Ben Anderson | Searcharxiv

arXiv2025

An Electronic Ising Machine

We develop a custom printed circuit board (PCB) for a low-power and high-speed accelerator of NP-Hard graph problems. The architecture implements an annealing-based computing paradigm using a network of nonlinear electronic oscillators whose phase dynamics converge to stable configurations that encode solutions. We review the theoretical framework, and present our circuit design, simulations, and experimental results. We further highlight some key future research directions for the emerging developing of computing architectures based on energy minimization.

cs.ET↗

arXiv2017

Robust FCC solute diffusion predictions from ab-initio machine learning methods

We evaluate the performance of four machine learning methods for modeling and predicting FCC solute diffusion barriers. More than 200 FCC solute diffusion barriers from previous density functional theory (DFT) calculations served as our dataset to train four machine learning methods: linear regression (LR), decision tree (DT), Gaussian kernel ridge regression (GKRR), and artificial neural network (ANN). We separately optimize key physical descriptors favored by each method to model diffusion barriers. We also assess the ability of each method to extrapolate when faced with new hosts with limited known data. GKRR and ANN were found to perform the best, showing 0.15 eV cross-validation errors and predicting impurity diffusion in new hosts to within 0.2 eV when given only 5 data points from the host. We demonstrate the success of a combined DFT + data mining approach towards solving materials science challenges and predict the diffusion barrier of all available impurities across all FCC hosts.

cond-mat.mtrl-sci↗