When I was working on quantum transport at Tianjin University, I often used the non-equilibrium Green's function (NEGF) method to calculate electron transmission and current through a device. Given a Hamiltonian describing electronic energies and couplings, along with electrodes and an applied bias, the program produces a result. But if we want to know how much the current changes when a parameter is adjusted, we usually have to run the calculation again. That gets slow when there are many parameters.
From 80307810bf05dfdbcf7834d0c21fc7768c1890b7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zhanghao=20Zhouyin=20=28=E5=91=A8=E5=AF=85=E5=BC=A0?=
=?UTF-8?q?=E7=9A=93=29?= <64253517+floatingCatty@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:14:44 -0400
Subject: [PATCH 02/20] Add full Chinese ad-negf article with reciprocal
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+---
+layout: research
+permalink: /zh/research/ad-negf/
+title: "AD-NEGF：可微的量子输运计算"
+title_en: "AD-NEGF: differentiable quantum transport calculations"
+description: "AD-NEGF computes gradients through tight-binding quantum-transport calculations for sensitivity analysis and inverse Hamiltonian design."
+description_zh: "AD-NEGF 为紧束缚量子输运计算增加梯度，用于灵敏度分析和哈密顿量逆向设计。"
+paper_year: 2023
+paper_url: "https://doi.org/10.1103/PhysRevB.108.195143"
+code_url: "https://github.com/floatingCatty/ADNEGF"
+research_locale: zh
+translation_url: "/research/ad-negf/"
+translation_of: "/research/ad-negf/"
+alternate_en_url: "/research/ad-negf/"
+alternate_zh_url: "/zh/research/ad-negf/"
+---
+
+
A quantum transport calculation usually begins with a Hamiltonian. Constructing it directly from DFT can be reliable, but it becomes expensive when the system is large or when we want to compare many different structures. DeePTB can predict a tight-binding Hamiltonian from an atomic structure, which raises a natural question: can we use that Hamiltonian directly in a quantum transport solver?
From dae476a363a4509df8b54348c89addf2c4237ed2 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zhanghao=20Zhouyin=20=28=E5=91=A8=E5=AF=85=E5=BC=A0?=
=?UTF-8?q?=E7=9A=93=29?= <64253517+floatingCatty@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:14:51 -0400
Subject: [PATCH 04/20] Add full Chinese deeptb-negf article with reciprocal
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+---
+layout: research
+permalink: /zh/research/deeptb-negf/
+title: "DeePTB-NEGF：从学习到的哈密顿量计算量子输运"
+title_en: "DeePTB-NEGF: transport from learned Hamiltonians"
+description: "A DeePTB and DPNEGF workflow for quantum transport in graphene, h-BN and MoS2, compared with first-principles DFT-NEGF calculations."
+description_zh: "DeePTB 和 DPNEGF 结合，计算石墨烯、氮化硼和二硫化钼的量子输运，并与 DFT-NEGF 对照。"
+paper_year: 2025
+paper_url: "https://arxiv.org/abs/2512.11291"
+code_url: "https://github.com/deepmodeling/dpnegf"
+research_locale: zh
+translation_url: "/research/deeptb-negf/"
+translation_of: "/research/deeptb-negf/"
+alternate_en_url: "/research/deeptb-negf/"
+alternate_zh_url: "/zh/research/deeptb-negf/"
+---
+
+
FeRh is an alloy of iron and rhodium whose magnetism is unusually sensitive to the lattice constant. A small change in the distance between atoms can change the relative energies of different magnetic states. That makes it a difficult system for DFT: how much of a prediction comes from the material, and how much comes from the approximation we chose?
From 3d64b87fb34762d57b2731512021adce46ed7f10 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zhanghao=20Zhouyin=20=28=E5=91=A8=E5=AF=85=E5=BC=A0?=
=?UTF-8?q?=E7=9A=93=29?= <64253517+floatingCatty@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:14:58 -0400
Subject: [PATCH 06/20] Add full Chinese ferh-magnetism article with reciprocal
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+---
+layout: research
+permalink: /zh/research/ferh-magnetism/
+title: "FeRh 中交换关联泛函对磁性的影响"
+title_en: "Exchange-correlation functionals and magnetism in FeRh"
+description: "A note on why density-functional predictions of magnetism in FeRh change with lattice constant and exchange-correlation functional."
+description_zh: "关于 FeRh 合金的磁性预测为什么依赖晶格常数和 DFT 交换关联泛函。"
+paper_year: 2025
+paper_url: "https://doi.org/10.1016/j.commatsci.2024.113561"
+research_locale: zh
+translation_url: "/research/ferh-magnetism/"
+translation_of: "/research/ferh-magnetism/"
+alternate_en_url: "/research/ferh-magnetism/"
+alternate_zh_url: "/zh/research/ferh-magnetism/"
+---
+
+
As an undergraduate, I spent some time studying neural-network theory. I was interested in what happens to information inside a network: what survives as features pass through layers, and what gets discarded. Information theory gives us entropy and mutual information, but estimating them for high-dimensional neural-network features is difficult in practice.
From 4548bf60b84f02df1e43b6806e54a765308990af Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zhanghao=20Zhouyin=20=28=E5=91=A8=E5=AF=85=E5=BC=A0?=
=?UTF-8?q?=E7=9A=93=29?= <64253517+floatingCatty@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:15:05 -0400
Subject: [PATCH 08/20] Add full Chinese logdet-entropy article with reciprocal
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_pages/research-logdet-entropy-zh.md | 32 ++++++++++++++++++++++++++++
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@@ -0,0 +1,32 @@
+---
+layout: research
+permalink: /zh/research/logdet-entropy/
+title: "LogDet：用矩阵估计神经网络里的信息量"
+title_en: "LogDet: estimating information in neural networks"
+description: "The LogDet entropy estimator measures high-dimensional information using matrix operations and was applied to the analysis of neural networks."
+description_zh: "LogDet 熵估计器利用矩阵运算处理高维数据，用于分析神经网络特征中的信息变化。"
+paper_year: 2025
+paper_url: "https://doi.org/10.1016/j.neucom.2025.131520"
+paper_title: "Understanding neural networks with logarithm determinant entropy estimator"
+paper_doi: "10.1016/j.neucom.2025.131520"
+paper_authors:
+ - "Zhanghao Zhouyin"
+ - "Ding Liu"
+research_locale: zh
+translation_url: "/research/logdet-entropy/"
+translation_of: "/research/logdet-entropy/"
+alternate_en_url: "/research/logdet-entropy/"
+alternate_zh_url: "/zh/research/logdet-entropy/"
+---
+
+
Put two sheets of graphene on top of each other and rotate one slightly. A moiré pattern appears, much larger than the original atomic lattice. As the twist angle gets smaller, the moiré period grows and the atoms rearrange. In a recent project, I wanted to understand what causes this reconstruction near the magic angle.
From be2c6c7004bd5bb8841d52d320fbd2d460536506 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zhanghao=20Zhouyin=20=28=E5=91=A8=E5=AF=85=E5=BC=A0?=
=?UTF-8?q?=E7=9A=93=29?= <64253517+floatingCatty@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:15:19 -0400
Subject: [PATCH 10/20] Add Chinese moire-phonon research article with
reciprocal hreflang
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_pages/research-moire-phonon-zh.md | 34 ++++++++++++++++++++++++++++++
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diff --git a/_pages/research-moire-phonon-zh.md b/_pages/research-moire-phonon-zh.md
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+---
+layout: research
+permalink: /zh/research/moire-phonon/
+title: "魔角石墨烯里的声子凝聚"
+title_en: "Moiré phonon condensation in twisted bilayer graphene"
+description: "A note on moiré phonon condensation, soft modes, and structural reconstruction in magic-angle twisted bilayer graphene."
+description_zh: "关于魔角双层石墨烯结构重构、软声子模式和莫尔声子凝聚的一些计算与想法。"
+paper_year: 2026
+paper_url: "https://arxiv.org/abs/2607.06711"
+paper_title: "Moiré Phonon Condensation in Magic-Angle Twisted Bilayer Graphene"
+paper_doi: "10.48550/arXiv.2607.06711"
+paper_authors:
+ - "Zhanghao Zhouyin"
+ - "Jyun-jie Jiang"
+ - "Xianghua Kong"
+ - "Hong Guo"
+research_locale: zh
+translation_url: "/research/moire-phonon/"
+translation_of: "/research/moire-phonon/"
+alternate_en_url: "/research/moire-phonon/"
+alternate_zh_url: "/zh/research/moire-phonon/"
+---
+
+
When electrons interact strongly, calculating the electronic state of a material becomes difficult. We could try solving for many electrons at once, but the number of possible quantum states grows rapidly with system size. Quantum embedding takes a different approach. It treats a smaller, difficult part as an impurity problem and repeatedly exchanges information between that problem and an effective environment.
From b1c92f735d78f31f513970348743701b8b798afb Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zhanghao=20Zhouyin=20=28=E5=91=A8=E5=AF=85=E5=BC=A0?=
=?UTF-8?q?=E7=9A=93=29?= <64253517+floatingCatty@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:15:29 -0400
Subject: [PATCH 12/20] Add Chinese nqs-impurity research article with
reciprocal hreflang
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_pages/research-nqs-impurity-zh.md | 35 ++++++++++++++++++++++++++++++
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diff --git a/_pages/research-nqs-impurity-zh.md b/_pages/research-nqs-impurity-zh.md
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--- /dev/null
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+---
+layout: research
+permalink: /zh/research/nqs-impurity/
+title: "用神经网络求解量子嵌入里的杂质问题"
+title_en: "Neural quantum states for quantum embedding"
+description: "A research note on a neural-quantum-state impurity solver in ghost Gutzwiller quantum embedding and its sampling bottleneck."
+description_zh: "用神经量子态求解 ghost Gutzwiller 量子嵌入中的杂质模型，以及高精度采样带来的计算开销。"
+paper_year: 2026
+paper_url: "https://doi.org/10.1103/rkd8-q6yl"
+paper_title: "Neural-quantum-states impurity solver for quantum embedding problems"
+paper_doi: "10.1103/rkd8-q6yl"
+paper_authors:
+ - "Zhanghao Zhouyin"
+ - "Tsung-Han Lee"
+ - "Ao Chen"
+ - "Nicola Lanatà"
+ - "Hong Guo"
+research_locale: zh
+translation_url: "/research/nqs-impurity/"
+translation_of: "/research/nqs-impurity/"
+alternate_en_url: "/research/nqs-impurity/"
+alternate_zh_url: "/zh/research/nqs-impurity/"
+---
+
+
A band diagram is only part of what comes out of an electronic-structure calculation. The Hamiltonian matrix records orbital energies and couplings, the overlap matrix describes how the orbitals overlap, and the density matrix relates to where electrons are distributed. These are the quantities we often need for later property calculations.
From dbb4e41c2c5350aadca85448cc9f744420136b7f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zhanghao=20Zhouyin=20=28=E5=91=A8=E5=AF=85=E5=BC=A0?=
=?UTF-8?q?=E7=9A=93=29?= <64253517+floatingCatty@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:15:37 -0400
Subject: [PATCH 14/20] Add Chinese slem research article with reciprocal
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diff --git a/_pages/research-slem-zh.md b/_pages/research-slem-zh.md
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--- /dev/null
+++ b/_pages/research-slem-zh.md
@@ -0,0 +1,29 @@
+---
+layout: research
+permalink: /zh/research/slem/
+title: "SLEM：从原子结构预测量子算符"
+title_en: "SLEM: predicting quantum operators from atomic structures"
+description: "SLEM uses local, rotation-equivariant representations to predict quantum-operator matrices from atomic structures."
+description_zh: "SLEM 利用局域性和旋转等变性，从原子结构预测哈密顿量、重叠矩阵等量子算符。"
+paper_year: 2025
+paper_url: "https://arxiv.org/abs/2407.06053"
+research_locale: zh
+translation_url: "/research/slem/"
+translation_of: "/research/slem/"
+alternate_en_url: "/research/slem/"
+alternate_zh_url: "/zh/research/slem/"
+---
+
+
In electronic-structure calculations, I often have to work with very large matrices. Atomic orbitals mainly couple to nearby orbitals, so many matrix entries are zero. Storing and multiplying all those zeros would be wasteful. Sparse matrix formats avoid that, but distributing the calculation across many machines introduces other problems.
From 439a615e4ff5a631deaa7a0b93817d87aacf88ad Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Zhanghao=20Zhouyin=20=28=E5=91=A8=E5=AF=85=E5=BC=A0?=
=?UTF-8?q?=E7=9A=93=29?= <64253517+floatingCatty@users.noreply.github.com>
Date: Sun, 11 Oct 2026 20:15:45 -0400
Subject: [PATCH 16/20] Add Chinese vbcsr research article with reciprocal
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_pages/research-vbcsr-zh.md | 50 +++++++++++++++++++++++++++++++++++++
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diff --git a/_pages/research-vbcsr-zh.md b/_pages/research-vbcsr-zh.md
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--- /dev/null
+++ b/_pages/research-vbcsr-zh.md
@@ -0,0 +1,50 @@
+---
+layout: research
+permalink: /zh/research/vbcsr/
+title: "VBCSR：给大规模稀疏矩阵写的一套工具"
+title_en: "VBCSR: distributed sparse matrices for scientific computing"
+description: "VBCSR is a distributed variable-block sparse matrix library for electronic-structure and scientific computing workloads, with MPI and thresholded matrix products."
+description_zh: "VBCSR 是一套支持 MPI 的变块稀疏矩阵工具，面向电子结构计算和其他科学计算任务。"
+project_kind: "software"
+code_url: "https://github.com/DeepElectron/vbcsr"
+paper_url: "https://arxiv.org/abs/2609.04397"
+paper_year: 2026
+research_locale: zh
+translation_url: "/research/vbcsr/"
+translation_of: "/research/vbcsr/"
+alternate_en_url: "/research/vbcsr/"
+alternate_zh_url: "/zh/research/vbcsr/"
+---
+
+
A neural network can read only a material’s local structure and predict its quantum operators directly — reaching near first-principles accuracy while staying local enough to scale toward whole devices.
Learning local equivariant representations for quantum operators (SLEM)
Neural quantum states can act as the impurity solver inside quantum embedding, matching exact diagonalization on correlated models — where the real bottleneck is how precisely you sample observables, not training the network.
Neural-Quantum-States Impurity Solver for Quantum Embedding Problems
Trained only on first-principles eigenvalues, a deep-learning tight-binding model predicts the electronic structure of structures it has never seen — making million-atom, finite-temperature electronic simulations feasible.
Deep learning tight-binding approach for large-scale electronic simulations (DeePTB)
Making the non-equilibrium Green’s function method differentiable end to end turns a quantum transport simulator into something you can optimize through — providing gradients for sensitivity analysis and inverse design.
Automatic differentiable nonequilibrium Green's function formalism (AD-NEGF)
Infrastructure for representing and computing with very large sparse operators in electronic-structure and quantum-transport work — the quiet plumbing that lets bigger simulations run on smaller machines.