Efficient Phase Diagram Sampling by Active Learning

Dai, Chengyu; Bruss, Isaac; Glotzer, Sharon C.

doi:10.48550/arxiv.1803.03296

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Thus, we speculate that active learning is an essential tool to efficiently construct a phase diagram. A previous study employed an active learning method that uses a Gaussian process to sample the phase diagram [18]. Although this method dramatically reduces the number of sampling points, the demonstration was only performed using a phase diagram with only two kinds of phases.…”

Section: Introductionmentioning

confidence: 99%

Efficient construction method for phase diagrams using uncertainty sampling

Terayama

Tamura

Nosé

et al. 2019

Phys. Rev. Materials

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We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phase can be rapidly found, and smaller number of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science.

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Section: Introductionmentioning

confidence: 99%

Efficient construction method for phase diagrams using uncertainty sampling

Terayama

Tamura

Nosé

et al. 2019

Phys. Rev. Materials

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“…Furthermore, other MI techniques can also propose next candidate points to efficiently construct phase diagrams. 13,14) Enormous machine learning tools, which can be adopted for MI research, have been developed and released. However, many of these tools are developed as a code of specific programming languages such as Python.…”

Section: Introductionmentioning

confidence: 99%

Efficient recommendation tool of materials by an executable file based on machine learning

Terayama

Tsuda

Tamura

2019

Jpn. J. Appl. Phys.

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To accelerate the discoveries of novel materials, an easy-to-use materials informatics tool is essential. We develop materials informatics applications, which can be executed on a Windows computer without any special settings. Our applications efficiently perform Bayesian optimization to optimize materials properties and uncertainty sampling to complete a new phase diagram. We introduce the usage of these applications and show the sampling results for a ternary phase diagram.

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Efficient Phase Diagram Sampling by Active Learning

Cited by 2 publications

References 4 publications

Efficient construction method for phase diagrams using uncertainty sampling

Efficient construction method for phase diagrams using uncertainty sampling

Efficient recommendation tool of materials by an executable file based on machine learning

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