Deep elastic strain engineering of bandgap through machine learning

Abstract

Nanoscale specimens of semiconductor materials as diverse as silicon and diamond are now known to be deformable to large elastic strains without inelastic relaxation. These discoveries harbinger a new age of deep elastic strain engineering of the band structure and device performance of electronic materials. Many possibilities remain to be investigated as to what pure silicon can do as the most versatile electronic material and what an ultrawide bandgap material such as diamond, with many appealing functional figures of merit, can offer after overcoming its present commercial immaturity. Deep elastic strain engineering explores full six-dimensional space of admissible nonlinear elastic strain and its effects on physical properties. Here we present a general method that combines machine learning and ab initio calculations to guide strain engineering whereby material properties and performance could be designed. This method invokes recent advances in the field of artificial intelligence by utilizing a limited amount of ab initio data for the training of a surrogate model, predicting electronic bandgap within an accuracy of 8 meV. Our model is capable of discovering the indirect-to-direct bandgap transition and semiconductor-to-metal transition in silicon by scanning the entire strain space. It is also able to identify the most energy-efficient strain pathways that would transform diamond from an ultrawide-bandgap material to a smaller-bandgap semiconductor. A broad framework is presented to tailor any target figure of merit by recourse to deep elastic strain engineering and machine learning for a variety of applications in microelectronics, optoelectronics, photonics, and energy technologies.

A Critical Review of Machine Learning of Energy Materials

“…In addition to the bandgap, the prediction of full band structures has only been attempted on a limited set of materials, e.g., Si . The full band structure provides much richer information for the material and will be a key quantity for future ML predictions.…”

Extraction of mechanical properties of materials through deep learning from instrumented indentation

“…In these situations, it may be advantageous to complement the dataset of expensive experimental measurements by employing synthetic data derived from simulations of physical models. An example of such an approach entails the use of density functional theory calculations to train NNs so that DL algorithms can be developed to determine the least energetically expensive means of modulating the bandgap of a semiconductor material through elastic strain engineering (31).…”

Minimum-strain symmetrization of Bravais lattices

“…Many properties of a crystalline material are governed by the geometry of its Bravais lattice, and changes in the Bravais lattice, both static and dynamic. For example, modification of the Bravais geometry by the imposition of hydrostatic and/or shear strains forms the basis of elastic strain engineering 1 , a field whose successes include improved photoluminescence and electronic spectra in semiconductors [2][3][4] , vibrational properties in micromechanical oscillators 5 , and magnetic properties in multiferroics 6 . Alternatively, the design of materials which can sustain repeated cyclic changes in the Bravais lattice geometry is the fundamental aim of shape memory alloy research [7][8][9] .…”