References
- Pilania G, Wang C, Jiang X, et al. Accelerating materials property predictions using machine learning. Sci. Rep. 2013;3:1–6.
- Meredig B, Agrawal A, Kirklin S, et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B. 2014;89:094104.
- Seko A, Togo A, Hayashi H, et al. Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization. Phys. Rev. Lett. 2015;115:205901.
- Ikebata H, Hongo K, Isomura T, et al. Bayesian molecular design with a chemical language model. J. Comput. Aided. Mol. 2017;31:379–391.
- Ju S, Shiga T, Feng L, et al. Designing nanostructures for phonon transport via Bayesian optimization. Phys. Rev. X. 2017;7:021024.
- Kim E, Huang K, Jegelka S, et al. Virtual screening of inorganic materials synthesis parameters with deep learning. npj Comput. Mater. 2017;3:1–9.
- Pilania G, Gubernatis JE, Lookman T. Multi-fidelity machine learning models for accurate bandgap predictions of solids. Comput. Mater. Sci. 2017;129:156–163.
- Dieb TM, Ju S, Yoshizoe K, et al. MDTS: automatic complex materials design using Monte Carlo tree search. Sci. Technol. Adv. Mater. 2017;18:498–503.
- Yang X, Zhang J, Yoshizoe K, et al. ChemTS: an efficient python library for de novo molecular generation. Sci. Technol. Adv. Mater. 2017;18:972–976.
- Gómez-Bombarelli R, Wei JN, Duvenaud D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 2018;4:268–276.
- Sawada R, Iwasaki Y, Ishida M. Boosting material modeling using game tree search. Phys. Rev. Mater. 2018;2:103802.
- Sumita M, Yang X, Ishihara S, et al. Hunting for organic molecules with artificial intelligence: molecules optimized for desired excitation energies. ACS Cent. Sci. 2018;4:1126–1133.
- Sakurai A, Yada K, Simomura T, et al. Ultranarrow-band wavelength-selective thermal emission with aperiodic multilayered metamaterials designed by Bayesian optimization. ACS Cent. Sci. 2019;5:319–326.
- Wu S, Kondo Y, Kakimoto M, et al. Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm. npj Comput. Mater. 2019;5:1–11.
- Robeson LM. Correlation of separation factor versus permeability for polymeric membranes. J. Memb. Sci. 1991;62:165–185.
- Rajan K. Materials informatics. Mater. Today. 2005;8:38–45.
- Balachandran PV, Theiler J, Rondinelli JM, et al. Materials prediction via classification learning. Sci. Rep. 2015;5:1–16.
- Pilania G, Mannodi-Kanakkithodi A, Uberuaga BP, et al. Machine learning bandgaps of double perovskites. Sci. Rep. 2016;6:1–10.
- Svetnik V, Liaw A, Tong C, et al. Random forest: A classification and regression tool for compound classification and QSAR modeling. J. Chem. Inf. Comput. Sci. 2003;43:1947–1958.
- Carrete J, Li W, Mingo N, et al. Finding unprecedentedly low-thermal-conductivity half-heusler semiconductors via high-throughput materials modeling. Phys. Rev. X. 2014;4:011019.
- Attarian Shandiz M, Gauvin R. Application of machine learning methods for the prediction of crystal system of cathode materials in lithium-ion batteries. Comput. Mater. Sci. 2016;117:270–278.
- Stanev V, Oses C, Kusne AG, et al. Machine learning modeling of superconducting critical temperature. npj Comput. Mater. 2018;4:1–14.
- Toyao T, Suzuki K, Kikuchi S, et al. Toward effective utilization of methane: machine learning prediction of adsorption energies on metal alloys. J. Phys. Chem. C. 2018;122:8315–8326.
- Ghiringhelli LM, Vybiral J, Levchenko SV, et al. Big data of materials science: critical role of the descriptor. Phys. Rev. Lett. 2015;114:105503.
- Ghiringhelli LM, Vybiral J, Ahmetcik E, et al. Learning physical descriptors for materials science by compressed sensing. New J. Phys. 2017;19:023017.
- Ouyang R, Curtarolo S, Ahmetcik E, et al. SISSO: a compressed-sensing method for identifying the best low-dimensional descriptor in an immensity of offered candidates. Phys. Rev. Mater. 2018;2:083802.
- Igarashi Y, Ichikawa H, Nakanishi-Ohno Y, et al. ES-DoS: exhaustive search and density-of-states estimation as a general framework for sparse variable selection. J Phys Conf Ser. 2018;1036:012001.
- Igarashi Y, Takenaka H, Nakanishi-Ohno Y, et al. Exhaustive search for sparse variable selection in linear regression. J. Phys. Soc. Jpn. 2018;87:044802.
- Sodeyama K, Igarashi Y, Nakayama T, et al. Liquid electrolyte informatics using an exhaustive search with linear regression. Phys. Chem. Chem. Phys. 2018;20:22585–22591.
- Schmidt J, Marques MRG, Botti S, et al. Recent advances and applications of machine learning in solid-state materials science. npj Comput. Mater. 2019;5:1–36.
- Materials Project. [cited 2020 Jan 1]. Available from: https://materialsproject.org/
- Organic Materials Database. [cited 2020 Jan 1]. Available from: https://omdb.mathub.io/
- Stevanović V, Lany S, Zhang X, et al. Correcting density functional theory for accurate predictions of compound enthalpies of formation: fitted elemental-phase reference energies. Phys. Rev. B. 2012;85:115104.
- NIMS materials database (MatNavi). [cited 2020 Jan 1]. Available from: https://mits.nims.go.jp/index_en.html
- NIST alloy data. [cited 2020 Jan 1]. Available from: https://www.nist.gov/mml/acmd/trc/nist-alloy-data
- Mondolfo LF. Aluminum alloys. London: Butterworths; 1976.
- Davis JR. Aluminum and aluminum alloys. Netherlands: ASM International; 1993.
- Hirsch J, Skrotzki B, Gottstein G. Aluminium alloys: the physical and mechanical properties. New York: John Wiley & Sons; 2008.
- Total Materia. [cited 2018 Apr 1]. Available from: https://www.totalmateria.com/page.aspx?ID=Home&LN=EN
- Aluminum database. [cited 2018 Apr 1]. Available from: http://metal.matdb.jp/JAA-DB/
- ISO 2107. Aluminium and aluminium alloys - Wrought products - Temper designations.
- JIS H 0001. Aluminium, magnesium and their alloys - Temper designation.
- scikit-learn. Available from: https://scikit-learn.org/stable/
- Ueno T, Rhone TD, Hou Z, et al. An efficient Bayesian optimization library for materials science. Mater. Discovery. 2016;4:18–21.
- emcee. [cited 2019 Apr 1]. Available from: https://emcee.readthedocs.io/en/stable/
- Foreman-Mackey D, Hogg DW, Lang D, et al. emcee: the MCMC Hammer. Publ. Astron. Soc. Pac. 2013;125:306. .
- Goodman J, Weare J. Ensemble samplers with affine invariance. Commun. Appl. Math. Comput. Sci. 2010;5:65–80. .
- Tamura R, Hukushima K. Method for estimating spin-spin interactions from magnetization curves. Phys. Rev. B. 2017;95:064407.
- Obinata K, Katakami S, Yue Y, et al. Ising model parameter estimation with confidence evaluation using the exchange Monte Carlo method. J. Phys. Soc. Jpn. 2019;88:064802.
- Shinotsuka H, Nagata K, Yoshikawa H, et al. Development of spectral decomposition based on Bayesian information criterion with estimation of confidence interval. Sci. Technol. Adv. Mater. 2020;21:402–419.
- Boslaugh S. Statistics in a Nutshell. Sebastopol: O’Reilly Media, Inc; 2012.
- pandas.DataFrame.corr. [cited 2018 May 1]. Available from: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.corr.html
- Durrheim K, Tredoux C. Numbers, hypotheses & conclusions: a course in statistics for the social sciences. Cape Town: Juta and Company Ltd; 2004.
- Sato S, Endo T. Relation between tensile strength and hardness of aluminum alloys. J. Jpn. Inst. Light. Met. 1986;36:29–35.
- Kim JG, Baek SM, Lee HH, et al. Suppressed deformation instability in the twinning-induced plasticity steel-cored three-layer steel sheet. Acta Materialia. 2018;147:304–312.
- Liu R, Tian YZ, Zhang ZJ, et al. Exploring the fatigue strength improvement of Cu-Al alloys. Acta Materialia. 2018;144:613–626.
- IE-MCMC. [cited 2020 Jun 1]. Available from: https://github.com/rtmr/IE-MCMC