Author

Chen Wang

3 papers indexed here

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Open access Oct 2026

Architecture-agnostic knowledge distillation for machine-learning interatomic potentials.

Atomistic foundation models can achieve near-ab-initio accuracy and strong transferability across molecular and materials systems, but their large parameter counts lead to high inference latency and memory cost, limiting long-time-scale molecular dynamics and resource-constrained deployment. In many practical studies,...

Chen Wang, Si-Yu Hu, Guang-Ming Tan et al. · Neural Networks · 0 citations

Preprint Oct 2026

TrimSO(2): Fast and Memory-Efficient Training of Equivariant Interatomic Potentials

Equivariant interatomic potentials enable accurate and efficient simulations of molecules and materials. Among them, models based on SO(2) convolutions reduce the cost of tensor products and achieve state-of-the-art accuracy, but their training remains time-consuming and memory-intensive. Existing training implementati...

Chen Wang, Yuan-Chang Zhou, Guang-Ming Tan et al. · 0 citations

#machine learning Preprint Oct 2026

LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs

Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations. However, the high computational cost of their core Clebsch--Gordan tensor product (CGTP) convolutions severely limits model scalability and efficiency. In this pap...

Yuan-Chang Zhou, Ming-Zhen Li, Chen Wang et al. · 0 citations