To switch or not to switch – a machine learning approach for ferroelectricity

Neumayer, Sabine M.; Jesse, Stephen; Velarde, Gabriel; Kholkin, A. L.; Kravchenko, Ivan I.; Martin, Lane W.; Balke, Nina; Maksymovych, Peter

doi:10.1039/c9na00731h

Cited by 13 publications

(11 citation statements)

References 43 publications

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“…Other application-based use-cases of ML within material science include superconductors, [57,114,115] topological insulators, [112,116] ferroelectric materials, [113,[117][118][119] piezoelectric materials, [120][121][122] supercapacitors, [123][124][125] and 3D bioprinting. [126,127] This list is expected to grow in the near future, as access to trained personnel and high-throughput robotics increases.…”

Section: (12 Of 18)mentioning

confidence: 99%

Applied Machine Learning for Developing Next‐Generation Functional Materials

Dinic

Singh

Dong

et al. 2021

Adv Funct Materials

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Machine learning (ML) is a versatile technique to rapidly and efficiently generate insights from multidimensional data. It offers a much-needed avenue to accelerate the exploration and investigation of new materials to address timesensitive global challenges such as climate change. The availability of large datasets in recent years has enabled the development of ML algorithms for various applications including experimental/device optimization and material discovery. This perspective provides a summary of the recent applications of ML in material discovery in a range of fields, from optoelectronics to batteries and electrocatalysis, as well as an overview of the methods behind these advances. The paper also attempts to summarize some key challenges and trends in current research methodologies.

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Section: (12 Of 18)mentioning

confidence: 99%

Applied Machine Learning for Developing Next‐Generation Functional Materials

Dinic

Singh

Dong

et al. 2021

Adv Funct Materials

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“…Another recent example of application to a non-topographical SPM technique is the study of ferroelectric switching [ 135 ]. This switching is a function of both reading and writing voltages, and can vary with experimental conditions such as time and temperature, and is further complicated by competing processes.…”

Section: Reviewmentioning

confidence: 99%

The role of convolutional neural networks in scanning probe microscopy: a review

Azuri¹,

Rosenhek‐Goldian²,

Regev‐Rudzki³

et al. 2021

Beilstein J. Nanotechnol.

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Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

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“…34 Nowadays, these branches develop advanced methods and can be divided into four categories, that is, clas-sication, regression, clustering, and dimensionality reduction. [35][36][37][38][39][40][41][42][43][44] The algorithms for these branches include support vector machine (SVM), k-nearest neighbor (kNN), decision tree (DT), convolutional neural network (CNN), k-means, PCA, etc. [45][46][47][48][49][50] These algorithms have been well employed in SEIRA and SERS.…”

Section: Introductionmentioning

confidence: 99%