SPARSE REPRESENTATION FOR MACHINE LEARNING THE PROPERTIES OF DEFECTS IN 2D MATERIALS
A method for predicting at least one property of a crystal of a material, with the crystal exhibiting at least one point defect. At least one module samples structures of materials and point defects of the crystal. The method comprises: providing a neural network. The neural network comprises: recei...
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| creator | Kazeev, Nikita Faleev, Maxim Protasov, Stanislav Al-Maeeni, Abdalaziz Rashid Lukin, Ruslan Tormasov, Alexander Romanov, Ignat Bell, Serg Ustyuzhanin, Andrey Dobrovolskiy, Nikolay |
| description | A method for predicting at least one property of a crystal of a material, with the crystal exhibiting at least one point defect. At least one module samples structures of materials and point defects of the crystal. The method comprises: providing a neural network. The neural network comprises: receiving as input a structure of the material and an ideal crystal unit cell structure. The neural network also comprises: outputting at least one target quantity and using a generated set of data and representing the point defect. The point defect represents a set of coordinates and a type of the point defect and a crystal unit cell structure. The neural network further comprises: receiving as input at least one of a cloud of defect points and a global state vector. The neural network additionally comprises outputting a vector. |
| format | Patent |
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| subjects | INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS PHYSICS |
| title | SPARSE REPRESENTATION FOR MACHINE LEARNING THE PROPERTIES OF DEFECTS IN 2D MATERIALS |
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