Selected work

Representative Papers

Autonomous battery research: Principles of heuristic operando experimentation

Dec 29, 2025

Traditional in situ battery characterization methods struggle to reliably capture stochastic, transient failure events such as dendrite initiation. This work proposes a heuristic in situ experimental framework that integrates physics-informed digital twins with AI agents to actively guide multimodal beamline instrumentation toward mechanistically critical precursors. Departing from conventional uncertainty-driven active learning, the approach innovatively employs entropy-based metrics to quantify scientific information gain, thereby enhancing experimental efficiency and data value while adhering to FAIR data principles. The method effectively mitigates beam-induced damage and data redundancy, successfully capturing transient precursor phenomena overlooked by conventional techniques, and establishes a new paradigm for building trustworthy autonomous battery laboratories.

1 citationsRead paper

Reservoir Computing with Heterogeneous Magnetic Metamaterials

Aug 09, 2026

This work proposes a reservoir computing architecture based on geometrically heterogeneous arrays of magnetic nanorings to harness the intrinsic nonlinearity and history-dependent dynamics of physical systems for efficient temporal computation with minimal training overhead. Input signals are encoded via a rotating magnetic field, and the responses of nanorings with varying widths are simultaneously read out through multichannel planar Hall effect measurements. Geometric heterogeneity is introduced for the first time as a new degree of freedom to tailor reservoir dynamics, and combined with principal component analysis for noise suppression, substantially enhancing representational capacity. In Mackey-Glass time-series prediction tasks, the multichannel cooperative output significantly reduces the normalized root mean square error, demonstrating improved computational performance and paving the way toward scalable magnetic metamaterial-based computing systems.

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Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials

Jun 14, 2026

This work addresses the limited accuracy of short-range equivariant interatomic potential models in representing energy landscapes, which stems from insufficient aggregation capacity and spectral resolution in scalar (ℓ=0) channels. While preserving the equivariant tensor backbone, the authors introduce two lightweight, symmetry-preserving modules—Physics-Aware Neighborhood (PAN) pooling and Physics-Guided Spectral (PGS) mixer—that operate exclusively on scalar channels. These modules incorporate coordinate-sensitive modulation and enhanced radial spectral bases, establishing scalar-path fidelity as a critical design dimension for the first time. Integrated into architectures such as MACE, Allegro, and NequIP, the approach reduces prediction errors for forces, energies, and stresses by 22–27%, 19–22%, and 27–28%, respectively, across Ag, Si, LiF, and MD17/rMD17 datasets, with only ~5% additional inference overhead.

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Recent publications

Latest Papers

Reservoir Computing with Heterogeneous Magnetic Metamaterials

Aug 09, 2026

This work proposes a reservoir computing architecture based on geometrically heterogeneous arrays of magnetic nanorings to harness the intrinsic nonlinearity and history-dependent dynamics of physical systems for efficient temporal computation with minimal training overhead. Input signals are encoded via a rotating magnetic field, and the responses of nanorings with varying widths are simultaneously read out through multichannel planar Hall effect measurements. Geometric heterogeneity is introduced for the first time as a new degree of freedom to tailor reservoir dynamics, and combined with principal component analysis for noise suppression, substantially enhancing representational capacity. In Mackey-Glass time-series prediction tasks, the multichannel cooperative output significantly reduces the normalized root mean square error, demonstrating improved computational performance and paving the way toward scalable magnetic metamaterial-based computing systems.

0 citationsRead paper

Scalar-pathway fidelity improves physical accuracy in short-range equivariant interatomic potentials

Jun 14, 2026

This work addresses the limited accuracy of short-range equivariant interatomic potential models in representing energy landscapes, which stems from insufficient aggregation capacity and spectral resolution in scalar (ℓ=0) channels. While preserving the equivariant tensor backbone, the authors introduce two lightweight, symmetry-preserving modules—Physics-Aware Neighborhood (PAN) pooling and Physics-Guided Spectral (PGS) mixer—that operate exclusively on scalar channels. These modules incorporate coordinate-sensitive modulation and enhanced radial spectral bases, establishing scalar-path fidelity as a critical design dimension for the first time. Integrated into architectures such as MACE, Allegro, and NequIP, the approach reduces prediction errors for forces, energies, and stresses by 22–27%, 19–22%, and 27–28%, respectively, across Ag, Si, LiF, and MD17/rMD17 datasets, with only ~5% additional inference overhead.

0 citationsRead paper

Autonomous battery research: Principles of heuristic operando experimentation

Dec 29, 2025

Traditional in situ battery characterization methods struggle to reliably capture stochastic, transient failure events such as dendrite initiation. This work proposes a heuristic in situ experimental framework that integrates physics-informed digital twins with AI agents to actively guide multimodal beamline instrumentation toward mechanistically critical precursors. Departing from conventional uncertainty-driven active learning, the approach innovatively employs entropy-based metrics to quantify scientific information gain, thereby enhancing experimental efficiency and data value while adhering to FAIR data principles. The method effectively mitigates beam-induced damage and data redundancy, successfully capturing transient precursor phenomena overlooked by conventional techniques, and establishes a new paradigm for building trustworthy autonomous battery laboratories.

1 citationsRead paper