Search Results for author: Yixiao Chen

Found 5 papers, 1 papers with code

Ground state phases of the two-dimension electron gas with a unified variational approach

no code implementations29 May 2024 Conor Smith, Yixiao Chen, Ryan Levy, Yubo Yang, Miguel A. Morales, Shiwei Zhang

The two-dimensional electron gas (2DEG) is a fundamental model, which is drawing increasing interest because of recent advances in experimental and theoretical studies of 2D materials.

Efficient Numerical Algorithm for Large-Scale Damped Natural Gradient Descent

no code implementations26 Oct 2023 Yixiao Chen, Hao Xie, Han Wang

We propose a new algorithm for efficiently solving the damped Fisher matrix in large-scale scenarios where the number of parameters significantly exceeds the number of available samples.

DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials

1 code implementation21 Jun 2022 Wenfei Li, Qi Ou, Yixiao Chen, Yu Cao, Renxi Liu, Chunyi Zhang, Daye Zheng, Chun Cai, Xifan Wu, Han Wang, Mohan Chen, Linfeng Zhang

However, for high-level QM methods, such as density functional theory (DFT) at the meta-GGA level and/or with exact exchange, quantum Monte Carlo, etc., generating a sufficient amount of data for training a ML potential has remained computationally challenging due to their high cost.

Efficient Neural Network

DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory

no code implementations1 Aug 2020 Yixiao Chen, Linfeng Zhang, Han Wang, E Weinan

We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functional theory.

BIG-bench Machine Learning

Deep Density: circumventing the Kohn-Sham equations via symmetry preserving neural networks

no code implementations27 Nov 2019 Leonardo Zepeda-Núñez, Yixiao Chen, Jiefu Zhang, Weile Jia, Linfeng Zhang, Lin Lin

By directly targeting at the self-consistent electron density, we demonstrate that the adapted network architecture, called the Deep Density, can effectively represent the electron density as the linear combination of contributions from many local clusters.


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