Decoding the Micromagnetic Hamiltonian from Magnetic Fingerprints
arXiv:2607.27430
The paper presents deep convolutional neural networks that infer the full micromagnetic Hamiltonian of a material directly from magnetic fingerprint data obtained via First‑Order Reversal Curves, and includes an uncertainty‑quantifying parallel network.
Abstract
Extracting intrinsic magnetic Hamiltonians directly from magnetometry is challenging due to the high dimensionality of the parameter space and the degeneracy induced by ensemble averaging. Here, we introduce a collection of deep convolutional neural networks (CNNs) to extract the full phenomenological micromagnetic Hamiltonian directly from the magnetic fingerprints encoded within First-Order Reversal Curves (FORCs). We validate this approach via closed-loop verification, re-creating the input magnetometry for both simulated and experimental FORCs. To mitigate false positives, we deploy an `Alice--Bob' parallel network that quantifies prediction uncertainty based on solely the information in FORCs without any additional ground-truth knowledge. This framework provides a robust, machine-learning-assisted approach to unravel the underlying spin behaviors in complex magnetic systems