{"id":"c28ae8b2-402d-47df-8f6f-2ff9e96d6c50","arxiv_id":"2606.19798","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"MinSurf framework resolves atomic terminations and equilibrium morphologies for ten minerals by combining surface enumeration, DFT, ML potentials, and Wulff construction, achieving 0.0119 eV/Ų MAE and 1.14×10⁴ speedup over DFT.","lead":"MinSurf is a high-throughput framework that enumerates mineral surface terminations, labels them with DFT, trains machine-learning potentials, and uses Wulff construction to predict stable atomic-scale surfaces and crystal shapes. A smart generalist might read it to understand how faster computational tools can improve modeling of mineral interfaces used in carbon mineralization, energy storage, and catalysis.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"MinNEP generalization to unseen terminations remains the load-bearing assumption","rationale":"The reader correctly isolated the generalization step as the weakest link from the abstract. Full-text validation details would either close or confirm the gap; the proposed split test directly quantifies it.","tokens_in":1800,"tokens_out":302,"duration_ms":17048,"concrete_test":"Partition the 764 slabs by mineral; retrain MinNEP on nine minerals and evaluate MAE plus surface-energy rank correlation on the held-out mineral’s slabs. If test MAE > 0.025 eV/Ų or the top-three Wulff facets change for any mineral, the generalization assumption fails.","verdict_should_be":"CONDITIONAL","load_bearing_attack":"The headline performance numbers (MAE 0.0119 eV/Ų, hierarchy preservation, Wulff facets) rest on MinNEP being applied to the full enumerated surface space of the ten minerals. The training corpus is the 764 DFT slabs themselves; if a non-negligible fraction of the enumerated terminations lie outside the convex hull of the training configurations (different coordination, reconstruction, or stoichiometry), the model must extrapolate. The abstract supplies no explicit held-out termination or leave-one-mineral-out error, nor any uncertainty quantification on the predicted surface energies used for the morphology ranking. Without that, the claim that the DFT-derived hierarchy is faithfully reproduced by the surrogate cannot be taken as secured.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript presents the MinSurf framework, which enumerates atomic terminations for mineral surfaces, labels 764 slabs from ten minerals with DFT, trains the MinNEP machine-learning interatomic potential, and applies Wulff construction to predict stable surface energies, hierarchies, and equilibrium morphologies. It reports that MinNEP achieves a mean absolute error of 0.0119 eV/Ų relative to DFT, a 1.14×10^4 speedup, preserves the DFT-derived surface-energy ordering, reproduces dominant Wulff facets, and is consistent with X-ray diffraction for the α-quartz benchmark.","tokens_in":1944,"tokens_out":589,"duration_ms":35437,"significance":"If the reported performance and hierarchy preservation hold under proper validation, MinSurf would provide a scalable, reproducible route to mapping atomic-scale surface stability landscapes for minerals relevant to carbon mineralization, geo-energy, and catalysis. The integration of enumeration, DFT labeling, ML surrogate, and Wulff construction, together with the concrete acceleration factor, represents a practical advance for high-throughput interfacial modeling.","major_comments":[{"comment":"Abstract (MinNEP performance paragraph): The MAE of 0.0119 eV/Ų is stated without any description of the train/validation/test partitioning, held-out terminations, or leave-one-mineral-out protocol. Because the central claim is that MinNEP reproduces the DFT surface-energy hierarchy across the full enumerated space, the absence of explicit generalization metrics on unseen terminations is load-bearing.","section":"Abstract"},{"comment":"Abstract and results on morphology: The claim that MinNEP 'preserves the DFT-derived morphology-determining surface-energy hierarchy' and 'reproduces the dominant Wulff-exposed facets' is presented without uncertainty quantification or error bars on the predicted energies used for ranking. This leaves open whether small extrapolation errors could alter facet ordering.","section":"Abstract"},{"comment":"Methods (MinNEP training description): No details are supplied on how the 764 DFT slabs were split for training versus testing, nor on whether any enumerated terminations lie outside the training distribution (different coordination or stoichiometry). This directly affects the reliability of applying the surrogate to the complete surface space.","section":"Methods"}],"minor_comments":[{"comment":"The abstract mentions '90 corresponding oriented unit cells' but does not clarify how these bulk references were chosen or whether they introduce any systematic bias in surface-energy evaluation.","section":"Abstract"},{"comment":"Figure captions and text should explicitly state whether the reported MAE and hierarchy preservation are evaluated on training data only or on independent test configurations.","section":"Figures"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback, which highlights important aspects of validation and reporting needed to strengthen the manuscript. We address each major comment below and will incorporate the requested details in the revised version.","responses":[{"response":"We agree that the absence of explicit partitioning and generalization details in the abstract weakens the support for the central claim. In the revised manuscript we will expand both the abstract and the methods section to describe the train/validation/test split of the 764 DFT slabs, the use of held-out terminations, and any leave-one-mineral-out protocol employed to demonstrate generalization across minerals and terminations. Corresponding performance metrics on unseen data will be added.","revision_made":"yes","referee_comment":"[Abstract] Abstract (MinNEP performance paragraph): The MAE of 0.0119 eV/Ų is stated without any description of the train/validation/test partitioning, held-out terminations, or leave-one-mineral-out protocol. Because the central claim is that MinNEP reproduces the DFT surface-energy hierarchy across the full enumerated space, the absence of explicit generalization metrics on unseen terminations is load-bearing."},{"response":"We acknowledge that uncertainty quantification is necessary to evaluate the robustness of the reported hierarchy and facet ordering. The revised manuscript will include error estimates or confidence intervals on the MinNEP-predicted surface energies, together with a discussion of how these uncertainties affect the Wulff construction and the identification of dominant facets.","revision_made":"yes","referee_comment":"[Abstract] Abstract and results on morphology: The claim that MinNEP 'preserves the DFT-derived morphology-determining surface-energy hierarchy' and 'reproduces the dominant Wulff-exposed facets' is presented without uncertainty quantification or error bars on the predicted energies used for ranking. This leaves open whether small extrapolation errors could alter facet ordering."},{"response":"We agree that a complete description of the data partitioning and any extrapolation analysis is required. The revised methods section will specify the exact splitting procedure for the 764 slabs, report performance on held-out sets, and include an assessment of whether enumerated terminations with differing coordination or stoichiometry fall outside the training distribution.","revision_made":"yes","referee_comment":"[Methods] Methods (MinNEP training description): No details are supplied on how the 764 DFT slabs were split for training versus testing, nor on whether any enumerated terminations lie outside the training distribution (different coordination or stoichiometry). This directly affects the reliability of applying the surrogate to the complete surface space."}],"tokens_in":1534,"tokens_out":554,"duration_ms":12439,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core contribution is a pipeline that enumerates atomic terminations for mineral surfaces, labels them with DFT, trains a NEP-style potential, and feeds the results into Wulff constructions. Applied to ten minerals it produces 764 slabs and claims the surrogate reproduces the DFT surface-energy ordering while running 11400 times faster.\n\nThe work does a few things cleanly. It reports a concrete MAE of 0.0119 eV/Ų on the training data and shows that the resulting morphologies match the dominant facets seen in the DFT reference. The quartz X-ray diffraction check is an independent consistency test that adds some weight. For groups that need many representative mineral interfaces for interfacial simulations, having a reproducible way to generate the low-energy terminations is practical.\n\nThe soft spot is exactly where the stress-test note flags it. The abstract gives no held-out termination set, no leave-one-mineral-out numbers, and no uncertainty on the predicted energies used for the final ranking. If a non-trivial fraction of the enumerated slabs sit outside the training distribution, the hierarchy preservation claim is not yet secured. The choice of the 90 oriented unit cells is also left unexplained, which matters because surface energies are differences against those references.\n\nThis paper is aimed at computational materials scientists who run high-throughput interface work on minerals. It is coherent on its own terms and supplies enough quantitative results to justify sending it to referees, provided the authors can add explicit validation splits and error estimates in revision.","headline":"MinSurf scales termination enumeration across ten minerals with a trained potential that hits reported accuracy and speedup numbers, but the generalization to all enumerated slabs rests on an untested assumption.","tokens_in":2420,"tokens_out":379,"would_cite":false,"duration_ms":19719,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"MinSurf trains a neural potential on 764 DFT mineral slabs to predict surface energies at 0.0119 eV/Ų error and 11400x speedup.","keywords":["mineral surfaces","surface energy","machine learning potential","Wulff construction","high-throughput computation","density functional theory","crystal morphology","atomic termination"],"falsifier":"Experimental measurement of surface terminations or crystal facet distributions for one of the ten minerals that differs from the MinNEP-predicted Wulff shape.","tokens_in":2714,"feed_emoji":"⛰️","tokens_out":673,"duration_ms":24473,"temperature":0.7,"pith_summary":"The paper presents MinSurf as a workflow that enumerates atomic terminations of mineral surfaces, computes their energies with DFT on a training set of 764 slabs from ten minerals, fits a machine-learned potential called MinNEP, and applies Wulff construction to obtain equilibrium shapes. This is motivated by the fact that full DFT across all possible terminations is too costly, so most simulations pick facets without systematic comparison. A reader would care because surface terminations control reactivity in carbon mineralization, geo-energy, catalysis, and contaminant processes, and having representative low-energy models changes what simulations can be trusted. The work reports that MinNEP reproduces the DFT energy ordering and the dominant exposed facets while passing an X-ray diffraction check on alpha-quartz.","feed_headline":"Neural potential predicts mineral surface energies to 0.01 eV accuracy","feed_subtitle":"MinSurf evaluates 764 slabs from ten minerals at 11400x DFT speed while keeping the same stable facets","key_machinery":"MinNEP, a machine-learning interatomic potential trained on DFT surface energies of enumerated slabs from ten minerals, used to evaluate energies across many more terminations than direct DFT allows.","core_discovery":"MinSurf combines surface enumeration, DFT labeling of 764 slabs plus 90 bulk references, training of the MinNEP interatomic potential, and Wulff construction to resolve which atomic terminations are stable, to map surface-energy landscapes, and to predict equilibrium morphologies; the resulting potential matches DFT surface energies to a mean absolute error of 0.0119 eV per square angstrom, delivers a 1.14 times 10 to the 4 acceleration, and preserves the morphology-determining energy hierarchy.","pith_inferences":["The same enumeration-plus-potential approach could be applied to additional mineral families or to surfaces with defects if the potential remains accurate outside the training distribution.","Morphology predictions could be tested against crystal-growth experiments that control for temperature and supersaturation rather than only against room-temperature XRD.","The framework supplies a concrete route to compare many more candidate terminations than current DFT budgets allow, which may shift practice from facet-level to termination-level modeling in interface studies."],"forward_implications":["Surface models for simulations of carbon mineralization and heterogeneous catalysis can be chosen by predicted energy rather than convention.","The DFT-derived ordering of surface stabilities is preserved at the accelerated scale.","Dominant facets in equilibrium morphologies match those from direct DFT.","X-ray diffraction patterns provide an independent check that the chosen reference structures are crystallographically consistent.","Reproducible atomic-scale surface models become available for high-throughput studies of mineral interfaces."],"fun_headline_variants":["MinNEP matches DFT on 764 mineral surface slabs","MinSurf maps atomic terminations at 11400x DFT speed","Neural potential resolves mineral surface energy landscapes","MinNEP preserves DFT morphology hierarchy for ten minerals","764 slabs benchmarked with 0.0119 eV per Ų error"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The neural potential trained on the 764 labeled slabs will give accurate energies for atomic terminations it never saw during training.","fun_headline_variants_meta":{"raw":{"variants":["MinNEP matches DFT on 764 mineral surface slabs","MinSurf maps atomic terminations at 11400x DFT speed","Neural potential resolves mineral surface energy landscapes","MinNEP preserves DFT morphology hierarchy for ten minerals","764 slabs benchmarked with 0.0119 eV per Ų error"]},"model":"grok-4.3","cost_usd":0.004546,"raw_usage":{"total_tokens":2293,"prompt_tokens":734,"num_sources_used":0,"completion_tokens":81,"cost_in_usd_ticks":45462000,"prompt_tokens_details":{"text_tokens":734,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1478,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":734,"tokens_out":81,"duration_ms":13777,"temperature":1.0,"reasoning_tokens":1478,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T16:43:26.390361+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Experimental measurement of surface terminations or crystal facet distributions for one of the ten minerals that differs from the MinNEP-predicted Wulff shape.","supporting_citations":[],"review_version":1}