Computing at the Edge Enabled by Indoor Photovoltaics and Ferroelectrics

Robert L. Z. Hoye; Markus Hellenbrand; Nuno Estrócio; Austin M. Kay; Alice Scardina; Alessandro Mezzetti; Ignasi Fina; Jorge Íñiguez-González; Luís S. Marques; Francesco Matteucci; Quanxi Jia; Judith L. MacManus-Driscoll; Bert Offrein; Florencio Sánchez; Beatriz Noheda; George Koutsourakis; Marina Freitag; Gregory Burwell; Giulia Grancini; Jose P. B. Silva

arXiv · 2610.05133

Computing at the Edge Enabled by Indoor Photovoltaics and Ferroelectrics

View original source ↗Read source document ↗Export metadata ↓

Abstract

The exponential, pervasive rise of artificial intelligence (AI), hosted in large data centres, will test the limits of the underpinning infrastructure, including electricity and water supplies, which are becoming increasingly precious resources. Rather than performing all AI computations in centralized servers, for many functionalities, these can be performed locally across a network of billions of small, autonomous nodes, saving substantial energy costs associated with communication. This alternative paradigm is known as edge computing, or distributed intelligence (DI), but has been held back by the lack of availability of reliable local energy supplies matching the energy requirements of the computational infrastructure. Opportunities to address this challenge are emerging with rapid advances in high-performance indoor photovoltaics (IPVs) for local energy harvesting, as well as reductions in computational cost through neuromorphic or in-memory computing devices. In this perspective, we discuss the requirements of IPVs for DI, the extent to which emerging materials fulfil these requirements, and how current gaps could be addressed. We make the case that (anti)ferroelectric materials can surpass bottlenecks for both electrostatic energy storage and low-power in-memory neuromorphic computing. The core theme of this perspective is that co-creation between energy harvesting, storage and computing is essential for making DI a reliable and more sustainable alternative to centralized AI.

Explore related subjects

cond-mat.mtrl-sci

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Robert L. Z. Hoye, Markus Hellenbrand, Nuno Estrócio, Austin M. Kay, Alice Scardina, Alessandro Mezzetti, Ignasi Fina, Jorge Íñiguez-González, Luís S. Marques, Francesco Matteucci, Quanxi Jia, Judith L. MacManus-Driscoll, Bert Offrein, Florencio Sánchez, Beatriz Noheda, George Koutsourakis, Marina Freitag, Gregory Burwell, Giulia Grancini, Jose P. B. Silva. 2026-10-04. Computing at the Edge Enabled by Indoor Photovoltaics and Ferroelectrics. https://arxiv.org/abs/2610.05133

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Probabilistic calibration of crystal plasticity material models with synthetic global and local data

Calibrating full-field crystal plasticity (CP) models using global stress-strain curves alone often produces non-unique parametrizations: multiple parameter sets can predict the same global behavior but different local, grain-scale behavior. Incorporating local calibration data can mitigate uniqueness issues, but this typically requires specialized experiments like high-energy X-ray diffraction microscopy. The computational expense of full-field simulations also prevents uncertainty quantification with sampling-based probabilistic calibration algorithms like Markov chain Monte Carlo. In this study, a two-stage probabilistic calibration procedure is developed that uses both global and local data and balances the efficiency of a surrogate model with the accuracy of a full-field CP model. To enable tractable Bayesian inference via sampling from the full-field model, an efficient, parallelized sequential Monte Carlo (SMC) algorithm is used to perform the calibration, which involves more than 100,000 CP simulations. Synthetic measurement data is used in the calibration to assess uncertainty and accuracy of the parameter posterior distributions relative to a "ground-truth" simulation with a microstructure representative of Inconel 718. The two-stage calibration is shown to be robust to limited and noisy local data. Posterior distributions 'and computational costs are compared between the two-stage calibration and a one-stage calibration using only the full-field model, which is also enabled by SMC, as well as an efficient but biased one-stage calibration using a surrogate model. Overall, including local measurement data is shown to reduce parameter uncertainty, while joint distributions of the calibrated parameters highlight important considerations in choosing constitutive models and calibration data, including challenges resulting from correlated parameters.

cond-mat.mtrl-sci↗

arXiv2026

Altermagnetism in MnF$_2$: Band Splitting and Its Physical Consequences

MnF$_2$ is widely regarded as a candidate altermagnet, but the magnitude and implications of its altermagnetic band splitting remain debated. Using electronic-structure calculations, we construct minimal models that capture the magnetic and electronic properties of MnF$_2$. These models show that the parameters governing the chiral magnon splitting and the spin splitting of the electronic bands are relatively small. Moreover, the electronic system lies in the strong-coupling regime, where most magnetic properties are controlled by the ratio $t/U$ between the characteristic hopping amplitude $t$ and the large on-site Coulomb repulsion $U$. Consequently, all exchange interactions scale as $1/U$, so a small altermagnetic hopping $δt$ produces only a proportionally small exchange term. Upon doping, the altermagnetic contribution to the anomalous Hall effect is likewise suppressed, being smaller than the conventional (non-altermagnetic) contribution by a factor of order $δt/U$. In contrast, the behavior of the conductivity tensor $\hatσ(ω)$ at $\hbar ω\sim U$ differs qualitatively, because $δt$ enters the energies of interband optical transitions \emph{directly} rather than through the reduced ratio $δt/U$. This contribution strongly reshapes $\hatσ(ω)$, leading to a dramatic enhancement of the magneto-optical response.

cond-mat.mtrl-sci↗

arXiv2026

An Extensible Agentic Framework for Expert-Level Automation of Atomistic Simulations

Traditionally, atomistic simulation has been constrained by the computational scaling limits of ab initio methods and the parameterization overhead of empirical force fields. The recent emergence of universal machine learning interatomic potentials has significantly mitigated these bottlenecks, offering near-quantum accuracy and generalizability across diverse chemical spaces at a fraction of the computational cost. However, this shift has relocated the bottleneck to the human dimension: time-consuming mechanical processes, such as input preparation and data analysis, now dominate the research lifecycle. We introduce Paimon, a Platform for Agentic Integration in Materials Optimization and Nanoscale-simulations. Through hundreds of trials on an expert-level liquid electrolyte simulation, we show that Paimon substantially improves the reliability of agentic workflows by suppressing silent errors: plausible yet physically incorrect results. Paimon accommodates a new capability without modifying the agents already present, which we demonstrate by integrating an externally developed scientific agent. As an agent harness for atomistic simulations, Paimon affords researchers a continuous, science-centric workflow throughout the entire simulation lifecycle.

cond-mat.mtrl-sci↗