首页|New Machine Learning Findings from A.N. Frumkin Institute of Physical Chemistry and Electrochemistry Discussed (Oxide Ceramics of a 2 M 3 O 12 Family With Negat ive and Close-to-zero Thermal Expansion Coefficients: Machine Learning-based ... )
New Machine Learning Findings from A.N. Frumkin Institute of Physical Chemistry and Electrochemistry Discussed (Oxide Ceramics of a 2 M 3 O 12 Family With Negat ive and Close-to-zero Thermal Expansion Coefficients: Machine Learning-based ... )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ma chine Learning. According to news reporting originating in Moscow, Russia, by Ne wsRx journalists, research stated, “Ceramic materials with negative and close -t o -zero coefficients of thermal expansion may open the avenue to the technologie s that have so far been constrained by physical limitations concerned with the t hermal stress or with the insufficient structural stability. Two important chara cteristics of NTE materials which could be used for the evaluation of the possib le area and limitations of the sphere of application for negative thermal expans ion (NTE) materials are the linear thermal expansion coefficient and the transit ion temperature from monoclinic to orthorhombic phase.” The news reporters obtained a quote from the research from the A.N. Frumkin Inst itute of Physical Chemistry and Electrochemistry, “In this study, the machine le arning methods were involved in the analysis of experimental data for NTE oxide ceramics of A 2 M 3 O 12 family (where M is Mo 6 + , W 6 + , V 5 + or P 5 + whil e A position may be accommodated by the wide range of metal cations). The models are characterized by the following statistical coefficients: the determination coefficient R 2 = 0.81 and prediction error RMSE = 1.170 for linear thermal expa nsion coefficient; the corresponding parameters for the phase transition tempera ture were assessed as 0.81 and 82.239, respectively. Ionic conductivity in this class of compounds has been discussed as a tandem functional characteristic, emp hasizing the role of anharmonicity in both characteristics. The role of synthesi s route and defect chemistry in NTE was analyzed. A conclusion on the expected e nhancement of NTE resulted from the intentional introduction of cation A vacanci es has been made.”
MoscowRussiaCyborgsEmerging Techno logiesMachine LearningA.N. Frumkin Institute of Physical Chemistry and Elect rochemistry
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