Massachusetts Institute of Technology

Nanyang Technological University

Brown University

Skolkovo Institute of Science and Technology

Ming Dao

Ju Li

Zhe Shi

Subra Suresh

George E Karniadakis

Alexander Shapeev

Lu Lu

Evgenii Tsymbalov

Evgenii Alekseevich Tsymbalov

Alexander Vasilievich Shapeev

Methods for training statistical models for the bandgap and energy dispersion of materials as a function of an applied strain, as well as uses of these trained statistical models for elastic strain engineering of materials, are described.

Elastic strain engineering of materials

Compositions and methods related to multiaxially straining defect doped materials as well as their use in electrical circuits are generally described.

Multiaxial strain engineering of defect doped materials

Compositions and methods related to straining defect doped materials as well as their methods of use in electrical circuits are generally described.

Elastic strain engineering of defect doped materials

Methods and apparatus for extracting one or more mechanical properties for a material based on one or more indentation parameters for the material. The method comprises receiving load-displacement data from one or more instrumented indentation tests on the material, determining, by at least one computer processor, the indentation parameters for the material based, at least in part, on the received load-displacement data, providing as input to a trained neural network, the indentation parameters for the material, determining, based on an output of the trained neural network, the one or more mechanical properties of the material, and displaying an indication of the determined one or more mechanical properties of the material to a user of the computer system.

Machine learning techniques for estimating mechanical properties of materials

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Ming Dao