Selected work

Representative Papers

Algorithms for Sampling Self-Orthogonal and Totally Self-Orthogonal Codes in Odd Characteristic

Oct 06, 2026

This study addresses the lack of efficient uniform sampling methods for self-orthogonal and totally self-orthogonal linear codes of arbitrary dimension over finite fields by reconstructing sampling algorithms based on mass formulas. It achieves, for the first time, the uniform generation of self-dual codes at arbitrary rates. The concept of "total orthogonality" is introduced to handle equivalence under Galois automorphisms, alongside a proposed sampling scheme for linear codes with prescribed total hull dimension. Furthermore, it is proven that all linear codes possess a constant Hermitian type. This work successfully instantiates code-based cryptographic schemes whose security relies on random self-dual codes, and elucidates the trade-off between extension degree and rate reduction, as well as its necessity in the permuted code equivalence (PCE) problem.

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AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis

Oct 26, 2025

Existing machine-learned interatomic potentials (MLIPs) for heterogeneous catalysis modeling are constrained by limited training data and struggle to simultaneously satisfy spin-polarization awareness and high-fidelity accuracy requirements. To address this, we introduce AQCat25—a large-scale dataset comprising 13.5 million DFT calculations—and propose a meta-data conditional modulation mechanism based on Feature-wise Linear Modulation (FiLM), which explicitly encodes system-specific attributes such as spin state and exchange-correlation functional type. This design mitigates knowledge conflicts and catastrophic forgetting inherent in multi-fidelity and multi-physics modeling. By jointly training with the OC20 dataset, our model preserves strong generalization across diverse catalytic systems while significantly improving performance on spin-sensitive and high-accuracy tasks—particularly transition-state energy barrier prediction. To our knowledge, this work presents the first general-purpose catalytic potential that concurrently achieves spin-awareness, high predictive fidelity, and robust cross-dataset transferability.

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Recent publications

Latest Papers

Algorithms for Sampling Self-Orthogonal and Totally Self-Orthogonal Codes in Odd Characteristic

Oct 06, 2026

This study addresses the lack of efficient uniform sampling methods for self-orthogonal and totally self-orthogonal linear codes of arbitrary dimension over finite fields by reconstructing sampling algorithms based on mass formulas. It achieves, for the first time, the uniform generation of self-dual codes at arbitrary rates. The concept of "total orthogonality" is introduced to handle equivalence under Galois automorphisms, alongside a proposed sampling scheme for linear codes with prescribed total hull dimension. Furthermore, it is proven that all linear codes possess a constant Hermitian type. This work successfully instantiates code-based cryptographic schemes whose security relies on random self-dual codes, and elucidates the trade-off between extension degree and rate reduction, as well as its necessity in the permuted code equivalence (PCE) problem.

0 citationsRead paper

AQCat25: Unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis

Oct 26, 2025

Existing machine-learned interatomic potentials (MLIPs) for heterogeneous catalysis modeling are constrained by limited training data and struggle to simultaneously satisfy spin-polarization awareness and high-fidelity accuracy requirements. To address this, we introduce AQCat25—a large-scale dataset comprising 13.5 million DFT calculations—and propose a meta-data conditional modulation mechanism based on Feature-wise Linear Modulation (FiLM), which explicitly encodes system-specific attributes such as spin state and exchange-correlation functional type. This design mitigates knowledge conflicts and catastrophic forgetting inherent in multi-fidelity and multi-physics modeling. By jointly training with the OC20 dataset, our model preserves strong generalization across diverse catalytic systems while significantly improving performance on spin-sensitive and high-accuracy tasks—particularly transition-state energy barrier prediction. To our knowledge, this work presents the first general-purpose catalytic potential that concurrently achieves spin-awareness, high predictive fidelity, and robust cross-dataset transferability.

0 citationsRead paper