Abstract
Like any other field of research, materials science involves a lot of trial and error: in the process of creating a new material or device, we will inevitably make several prototypes which fail to perform as hoped.
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Notes
- 1.
Of course, prototyping and trial-and-error are necessary for developing scientific understanding.
References
Quote by President Obama, June 2011 at Carnegie Mellon University. See https://www.obamawhitehouse.archives.gov/mgi for more details.
Hinuma Y, et al. Discovery of earth-abundant nitride semiconductors by computational screening and high-pressure synthesis. Nat Commun. 2016;7:11962.
Seko A, et al. Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization. Phys Rev Lett. 2015;115:205901.
Seko A, et al. Machine learning with systematic density-functional theory calculations: application to melting temperatures of single- and binary-component solids. Phys Rev B. 2014;89:054303.
Balachandran PV, et al. Adaptive strategies for materials design using uncertainties. Sci Rep. 2016;6:19660.
Kiyohara S, et al. Acceleration of stable interface structure searching using a Kriging approach. Jpn J Appl Phys. 2016;55:045502.
Ju S, et al. Designing nanostructures for photon transport via Bayesian optimization. Phys Rev X. 2017;7:021024.
Ueno T, et al. COMBO: An efficient Bayesian optimization library for materials science. Mater Discov. 2016;4:18.
Rupp M, et al. Fast and accurate modeling of molecular atomization energies with machine learning. Phys Rev Lett. 2012;108:058301.
Hansen K, et al. Assessment and validation of machine learning methods for predicting molecular atomization energies. J Chem Theory Comput. 2013;9:3404.
Huo, H, Rupp, M. Unified representation for machine learning of molecules and crystals. arXiv:1704.06439v1 [physics.chem-ph].
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Packwood, D. (2017). Overview of Bayesian Optimization in Materials Science. In: Bayesian Optimization for Materials Science. SpringerBriefs in the Mathematics of Materials, vol 3. Springer, Singapore. https://doi.org/10.1007/978-981-10-6781-5_1
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DOI: https://doi.org/10.1007/978-981-10-6781-5_1
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