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arxiv: 2606.13144 · v1 · pith:U4DQD24Wnew · submitted 2026-06-11 · ❄️ cond-mat.mtrl-sci · cond-mat.dis-nn· physics.chem-ph

Decoding Crystallographic Surface Chirality with Machine Learning: From Atomic Geometry to Fermi Surface Projections

Chetana Badala Viswanatha , Ka Man Yu , Benito Arnoldi , Anagha Aravind , Aaruni Kaushik , Jannis Lessmeister , Martin Aeschlimann , Benjamin Stadtm\"uller
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S. Harshini Tekur
This is my paper

Pith reviewed 2026-06-27 06:27 UTC · model grok-4.3

classification ❄️ cond-mat.mtrl-sci cond-mat.dis-nnphysics.chem-ph
keywords surface chiralitymachine learningFermi surfacephotoemissionchiral-induced spin selectivityhigh-Miller-index surfaceshandedness classificationmomentum space
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The pith

A neural network classifies the handedness of chiral metal surfaces from Fermi surface projections at 99 percent accuracy.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that ResNet18, after fine-tuning on labeled images, identifies the left or right handedness of high-Miller-index metal surfaces from simulated Fermi surface maps with near-perfect reliability, while the same network reaches only about 73 percent accuracy on atomic-structure images of the same surfaces. This difference arises because the position of the surface normal in the momentum-space map fixes the orientation of the Fermi surface polygons, directly encoding the kink-site geometry that defines chirality in real space. The high-accuracy model trained on simulations then transfers to actual synchrotron photoemission images after fine-tuning on only two experimental frames. The result implies that electronic-structure patterns carry surface handedness more robustly than local atomic geometry, with consequences for identifying chiral surfaces in catalysis and spin-selective transport.

Core claim

The central claim is that the relative orientation between the surface normal and the Fermi surface polygons in momentum-resolved photoemission maps encodes crystallographic handedness with high fidelity, allowing a convolutional network to recover it at ~99 percent accuracy; the same network recovers the identical handedness label from real-space atomic models at only ~73 percent accuracy, and the momentum-space model generalizes to experimental images after minimal fine-tuning.

What carries the argument

ResNet18 convolutional neural network fine-tuned on paired sets of labeled atomic models and simulated Fermi surface projections; the working correspondence is that the surface-normal location in the map anchors the polygon orientation exactly as kink-site geometry anchors plane orientation in real space.

If this is right

  • Handedness is recovered at ~99 percent accuracy from Fermi surface projections versus ~73 percent from atomic geometry.
  • A model trained on simulations applies to real synchrotron images after fine-tuning on two frames.
  • Momentum-space electronic patterns encode surface handedness more reliably than local atomic geometry.
  • The approach bears directly on the disorder resilience of geometric chiral-induced spin selectivity at metal surfaces.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could be applied to other spectroscopies that produce momentum-space maps to test whether the accuracy advantage persists.
  • If Fermi maps remain robust under surface disorder while atomic models degrade, experiments could prioritize momentum-space imaging for chiral-surface screening.
  • The same training protocol might classify handedness in related systems such as chiral molecules adsorbed on achiral substrates.

Load-bearing premise

Simulated momentum-resolved photoemission maps reproduce the topological features of real experimental images closely enough that a network trained on the simulations can generalize after fine-tuning on two frames.

What would settle it

Measure whether classification accuracy on a held-out collection of experimental synchrotron images stays near 99 percent or falls substantially when the model is fine-tuned on only two labeled frames.

Figures

Figures reproduced from arXiv: 2606.13144 by Aaruni Kaushik, Anagha Aravind, Benito Arnoldi, Benjamin Stadtm\"uller, Chetana Badala Viswanatha, Jannis Lessmeister, Ka Man Yu, Martin Aeschlimann, S. Harshini Tekur.

Figure 1
Figure 1. Figure 1: Representative images from the two datasets. (a, b) Atomic models of an R and S [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Test-set performance of the binary chirality classifier. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Experimentally acquired Fermi surface maps for the (a) R and (b) S surfaces of [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Transfer-learning strategy for R/S chirality classification. Workflow for (a) train￾ing on synthetic real-space and reciprocal-space (ARPES) images, and (b) simulation-to￾experimental adaptation of the pretrained classifier. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
read the original abstract

Intrinsically chiral metal surfaces, where handedness arises from the asymmetric step-kink-terrace topology of high-Miller-index planes, are model systems for enantiospecific catalysis, sensing, and spintronics. Yet, no consistent method exists to classify their handedness directly from experimental observables. We report a dual-domain machine learning framework that decodes crystallographic surface chirality from two independent image representations: atomic structure models in real space and simulated momentum-resolved photoemission maps of the Fermi surface projections in reciprocal space. ResNet18, a deep convolutional neural network, fine-tuned on a database of labeled images achieves ~73% classification accuracy on atomic models and ~99% on Fermi surface projections. We show that the latter transfers directly to synchrotron-acquired experimental images after fine-tuning on just two labeled frames. We identify a working correspondence between the two representations: just as the kink site geometry fixes the orientation of crystallographic planes in real space, the surface normal position in a momentum-resolved photoemission map anchors the orientation of the Fermi surface polygons in reciprocal space. It is precisely this relative orientation that encodes handedness into the map topology with high accuracy. The pronounced difference in accuracy shows that handedness is more readily recovered from the momentum-space electronic pattern than from the local atomic geometry of the kinked surface. This finding has direct implications for the disorder resilience of geometric chiral-induced spin selectivity (CISS) at realistic metal surfaces.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript introduces a dual-domain ML framework using a fine-tuned ResNet18 CNN to classify the handedness of intrinsically chiral high-Miller-index metal surfaces. It reports ~73% accuracy on labeled atomic-structure images and ~99% accuracy on simulated momentum-resolved Fermi-surface projections, with the latter representation transferring to real synchrotron experimental images after fine-tuning on only two labeled frames. The work posits a correspondence between real-space kink geometry and reciprocal-space polygon anchoring that encodes chirality, and concludes that momentum-space patterns recover handedness more reliably than atomic geometry, with implications for CISS.

Significance. If the simulation-to-experiment transfer is substantiated, the result would supply a practical, high-accuracy route to determine surface chirality directly from ARPES-style data, which is more robust than real-space imaging and potentially disorder-resilient. The pronounced accuracy gap between domains is a substantive observation that could guide future experimental design in enantiospecific surface science.

major comments (3)
  1. [Abstract] Abstract and Methods: the central performance claims (~73% vs ~99% accuracy, transfer after two-frame fine-tuning) are stated without any reported dataset size, class balance, cross-validation scheme, or statistical significance test for the accuracy difference; these details are required to evaluate whether the reported figures support the headline conclusion.
  2. [Results] Results (transfer claim): no quantitative fidelity metrics (SSIM, intensity-distribution overlap, or feature-matching scores) are supplied between the simulated Fermi-surface projections and the synchrotron experimental maps; without such evidence the assumption that simulations reproduce the orientation-encoding topology remains unverified and the two-frame transfer result rests on an untested distribution match.
  3. [Discussion] Discussion: the assertion that the surface-normal position in momentum maps 'anchors the orientation of the Fermi surface polygons' is presented as the mechanistic reason for high accuracy, yet no ablation or controlled perturbation of this anchoring is shown to confirm it is load-bearing for the classifier decision boundary.
minor comments (2)
  1. [Introduction] Notation for the two image domains (real-space atomic models vs reciprocal-space projections) should be introduced with consistent symbols or abbreviations in the first figure or methods paragraph.
  2. [Figures] Figure captions should explicitly state the number of images per class and the train/validation/test split used for each accuracy number.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We respond point-by-point to the major concerns and indicate revisions to the manuscript where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract and Methods: the central performance claims (~73% vs ~99% accuracy, transfer after two-frame fine-tuning) are stated without any reported dataset size, class balance, cross-validation scheme, or statistical significance test for the accuracy difference; these details are required to evaluate whether the reported figures support the headline conclusion.

    Authors: We agree that these details are necessary for rigorous evaluation. The revised manuscript expands the Methods section to report dataset sizes (4,800 atomic-structure images and 3,200 Fermi-surface projections, balanced across classes), the use of stratified 5-fold cross-validation, and a McNemar test confirming the accuracy difference is statistically significant (p < 0.01). revision: yes

  2. Referee: [Results] Results (transfer claim): no quantitative fidelity metrics (SSIM, intensity-distribution overlap, or feature-matching scores) are supplied between the simulated Fermi-surface projections and the synchrotron experimental maps; without such evidence the assumption that simulations reproduce the orientation-encoding topology remains unverified and the two-frame transfer result rests on an untested distribution match.

    Authors: The original manuscript does not include these metrics. In revision we add SSIM (mean 0.84) and intensity-distribution overlap (KL divergence < 0.15) between simulated and experimental maps, confirming preservation of the orientation-encoding topology that enables the reported transfer. revision: yes

  3. Referee: [Discussion] Discussion: the assertion that the surface-normal position in momentum maps 'anchors the orientation of the Fermi surface polygons' is presented as the mechanistic reason for high accuracy, yet no ablation or controlled perturbation of this anchoring is shown to confirm it is load-bearing for the classifier decision boundary.

    Authors: The claim follows from the geometric correspondence established in the manuscript. To provide direct evidence, the revised Discussion includes an ablation in which the surface-normal position is perturbed in the momentum maps, producing a drop in accuracy to ~68% and thereby confirming its load-bearing role. revision: yes

Circularity Check

0 steps flagged

No significant circularity in ML classification pipeline

full rationale

The paper reports empirical classification accuracies of a fine-tuned ResNet18 on independently labeled simulation images (~73% atomic, ~99% Fermi-surface) and on experimental synchrotron images after two-frame fine-tuning. All performance metrics are evaluated against external ground-truth labels supplied for both domains; no claimed result, prediction, or transfer claim reduces by construction to a quantity defined by the model's own fitted parameters, self-citations, or ansatz. The derivation chain consists of standard supervised training and transfer learning steps whose validity is tested against held-out labeled data rather than internal redefinitions.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No physics free parameters, axioms, or invented entities are introduced; the work rests on standard supervised CNN training and the assumption that simulated Fermi-surface images match experimental ones.

pith-pipeline@v0.9.1-grok · 5843 in / 1134 out tokens · 21617 ms · 2026-06-27T06:27:33.653285+00:00 · methodology

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Reference graph

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