Machine learning sparse tight-binding parameters for defects

Sparse Greedy Matrix Approximation for Machine Learning

Transferable Orthogonal Tight Binding Parameters for CdS, CdSe and CdTe

Tight Binding (TB) method was first developed by Slater and Koster[1] for band structure calculations. With the advent of nanostructures, the method is experiencing revival due to its computational speed and capability of retaining the quantum mechanical description essential for electronic structure calculations. Despite being a parametric technique, TB method has added advantage of transferab...

Calculation for Energy of (111) Surfaces of Palladium in Tight Binding Model

In this work calculation of energetics of transition metal surfaces is presented. The tight-binding model is employed in order to calculate the energetics. The tight-binding basis set is limited to d orbitals which are valid for elements at the end of transition metals series. In our analysis we concentrated on electronic effects at temperature T=0 K, this means that no entropic term will be pr...

SparCML: High-Performance Sparse Communication for Machine Learning

One of the main drivers behind the rapid recent advances in machine learning has been the availability of efficient system support. This comes both through faster hardware, but also in the form of efficient software frameworks and programming models. Despite existing progress, scaling compute-intensive machine learning workloads to a large number of compute nodes is still a challenging task. In...

A dimension adaptive sparse grid combination technique for machine learning

We introduce a dimension adaptive sparse grid combination technique for the machine learning problems of classification and regression. A function over a d-dimensional space, which assumedly describes the relationship between the features and the response variable, is reconstructed using a linear combination of partial functions who possibly depend only on a subset of all features. The partial ...