetic-algorithm discovery of a direct-gap and optically allowed superstructure from indirect-gap si and ge semiconductors. Phys. Rev. Lett. 108(2):027401.Google Scholar
  • Franceschetti A, Zunger A (1999) The inverse band-structure problem of finding an atomic configuration with given electronic properties. Nature 402(6757):60–63.Google Scholar
  • Frazier PI (2018) Bayesian optimization. INFORMS Tutorials 255–278. https://doi.org/10.1287/educ.2018.0188.Google Scholar
  • Gaida NA, Niwa K, Sasaki T, Hasegawa M (2021) Phase relations and thermoelasticity of magnesium silicide at high pressure and temperature. J. Chem. Phys. 154(14):144701.Google Scholar
  • Glass CW, Oganov AR, Hansen N (2006) Uspex-evolutionary crystal structure prediction. Comput. Phys. Commun. 175(11–12):713–720.Google Scholar
  • Goedecker S (2004) Minima hopping: An efficient search method for the global minimum of the potential energy surface of complex molecular systems. J. Chem. Phys. 120(21):9911–9917.Google Scholar
  • Gonze X, Jollet F, Araujo FA, Adams D, Amadon B, Applencourt T, Audouze C, et al. (2016) Recent developments in the abinit software package. Comput. Phys. Commun. 205:106–131.Google Scholar
  • Ha H, Rana S, Gupta S, Nguyen T, Venkatesh S, et al. (2019) Bayesian optimization with unknown search space. Adv. Neural Inf. Process. Syst., vol. 32 (Curran Associates, Inc., New York), 11795–11804. https://proceedings.neurips.cc/paper_files/paper/2019/file/ccf0304d099baecfbe7ff6844e1f6d91-Paper.pdf.Google Scholar
  • Hartwigsen C, Goedecker S, Hutter J (1998) Relativistic separable dual-space gaussian pseudopotentials from H to Rn. Phys. Rev. B Condens. Matter Mater. Phys. 58:3641.Google Scholar
  • Hohenberg P, Kohn W (1964) Inhomogeneous electron gas. Phys. Rev. 136:B864–B871.Google Scholar
  • Huan TD (2018) Pressure-stabilized binary compounds of magnesium and silicon. Phys. Rev. Mater. 2(2):023803.Google Scholar
  • Huan TD, Mannodi-Kanakkithodi A, Ramprasad R (2015) Accelerated materials property predictions and design using motif-based fingerprints. Phys. Rev. B Condens. Matter Mater. Phys. 92(1):014106.Google Scholar
  • Jackson JE (2005) A user’s guide to principal components, volume 587 (John Wiley & Sons, Hoboken, NJ).Google Scholar
  • Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4):455–492.Google Scholar
  • Joseph VR (2016) Space-filling designs for computer experiments: A review. Qual. Eng. 28(1):28–35.Google Scholar
  • Joseph VR, Gul E, Ba S (2015) Maximum projection designs for computer experiments. Biometrika 102(2):371–380.Google Scholar
  • Kobayashi Y, Naito M, Sudoh K, Gentils A, Bachelet C, Bourçois J (2019) Formation of crystallographically oriented metastable Mg1.8Si in Mg ion-implanted Si. Cryst. Growth Des. 19(12):7138–7142.Google Scholar
  • Kohn W, Sham L (1965) Self-consistent equations including exchange and correlation effects. Phys. Rev. 140:A1133–A1138.Google Scholar
  • Krishna A, Craig SR, Shi C, Joseph VR (2022) Inverse design of acoustic metasurfaces using space-filling points. Appl. Phys. Lett. 121(7):071701.Google Scholar
  • Maddox J (1988) Crystals from first principles. Nature 335(6187):201.Google Scholar
  • Mannodi-Kanakkithodi A, Pilania G, Huan TD, Lookman T, Ramprasad R (2016) Machine learning strategy for the accelerated design of polymer dielectrics. Sci. Rep. 6:20952.Google Scholar
  • Martoňák R, Donadio D, Oganov AR, Parrinello M (2006) Crystal structure transformations in sio 2 from classical and ab initio metadynamics. Nat. Mater. 5(8):623–626.Google Scholar
  • Monkhorst HJ, Pack JD (1976) Special points for brillouin-zone integrations. Phys. Rev. B. 13:5188.Google Scholar
  • Morris MD, Mitchell TJ (1995) Exploratory designs for computational experiments. J. Statist. Plann. Inference. 43(3):381–402.Google Scholar
  • Nguyen V, Gupta S, Rane S, Li C, Venkatesh S (2017), Bayesian optimization in weakly specified search space. 2017 IEEE International Conference on Data Mining (ICDM) (IEEE, Piscataway, NJ), 347–356.Google Scholar
  • Oganov AR (2011) Modern Methods of Crystal Structure Prediction (John Wiley & Sons, Weinheim, Germany).Google Scholar
  • Oganov AR, Glass CW (2006) Crystal structure prediction using ab initio evolutionary techniques: Principles and applications. J. Chem. Phys. 124(24):244704.Google Scholar
  • Oganov AR, Lyakhov AO, Valle M (2011) How evolutionary crystal structure prediction works and why. Acc. Chem. Res. 44(3):227–237.Google Scholar
  • Oganov AR, Pickard CJ, Zhu Q, Needs RJ (2019) Structure prediction drives materials discovery. Nat. Rev. Mater. 4(5):331–348.Google Scholar
  • Pannetier J, Bassas-Alsina J, Rodriguez-Carvajal J, Caignaert V (1990) Prediction of crystal structures from crystal chemistry rules by simulated annealing. Nature 346(6282):343–345.Google Scholar
  • Perdew JP, Burke K, Ernzerhof M (1996) Generalized gradient approximation made simple. Phys. Rev. Lett. 77:3865–3868.Google Scholar
  • Pickard CJ, Needs R (2006) High-pressure phases of silane. Phys. Rev. Lett. 97(4):045504.Google Scholar
  • Pickard CJ, Needs R (2011) Ab initio random structure searching. J. Phys. Condens. Matter. 23(5):053201.Google Scholar
  • Roustant O, Ginsbourger D, Deville Y (2012) Dicekriging, diceoptim: Two r packages for the analysis of computer experiments by kriging-based metamodeling and optimization.Google Scholar
  • Santner TJ, Williams BJ, Notz WI (2018) The Design and Analysis of Computer Experiments (Springer, New York).Google Scholar
  • Schön JC, Jansen M (1996) First step toward planning of syntheses in solid-state chemistry: Determination of promising structure candidates by global optimization. Angew. Chem. Int. Ed. Engl. 35(12):1286–1304.Google Scholar
  • Shahriari B, Bouchard-Côté A, Freitas N (2016) Unbounded bayesian optimization via regularization. Gretton A, Robert CC, eds. Proc. 19th Internat. Conf. Artificial Intelligence Statist., vol. 51, 1168–1176.Google Scholar
  • Siivola E, Paleyes A, González J, Vehtari A (2021) Good practices for bayesian optimization of high dimensional structured spaces. Appl. AI Lett. 2(2):e24.Google Scholar
  • Stillinger FH (1999) Exponential multiplicity of inherent structures. Phys. Rev. E. 59(1):48.Google Scholar
  • Surjanovic S, Bingham D (2013) Virtual library of simulation experiments: Test functions and datasets (Accessed August 21, 2022), https://www.sfu.ca/~ssurjano/.Google Scholar
  • Tekin A, Caputo R, Züttel A (2010) First-principles determination of the ground-state structure of libh 4. Phys. Rev. Lett. 104(21):215501.Google Scholar
  • Therrien F, Jones EB, Stevanović V (2021) Metastable materials discovery in the age of large-scale computation. Appl. Phys. Rev. 8(3):031310.Google Scholar
  • Trimarchi G, Freeman AJ, Zunger A (2009) Predicting stable stoichiometries of compounds via evolutionary global space-group optimization. Phys. Rev. B Condens. Matter Mater. Phys. 80(9):092101.Google Scholar
  • Tripathy R, Bilionis I, Gonzalez M (2016) Gaussian processes with built-in dimensionality reduction: Applications to high-dimensional uncertainty propagation. J. Comput. Phys. 321:191–223.Google Scholar
  • Vu TN, Nayak SK, Nguyen NTT, Alpay SP, Tran H (2021) Atomic configurations for materials research: A case study of some simple binary compounds. AIP Adv. 11(4):045120.Google Scholar
  • Wales DJ, Doye JP (1997) Global optimization by basin-hopping and the lowest energy structures of lennard-jones clusters containing up to 110 atoms. J. Phys. Chem. A. 101(28):5111–5116.Google Scholar
  • Wang L, Yerramilli S, Iyer A, Apley D, Zhu P, Chen W (2022) Scalable gaussian processes for data-driven design using big data with categorical factors. J. Mech. Des. 144(2):021703.Google Scholar
  • Weymuth T, Reiher M (2014) Inverse quantum chemistry: Concepts and strategies for rational compound design. Int. J. Quantum Chem. 114(13):823–837.Google Scholar
  • Xiang H, Huang B, Kan E, Wei S-H, Gong X (2013) Toward direct-gap silicon phases by the inverse band structure design approach. Phys. Rev. Lett. 110(11):118702.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.