Journal ArticleDOI
Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability
TLDR
By applying the support vector classification method, models for predicting the GFA of binary metallic alloys from random compositions are developed and suggest that machine learning is very powerful and efficient and has great potential for discovering new metallic glasses with good GFA.Abstract:
The prediction of the glass-forming ability (GFA) by varying the composition of alloys is a challenging problem in glass physics, as well as a problem for industry, with enormous financial ramifications. Although different empirical guides for the prediction of GFA were established over decades, a comprehensive model or approach that is able to deal with as many variables as possible simultaneously for efficiently predicting good glass formers is still highly desirable. Here, by applying the support vector classification method, we develop models for predicting the GFA of binary metallic alloys from random compositions. The effect of different input descriptors on GFA were evaluated, and the best prediction model was selected, which shows that the information related to liquidus temperatures plays a key role in the GFA of alloys. On the basis of this model, good glass formers can be predicted with high efficiency. The prediction efficiency can be further enhanced by improving larger database and refined i...read more
Citations
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Journal Article
Big Data of Materials Science -- Critical Role of the Descriptor
TL;DR: A trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful when the scientific connection between the descriptor and the actuating mechanisms is unclear.
Journal ArticleDOI
Machine learning assisted design of high entropy alloys with desired property
Cheng Wen,Cheng Wen,Yan Zhang,Changxin Wang,Dezhen Xue,Yang Bai,Stoichko Antonov,Lanhong Dai,Turab Lookman,Yanjing Su +9 more
TL;DR: In this article, a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system was proposed.
Journal ArticleDOI
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
TL;DR: A robust chemistry consisting of a nearsighted neural network potential, TensorMol-0.1, with screened long-range electrostatic and van der Waals physics is constructed and achieves millihartree accuracy and a scalability to tens-of-thousands of atoms on ordinary laptops.
Journal ArticleDOI
Using deep neural network with small dataset to predict material defects
TL;DR: This study attempted to predict solidification defects by DNN regression with a small dataset that contains 487 data points and found that a pre-trained and fine-tuned DNN shows better generalization performance over shallow neural network, support vector machine, and DNN trained by conventional methods.
Journal ArticleDOI
Fe-based bulk metallic glasses: Glass formation, fabrication, properties and applications
TL;DR: In this article, the authors present the research development and achievements of Fe-based BMGs, including their preparation, glass-forming ability, crystallization characteristics, mechanical properties, corrosion behaviors, soft and hard magnetic properties, and industrial applications.
References
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Scikit-learn: Machine Learning in Python
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Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
Bulk metallic glasses
W.H. Wang,C. Dong,Chan Hung Shek +2 more
TL;DR: In this article, the authors reviewed the recent development of new alloy systems of bulk metallic glasses and the properties and processing technologies relevant to the industrial applications of these alloys are also discussed.
Journal ArticleDOI
Non-crystalline Structure in Solidified Gold–Silicon Alloys
TL;DR: This article showed that amorphous configurations have been retained in the solid state by cooling from the melt with sufficient celerity so as to prevent formation of the equilibrium crystalline structures in solid metals and alloys.