2019
DOI: 10.1038/s41524-019-0222-z
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Smart machine learning or discovering meaningful physical and chemical contributions through dimensional stacking

Abstract: Despite remarkable advances in characterization techniques of functional materials yielding an ever growing amount of data, the interplay between the physical and chemical phenomena underpinning materials' functionalities is still often poorly understood. Dimensional reduction techniques have been used to tackle the challenge of understanding materials' behavior, leveraging the very large amount of data available. Here, we present a method for applying physical and chemical constraints to dimensional reduction… Show more

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Cited by 17 publications
(26 citation statements)
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“…[51] Dimensional stacking was applied by simple concatenation of the contact frequency at resonance and piezoresponse data prior to analysis. [39] All raw data and analysis codes are provided in the form of Jupyter notebooks. [52] These notebooks perform the analyses discussed above as well as generate the plots used as figures in this work.…”
Section: Methodsmentioning
confidence: 99%
See 2 more Smart Citations
“…[51] Dimensional stacking was applied by simple concatenation of the contact frequency at resonance and piezoresponse data prior to analysis. [39] All raw data and analysis codes are provided in the form of Jupyter notebooks. [52] These notebooks perform the analyses discussed above as well as generate the plots used as figures in this work.…”
Section: Methodsmentioning
confidence: 99%
“…In order to impart additional physical constraints, dimensional stacking is used to concatenate the piezoresponse and contact resonance data prior to the DL analysis. [39] Such concatenation implies a fundamental correlation between the electromechanical response (as probed by the piezoresponse, PR, which combines A and φ information), and the elastic properties of the material (as tracked by the cantilever-tip resonance) to the applied waveform, as discussed above. We note that the piezoresponse data were also processed by mean removal from each PR curve, and subsequently scaled by a constant factor such that the resulting PR dataset has a variance comparable to that of the resonance curves (for details see the Supporting Information).…”
Section: Ti 08 O 3 Thin Films Through Systematic Analysis and Intromentioning
confidence: 99%
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“…Advantages of the novel materials and structures are obtained mainly due to the capability of AI models in finding the good balance between rationale microstructures and high accuracy, such that to effectively satisfy the performance requirements for those architected materials and structures [286][287][288][289]. Comparing with the applications in other fields, AI has not debuted in materials and structures until recent years [290][291][292][293][294][295][296][297][298][299][300]. Studies have been reported on using AI algorithms to emulate complex biological processes (e.g.…”
Section: Ai and Its Applications In Architected Materials And Structuresmentioning
confidence: 99%
“…In order to analyze and extract meaning from these data sets, machine learning has become increasingly important. [27][28][29][30][31][32][33][34] Dimensionality reduction without loss of important information, de-noising, clustering and identifying characteristic features in data sets have been achieved using supervised and unsupervised machine-learning algorithms.…”
Section: Introductionmentioning
confidence: 99%