The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials.With the recent discovery of large elastic deformation in nanoscale specimens a...The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials.With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond,employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit,through the design of an optimal straining pathway.Such simulations,however,call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties.Motivated by this challenge,we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space.These calculations enable us to identify ways in which the physical properties can be altered through“deep”elastic strain engineering up to a large fraction of the ideal strain.Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure.By training a surrogate model with ab initio computational data,our method can identify the most efficient strain energy pathway to realize physical property changes.The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass.We illustrate the applications of the method with specific results for diamonds,although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials。展开更多
基金The computations involved in this work were conducted on the computer cluster at Skolkovo Institute of Science and Technology(Skoltech)CEST Multiscale Molecular Modelling group and Massachusetts Institute of Technology(MIT)Nuclear Science Engineering department.E.T.,Z.S.,A.S.,and J.L.acknowledge support by the Skoltech-MIT Next Generation Program 2016-7/NGPE.T.and A.S.acknowledge support by the Center for Integrated Nanotechnologies,an Office of Science User Facility operated for the U.S.Department of Energy Office of Science by Los Alamos National Laboratory(Contract 89233218CNA000001)+1 种基金Sandia National Laboratories(Contract DE-NA-0003525)M.D.acknowledges support from MIT J-Clinic for Machine Learning and Health.S.S.acknowledges support from Nanyang Technological University through the Distinguished University Professorship.
文摘The controlled introduction of elastic strains is an appealing strategy for modulating the physical properties of semiconductor materials.With the recent discovery of large elastic deformation in nanoscale specimens as diverse as silicon and diamond,employing this strategy to improve device performance necessitates first-principles computations of the fundamental electronic band structure and target figures-of-merit,through the design of an optimal straining pathway.Such simulations,however,call for approaches that combine deep learning algorithms and physics of deformation with band structure calculations to custom-design electronic and optical properties.Motivated by this challenge,we present here details of a machine learning framework involving convolutional neural networks to represent the topology and curvature of band structures in k-space.These calculations enable us to identify ways in which the physical properties can be altered through“deep”elastic strain engineering up to a large fraction of the ideal strain.Algorithms capable of active learning and informed by the underlying physics were presented here for predicting the bandgap and the band structure.By training a surrogate model with ab initio computational data,our method can identify the most efficient strain energy pathway to realize physical property changes.The power of this method is further demonstrated with results from the prediction of strain states that influence the effective electron mass.We illustrate the applications of the method with specific results for diamonds,although the general deep learning technique presented here is potentially useful for optimizing the physical properties of a wide variety of semiconductor materials。
