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Machine learning properties of binary wurtzite superlattices
Pilania, G. ; Liu, X.-Y.
Journal of materials science, 2018-05, Vol.53 (9), p.6652-6664
[Periódico revisado por pares]
New York: Springer US
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Título:
Machine learning properties of binary wurtzite superlattices
Autor:
Pilania, G.
;
Liu, X.-Y.
Assuntos:
Artificial intelligence
;
Characterization and Evaluation of Materials
;
Chemistry and Materials Science
;
Classical Mechanics
;
Computation
;
Crystallography and Scattering Methods
;
Elastic properties
;
Learning strategies
;
Machine learning
;
Material properties
;
materials informatics, wurtzite superlattices, binary octets, statistical learning
;
Materials information
;
MATERIALS SCIENCE
;
Nuclear energy
;
Organic chemistry
;
Polymer Sciences
;
Prediction models
;
Quantum mechanics
;
Solid Mechanics
;
Statistical methods
;
Superlattices
;
Wurtzite
É parte de:
Journal of materials science, 2018-05, Vol.53 (9), p.6652-6664
Notas:
LA-UR-17-30057
AC52-06NA25396
USDOE National Nuclear Security Administration (NNSA)
Descrição:
The burgeoning paradigm of high-throughput computations and materials informatics brings new opportunities in terms of targeted materials design and discovery. The discovery process can be significantly accelerated and streamlined if one can learn effectively from available knowledge and past data to predict materials properties efficiently. Indeed, a very active area in materials science research is to develop machine learning based methods that can deliver automated and cross-validated predictive models using either already available materials data or new data generated in a targeted manner. In the present contribution, we show that fast and accurate predictions of a wide range of properties of binary wurtzite superlattices, formed by a diverse set of chemistries, can be made by employing state-of-the-art statistical learning methods trained on quantum mechanical computations in combination with a judiciously chosen numerical representation to encode materials’ similarity. These surrogate learning models then allow for efficient screening of vast chemical spaces by providing instant predictions of the targeted properties. Moreover, the models can be systematically improved in an adaptive manner, incorporate properties computed at different levels of fidelities and are naturally amenable to inverse materials design strategies. While the learning approach to make predictions for a wide range of properties (including structural, elastic and electronic properties) is demonstrated here for a specific example set containing more than 1200 binary wurtzite superlattices, the adopted framework is equally applicable to other classes of materials as well.
Editor:
New York: Springer US
Idioma:
Inglês
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