{"id":"c18f35fc-da93-4da3-83e4-d6a399e400e5","arxiv_id":"2605.30012","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":2,"one_line_summary":"A two-level ML approach using graph neural network potentials and direct energy predictors maps thermodynamic stability across (Cs/FA)Pb(Br/I)3 and (Cs/FA)Sn(Br/I)3 compositions, finding narrower stable regions for Sn-based systems with peak stability at high iodine content.","lead":"The paper applies a two-level machine learning strategy to compute free energy landscapes for complex hybrid perovskite alloys. This computational shortcut could help identify stable compositions for more durable solar cell materials.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Two-level ML strategy's ability to capture free-energy contributions from alloy disorder and FA orientations across full composition space is the load-bearing assumption.","rationale":"The reader's weakest_assumption directly identifies the same point. Because the supplied abstract contains no numerical validation of the ML components on the relevant disordered, orientationally complex cells, the concern remains load-bearing and the low-confidence UNVERDICTED verdict is appropriate; no stronger internal inconsistency is visible from the given material.","tokens_in":1691,"tokens_out":409,"duration_ms":13276,"concrete_test":"Extract the reported test-set MAE and parity plots for the secondary energy models on quaternary compositions (if present in §Methods or SI); recompute a 2×2×2 supercell formation energy for a representative 50:50 Cs/FA, 50:50 Br/I, 50:50 Pb/Sn configuration using both the published ML pipeline and a fresh DFT relaxation; if the ML-DFT discrepancy exceeds 5 meV/atom or changes the sign of the mixing energy, the stability boundaries are unreliable.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (narrower Sn stability windows, max stability at high I, no central stabilization) rests on computed free-energy landscapes. These landscapes are obtained via GNN interatomic potentials for relaxation plus secondary models that predict energies directly from unrelaxed structures. For the claim to hold, both levels must reproduce DFT-quality energies while correctly sampling configurational entropy from random alloy occupations and FA molecular orientations over the entire (Cs/FA)(Pb/Sn)(Br/I)3 space. The abstract provides no quantitative validation (e.g., MAE on held-out mixed compositions, convergence of free-energy differences with supercell size, or direct comparison of ML vs. DFT formation energies for quaternary cells), leaving open the possibility that systematic errors in the secondary models or incomplete sampling of orientational degrees of freedom shift the location or width of the reported stability regions.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript presents a two-level machine-learning strategy to compute thermodynamic stability of quaternary hybrid perovskites (Cs/FA)Pb(Br/I)3 and (Cs/FA)Sn(Br/I)3. Graph neural network interatomic potentials trained on DFT data enable efficient relaxations, while secondary ML models predict energies directly from unrelaxed structures; together these allow sampling of free-energy landscapes that incorporate alloy disorder and FA molecular orientations. The central results are that Sn-based alloys exhibit narrower stable composition windows than Pb-based counterparts, maximum stability occurs at high iodine content, and no stabilization is found near the center of the composition space.","tokens_in":1896,"tokens_out":557,"duration_ms":21751,"significance":"If the ML models reproduce DFT-quality energies and configurational sampling across the full composition space, the work supplies concrete guidance for compositional engineering of stable perovskite alloys and demonstrates a scalable route to mapping phase stability in complex multicomponent systems. The two-level approach is a clear methodological strength for rendering quaternary alloys computationally tractable.","major_comments":[{"comment":"The load-bearing assumption is that the secondary ML models, when combined with the GNN potentials, accurately reproduce free-energy differences arising from random alloy occupations and FA orientations over the entire (Cs/FA)(Pb/Sn)(Br/I)3 space. No quantitative validation (MAE on held-out mixed-composition supercells, convergence of free-energy differences with supercell size, or direct ML-vs-DFT formation-energy comparisons for representative quaternary cells) is reported in the results or methods sections; without these metrics the reported narrowing of Sn stability windows and the location of maximum stability remain vulnerable to systematic bias.","section":"Results and Methods"},{"comment":"The claim that 'no stabilization is observed near the center of the composition space' is presented as a key finding, yet the manuscript does not show how the configurational entropy term is computed or converged when both A-site (Cs/FA) and X-site (Br/I) disorder plus FA orientations are sampled simultaneously; an explicit test of whether the secondary model preserves the correct entropy scaling with composition is required to support this conclusion.","section":"Free-energy landscapes"}],"minor_comments":[{"comment":"Notation for the two ML levels is introduced without a clear diagram or table summarizing training data sizes, hyperparameters, and validation splits for each level.","section":"Methods"},{"comment":"Figure captions should explicitly state the supercell size and number of sampled configurations used to generate each free-energy surface.","section":"Figures"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comments, which highlight important aspects of validation and entropy treatment. We address each major comment below and will incorporate additional quantitative checks and methodological details in the revised manuscript.","responses":[{"response":"We agree that explicit quantitative validation metrics for the secondary models on mixed-composition cells would strengthen the claims. In the revised manuscript we will add: (i) MAE values for the secondary ML models evaluated on held-out supercells spanning the full quaternary composition space, (ii) direct ML-versus-DFT formation-energy comparisons for representative quaternary cells, and (iii) a brief convergence test of free-energy differences with supercell size. These will be placed in a new subsection of the Methods and referenced in the Results to demonstrate that systematic bias does not affect the reported stability trends.","revision_made":"yes","referee_comment":"[Results and Methods] The load-bearing assumption is that the secondary ML models, when combined with the GNN potentials, accurately reproduce free-energy differences arising from random alloy occupations and FA orientations over the entire (Cs/FA)(Pb/Sn)(Br/I)3 space. No quantitative validation (MAE on held-out mixed-composition supercells, convergence of free-energy differences with supercell size, or direct ML-vs-DFT formation-energy comparisons for representative quaternary cells) is reported in the results or methods sections; without these metrics the reported narrowing of Sn stability windows and the location of maximum stability remain vulnerable to systematic bias."},{"response":"We acknowledge the need for explicit documentation of the entropy calculation. The configurational entropy is obtained from the standard ideal-mixing expression applied to the sampled A-site and X-site occupations, with FA orientations included via the secondary model. In the revision we will add a dedicated paragraph in the Methods section that (a) details the sampling procedure for simultaneous A/X disorder and orientations, (b) shows how the entropy term is evaluated, and (c) provides a direct test comparing entropy scaling from the secondary model against DFT on smaller cells. This will confirm that the model preserves the expected composition dependence and thereby supports the observation of no central stabilization.","revision_made":"yes","referee_comment":"[Free-energy landscapes] The claim that 'no stabilization is observed near the center of the composition space' is presented as a key finding, yet the manuscript does not show how the configurational entropy term is computed or converged when both A-site (Cs/FA) and X-site (Br/I) disorder plus FA orientations are sampled simultaneously; an explicit test of whether the secondary model preserves the correct entropy scaling with composition is required to support this conclusion."}],"tokens_in":1454,"tokens_out":563,"duration_ms":20869,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper's core result is a computational scan showing that Sn-based (Cs/FA)Sn(Br/I)3 alloys have tighter stable composition ranges than the Pb analogs, with best stability at high iodine and no extra stabilization in the middle of the space.\n\nWhat is new is the specific two-level setup: graph neural network potentials for fast relaxations combined with secondary models that predict energies straight from unrelaxed cells. This lets them include both random alloy occupations and FA molecular orientations over the full quaternary space without running DFT on every configuration.\n\nThe approach is a practical step for handling the combinatorial explosion in these materials. It builds on existing GNN potential work and applies it to a relevant photovoltaic alloy problem.\n\nThe main soft spot is validation. The stability conclusions rest on the models reproducing DFT-quality free energies for disordered, mixed-cation, mixed-anion cells with varying molecular orientations. The abstract supplies no MAE values on held-out mixed compositions, no supercell convergence checks, and no direct ML-versus-DFT comparisons for quaternary cells. Without those numbers the reported narrower Sn windows could shift if the secondary models have systematic bias on unrelaxed structures or if orientational sampling is incomplete.\n\nThis work is for computational materials groups focused on perovskite alloys and ML-accelerated thermodynamics. Readers already using similar potentials will see a concrete application and may pick up workflow ideas. It is coherent enough on its own terms to merit peer review so referees can examine the training data, test metrics, and sampling details.","headline":"The two-level ML workflow maps free-energy landscapes for these quaternary perovskites and reports narrower Sn stability windows with a high-I peak, but the claims depend on unvalidated model accuracy across mixed compositions.","tokens_in":2364,"tokens_out":390,"would_cite":false,"duration_ms":16839,"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":"Machine learning shows tin-based hybrid perovskites have narrower stable composition regions than lead-based ones.","keywords":["hybrid perovskites","thermodynamic stability","machine learning","alloy compositions","tin perovskites","lead perovskites","phase stability","free energy landscapes"],"falsifier":"An experimental phase diagram that finds stable Sn-based compositions near the center of the (Cs/FA)Sn(Br/I)3 space or broader stable windows than the lead analog would contradict the reported stability maps.","tokens_in":2602,"feed_emoji":"⚗️","tokens_out":645,"duration_ms":19104,"temperature":0.7,"pith_summary":"The paper introduces a two-level machine learning strategy to compute free energy landscapes for quaternary hybrid perovskites of the form (Cs/FA)Pb(Br/I)3 and (Cs/FA)Sn(Br/I)3. Graph neural network potentials trained on density functional theory data handle efficient structure relaxations, while secondary models predict energies directly from unrelaxed structures to account for alloy disorder and molecular orientations. The resulting maps indicate that the tin-based system occupies a narrower range of stable compositions than the lead-based counterpart, with peak stability at high iodine content and no extra stabilization near the center of the composition space. This restricts the compositional tuning options available for tin perovskites.","feed_headline":"Tin perovskites show narrower stable regions than lead ones","feed_subtitle":"ML maps place maximum stability at high iodine content with none near the composition center.","key_machinery":"Two-level ML strategy: graph neural network interatomic potentials for DFT-trained relaxations plus secondary models for direct energy prediction on unrelaxed structures, enabling free-energy calculations across full composition space while including alloy disorder and FA orientations.","core_discovery":"A two-level machine-learning approach combining graph neural network interatomic potentials and secondary energy-prediction models maps the free-energy landscapes of (Cs/FA)Pb(Br/I)3 and (Cs/FA)Sn(Br/I)3 perovskites and reveals narrower stable composition regions for the Sn-based system, maximum stability at high I content, and no stabilization near the center of the composition space.","pith_inferences":["Targeted experiments at high-iodine Sn compositions could test the predicted stability maximum.","The same modeling workflow could be applied to other quaternary alloys where direct DFT sampling remains prohibitive.","If stability trends correlate with device lifetimes, the narrower Sn windows would constrain tin-perovskite photovoltaic designs more tightly than lead ones."],"forward_implications":["Compositional engineering choices are more restricted for tin-based perovskites than for lead-based ones.","Highest stability occurs in iodine-rich compositions for both systems.","Mixed Br/I alloys near the middle of the composition space receive no additional stabilization.","The computed stability maps can directly inform synthesis targets for stable perovskite solar cells."],"fun_headline_variants":["Sn perovskites stability narrower than Pb","ML stability maps peak at high iodine","No stability near perovskite composition center","Free energy shows Sn limits options vs Pb"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The machine-learning models accurately reproduce the free-energy landscapes that include alloy disorder and formamidinium molecular orientations.","fun_headline_variants_meta":{"raw":{"variants":["Sn perovskites stability narrower than Pb","ML stability maps peak at high iodine","No stability near perovskite composition center","Free energy shows Sn limits options vs Pb"]},"model":"grok-4.3","cost_usd":0.006947,"raw_usage":{"total_tokens":3200,"prompt_tokens":627,"num_sources_used":0,"completion_tokens":49,"cost_in_usd_ticks":69474500,"prompt_tokens_details":{"text_tokens":627,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2524,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":627,"tokens_out":49,"duration_ms":19700,"temperature":1.0,"reasoning_tokens":2524,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-29T06:37:20.351537+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"An experimental phase diagram that finds stable Sn-based compositions near the center of the (Cs/FA)Sn(Br/I)3 space or broader stable windows than the lead analog would contradict the reported stability maps.","supporting_citations":[],"review_version":1}