53 citations · 138 across the 9 of their papers we have counts for
9 papers
Manifold Learning in Atomistic Simulations: A Conceptual Review
Jakub Rydzewski, Ming Chen, Omar Valsson
Analyzing large volumes of high-dimensional data requires dimensionality reduction: finding meaningful low-dimensional structures hidden in their high-dimensional observations. Suc…
Structure Optimization with Stochastic Density Functional Theory
Ming Chen, Roi Baer, Eran Rabani
Linear-scaling techniques for Kohn-Sham density functional theory (KS-DFT) are essential to describe the ground state properties of extended systems. Still, these techniques often…
Stochastic Density Functional Theory: Real- and Energy-Space Fragmentation for Noise Reduction
Ming Chen, Roi Baer, Daniel Neuhauser +1
Stochastic density functional theory (sDFT) is becoming a valuable tool for studying ground state properties of extended materials. The computational complexity of describing the K…
Range-Separated Stochastic Resolution of Identity: Formulation and Application to Second Order Green's Function Theory
Wenjie Dou, Ming Chen, Tyler Y. Takeshita +3
We develop a range-separated stochastic resolution of identity approach for the -index electron repulsion integrals, where the larger terms (above a predefined threshold) are tr…
Dopant levels in large nanocrystals using stochastic optimally tuned range-separated hybrid density functional theory
Alex J. Lee, Ming Chen, Wenfei Li +3
We apply a stochastic version of an optimally tuned range-separated hybrid functional to provide insight on the electronic properties of P- and B- doped Si nanocrystals of experime…
Stochastic Resolution of Identity for Real-Time Second-Order Green's Function: Ionization Potential and Quasi-particle Spectrum
Wenjie Dou, Tyler Y. Takeshita, Ming Chen +3
We develop a stochastic resolution of identity approach to the real-time second-order Green's function (real-time sRI-GF2) theory, extending our recent work for imaginary-time Mats…