1 code implementation • 3 Mar 2022 • He Ma, Arunachalam Narayanaswamy, Patrick Riley, Li Li
Systematic development of accurate density functionals has been a decades-long challenge for scientists.
2 code implementations • 16 May 2020 • He Ma, Wennie Wang, Siyoung Kim, Man-Hin Cheng, Marco Govoni, Giulia Galli
We present PyCDFT, a Python package to compute diabatic states using constrained density functional theory (CDFT).
Materials Science
no code implementations • 25 Feb 2020 • He Ma, Marco Govoni, Giulia Galli
Quantum computers hold promise to enable efficient simulations of the properties of molecules and materials; however, at present they only permit ab initio calculations of a few atoms, due to a limited number of qubits.
Materials Science Chemical Physics Quantum Physics
no code implementations • 25 Apr 2019 • Zhihao Fang, Wanyi Zhang, He Ma
We first utilize the Region of Interest (ROI) extraction based on Simple Linear Iterative Clustering (SLIC) algorithm and region growing algorithm to extract the ROI at the super-pixel level.
no code implementations • 7 Dec 2018 • Zhihao Fang, He Ma, Xuemin Zhu, Xutao Guo, Ruixin Zhou
3D reconstruction is a fundamental issue in many applications and the feature point matching problem is a key step while reconstructing target objects.
no code implementations • ICLR 2018 • Daniel Jiwoong Im, He Ma, Graham Taylor, Kristin Branson
Generative adversarial networks (GANs) have been extremely effective in approximating complex distributions of high-dimensional, input data samples, and substantial progress has been made in understanding and improving GAN performance in terms of both theory and application.
no code implementations • 13 Dec 2016 • Daniel Jiwoong Im, He Ma, Chris Dongjoo Kim, Graham Taylor
Generative Adversarial Networks have become one of the most studied frameworks for unsupervised learning due to their intuitive formulation.
1 code implementation • 26 May 2016 • He Ma, Fei Mao, Graham W. Taylor
We develop a scalable and extendable training framework that can utilize GPUs across nodes in a cluster and accelerate the training of deep learning models based on data parallelism.