{"id":"21df5f6f-ecc8-4540-9cc9-bc5337f63e7f","arxiv_id":"2512.09702","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":4,"one_line_summary":"On the T2 termination of γ-belite (010), water dissociates readily, driven by protonation of surface silica groups, and triggers two types of calcium surface reconstructions that only persist with water.","lead":"Molecular dynamics with a neural-network potential shows how water reacts with the two most stable faces of γ-belite, the key mineral in low-carbon cement. The results identify water-driven surface defects that may explain why this cement phase hydrates slowly and point to ways to activate it.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 'only stable in water' status of the type II Ca dimer rests on a single 100 ps HDNNP run of a pre-formed dimer surface, with no free-energy or DFT validation of the hydrated dimer state; this under-supports a central claim.","rationale":"The reader's weakest assumption was broad HDNNP extrapolation, including T5 and type II. I partially agree, but the sharper issue is that the hydrated type II state is only evidenced by one short pre-seeded trajectory. This is a genuine gap because the central claim of water-stabilized surface reconstruction depends on it. I credit the DFT vacuum optimization for type I and the four-water DFT check for the dimer; those are independent anchors. They do not, however, establish that the fully dimerized surface in bulk water is thermodynamically stable, which is what 'only stable in water' implies. The proposed free-energy and repeat-run check would settle this. The reader's CONDITIONAL verdict already captures the need for additional validation; my concern refines rather than changes it, so the verdict stays UNCHANGED.","tokens_in":19849,"tokens_out":8223,"duration_ms":89094,"concrete_test":"Reconstruct the free-energy profile for the type II hop using the HDNNP: umbrella sampling along the first-layer Ca-Ca distance from ~6.9 Å to ~3.75 Å at the T2-water interface (≥20 windows, ≥2 ns each), and run 5 independent 1 ns unbiased NPT simulations starting from the fully dimerized surface to check spontaneous dissociation. Cross-check the ideal, dimer, and transition-state ensembles with DFT (RPBE-D3) single points on 10 snapshots each. If the profile has a stable minimum at ~3.75 Å and the repeats do not dissociate, the claim is supported; if the dimer state is higher in free energy or dissociates, the conclusion should be weakened to 'one kinetically trapped trajectory.'","verdict_should_be":"UNCHANGED","load_bearing_attack":"The most load-bearing weakness is narrower than the reader's T5 concern. T5 is a side exploration; the central claim is that the type II Ca-dimer defect is stable only in water. That claim is built from (i) a DFT vacuum optimization of one inserted dimer, which relaxes toward a type I pattern, (ii) a DFT check that four water molecules can stabilize a dimer in a small (2×1) vacuum cell, and (iii) one HDNNP NPT simulation of a fully dimerized (4×4) T2 surface in bulk water that retains the dimers for 100 ps after a 10 ps frozen-surface equilibration. Item (iii) shows persistence, not thermodynamic stability. There is no free-energy profile for dimer formation or dissociation, no independent repeats, and no DFT single-point/energy check of the hydrated dimer-row state. The HDNNP is trained mainly on T2/T3 interface geometries; the authors state that it was not trained on T5-like structures (Sec. IV.C.3), and the water-bridged dimer motif is not independently validated. If the potential overstabilizes the Ca-Ca dimer or if the dimer is kinetically trapped behind a high barrier, the 'only in water' conclusion would be an artifact. The Table III protonation/hydroxide counts share the potential-accuracy dependence, but the type II stability claim is the more central and least-supported link.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper trains a high-dimensional neural network potential (HDNNP) on RPBE-D3 data and uses it in molecular dynamics simulations of several terminations of the γ-belite (010) surface in contact with liquid water. The authors find that water adsorbs both molecularly and dissociatively, that dissociation is controlled mainly by protonation of accessible SiO4 surface oxygens, and that the resulting hydroxide ions are mostly adsorbed at surface Ca atoms. The most stable T3 termination is shown to be only weakly reactive, while the slightly less stable T2 termination develops two types of Ca defects: a type I defect (Ca displacement plus SiO4 rotation) that is stable in vacuum, and a type II defect (a Ca dimer) that is claimed to be stable only in water. A full/empty-row T5 termination is also explored as a side case. The central claim is that a variety of surface structures exist at this interface and are stabilized by water.","tokens_in":20194,"tokens_out":5030,"duration_ms":56139,"significance":"If the main claims hold, this is a valuable contribution to the atomistic understanding of γ-belite hydration, relevant to low-carbon cement chemistry. The paper's strengths include an actively learned DFT reference dataset, bulk validation of the HDNNP against DFT and experiment, direct DFT geometry optimizations supporting the vacuum stability of type I defects and the stabilizing role of four water molecules for type II, and carefully documented computational settings. I do not regard the study as circular: the MLP is trained to DFT data and used for interpolation, and the key defect findings are checked by explicit DFT calculations. However, the thermodynamic stability of the type II dimer in water is the load-bearing claim that currently rests on a single 100 ps trajectory; this and the lack of statistical error bars for the principal quantitative results prevent me from recommending acceptance at this stage.","major_comments":[{"comment":"The claim that the type II Ca dimer is \"only stable in water\" is supported by (i) a DFT vacuum optimization that relaxes an inserted dimer toward a type I-like pattern, (ii) DFT stabilization by four explicit water molecules in a small (2×1) vacuum cell, and (iii) one 100 ps HDNNP NPT trajectory of a fully dimerized (4×4) T2 surface in bulk water, with the surface frozen for the first 10 ps. Item (iii) demonstrates kinetic persistence, not thermodynamic stability. There is no free-energy profile, no independent repeat, and no DFT energy check of the hydrated dimer-row state. Because the HDNNP was not trained on this fully dimerized motif, a systematic overstabilization of the Ca–Ca dimer cannot be excluded. This is the central link in the 'only in water' conclusion and the broader 'structural variety stabilized by water' message. I ask for either free-energy sampling along the Ca displac","section":"Section IV.C.3, Type II (Figs. 10–12)"},{"comment":"The authors state that the HDNNP \"has not been trained to T5-like structures\" and then use it to conclude that the full/empty T5 rows \"do not remain intact\" in water. This extrapolation is acknowledged, but the claim as written exceeds the evidence: a single 100 ps MD trajectory of an initially constructed T5 surface cannot establish instability if the potential is untrained for that motif. This is a side exploration rather than the central claim, but it is part of the paper's general suggestion of structural variety. The conclusion should be rephrased as a tentative observation, or additionally supported by DFT calculations or a refined potential.","section":"Section IV.C.3, Full/empty-row reconstruction (Fig. 14)"},{"comment":"The principal quantitative statements—essentially all protons adsorbed at surface oxygens, 96% vs 24% Ca-bound hydroxide for T2/T3, and the time-averaged species counts (32.5 OsH−, 30.7 O*H−, 8.2, 7.6)—are reported without statistical uncertainty. The SI shows standard deviations over 8 trajectories only for the NPT equilibration endpoints (Figs. S8/S9), not for the 1 ns NVT production used for Table III and Figure 3. Without block averages or independent production runs, the differences between T2 and T3 cannot be distinguished from sampling noise. I request error bars for all time-averaged quantities, or additional independent production trajectories.","section":"Table III and Section IV.C.2"}],"minor_comments":[{"comment":"Species classification relies on a 2.9 Å Ca–O adsorption threshold derived from the bulk RDF decay. Since this threshold directly defines H2O*/O*H− counts in Table III and the density profiles in Fig. 3, a sensitivity analysis (e.g., 2.8 and 3.0 Å) would strengthen the claim that the qualitative conclusions are unaffected by this choice.","section":"Section III.C"},{"comment":"For the thick slab, the type-I reconstruction converts only the top surface, but the reported cleavage energy in Eq. (2) averages over both surfaces. The statement that the \"effective stabilization\" of the transformed surface is higher is plausible but not directly demonstrated. Please report the per-surface energy of the reconstructed side separately, or explain the decomposition.","section":"Section IV.C.3, Type I"},{"comment":"The density profiles in Fig. 3 are very informative, but the text sometimes refers to peaks without indicating whether they are statistically converged. A statement on the block-length or time window used for the averages would help.","section":"Section IV.C.2, Vertical density profiles"},{"comment":"There are a few language issues, e.g., \"limiations\" in the Introduction and minor grammatical slips elsewhere. These do not affect the science but should be corrected in a revision.","section":"Introduction and Acknowledgements"}],"recommendation":"major_revision","confidential_remarks":"The paper is a solid application of an established MLP method to an important materials interface, with careful DFT validation of the most central defect finding (type I). The primary gap is the evidence for the thermodynamic status of the type II dimer: one 100 ps trajectory is not enough to support the 'only stable in water' claim, and the untrained T5 extrapolation further weakens the broader structural-variety statement. I believe the identified issues are fixable within the scope of the manuscript through additional sampling and/or free-energy analysis, but they are load-bearing and require a major revision. No concerns about authorship or citation practices."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nThe paper is worth engaging with. It is the first MLP-based MD study of γ-belite(010)-water interfaces, and it delivers a clear mechanistic picture: water dissociation at the T2 termination is driven by protonation of accessible SiO4 oxygens, with most hydroxides staying at surface calcium sites, while T3 is far less reactive. That core claim is supported by direct DFT checks and by a well-documented HDNNP with respectable errors (energy RMSE ~0.6 meV/atom, forces ~36 meV/Å). The type I defect study is the strongest part — a single seed defect converts the whole surface in vacuum, with DFT slab calculations at two thicknesses, and a lower cleavage energy. That is a solid, reproducible result.\n\nThe soft spots are real but not fatal. The type II Ca dimer is claimed to be stable only in water. The evidence is a DFT vacuum optimization that relaxes the dimer away, a small-cell DFT check with four waters that stabilizes it, and one 100 ps HDNNP run of a pre-formed dimer surface in bulk water. That shows persistence, not equilibrium stability. There is no free-energy profile, no repeated seeding runs, and the DFT check is a 2×1 cell rather than a realistic interface. So the 'only in water' conclusion is plausible but under-supported; the paper should soften it to 'stabilized by water in our simulations' or add a free-energy or dissolution test.\n\nThe T5 full/empty-row section is explicitly exploratory — the authors admit the HDNNP was not trained on T5-like structures. Their conclusion that the rows break up in water is tentative, and they say so. That is acceptable but should be identified as a prediction to check with a retrained potential. The bigger issue is reproducibility: neither the trained potential nor the dataset is released, which matters when several conclusions depend on extrapolation.\n\nMinor: Table III averages lack error bars, though the SI shows trajectory statistics for the equilibration phase, so this is easily fixed. The density of 2.82 vs 2.96 g/cm3 experimental is a bit off but within the usual GGA range.\n\nOverall, this is a serious, carefully written simulation paper with a real new mechanistic result. The type II claim needs tempering or stronger evidence, not a desk rejection. I would send it to peer review and ask the authors to either provide a free-energy/umbrella-sampling estimate for dimer formation or rephrase the claim to match the evidence, and to release the potential/data.\n\nBring to reading group if you're interested in mineral-water interfaces or MLP practice.","headline":"Solid first MLP-MD study of γ-belite-water with a convincing protonation-dissociation picture; the type II 'only in water' claim is plausible but under-supported by a single short run.","tokens_in":20666,"tokens_out":3983,"would_cite":true,"duration_ms":38412,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper argues that at the γ-belite (010)–water interface, water dissociation is governed by the protonation of accessible SiO4 oxygen atoms, and that water specifically stabilizes a calcium-dimer surface defect that does not exist in va","keywords":["belite","dicalcium silicate","gamma-belite (010) surface","solid-liquid interface","water dissociation","neural network potential","molecular dynamics","surface defects"],"falsifier":"Run unbiased density-functional-theory molecular dynamics, or use a neural-network potential that explicitly includes T5 and type-II-dimer configurations in its training data, on the fully dimer-covered T2 surface and on the T5 termination in water. If the type II dimers dissociate within picoseconds, or if the T5 full/empty rows remain intact instead of breaking, the paper's central stability assignments are wrong. A complementary observational check would be to look for the predicted 3.75 Å Ca-Ca distance and the bridging hydroxide-water pattern at the wet (010) surface by surface-sensitive","tokens_in":19729,"feed_emoji":"💧","tokens_out":6678,"duration_ms":65924,"temperature":0.7,"pith_summary":"Belite, the dicalcium silicate that makes low-carbon cement possible, is well known for reacting slowly with water. This paper uses a neural network potential trained to density-functional-theory data to run nanosecond molecular dynamics of the most stable γ-belite (010) face in contact with liquid water. The authors find that the extent of water dissociation is set by protonation of exposed SiO4 oxygen atoms: essentially every proton produced by water splitting lands on a surface oxygen, while most hydroxide ions adsorb at surface calcium atoms. They also identify two structural defects on the second-most-stable T2 termination—a calcium displacement plus tetrahedron rotation that is stable even in vacuum, and a calcium dimer that forms and persists only in the presence of water. The broader claim is that water can stabilize a variety of surface structures on belite, which matters for understanding and eventually enhancing its hydration reactivity.","feed_headline":"Water alone stabilizes new calcium dimers on belite's surface","feed_subtitle":"Simulations of gamma-belite's wet surface show water splits at silica-oxygen sites and locks in new calcium dimers.","key_machinery":"The load-bearing tool is a high-dimensional neural network potential (HDNNP)—a machine-learned model that represents the system's energy as a sum of environment-dependent atomic energies—trained on about 24,000 density-functional-theory structures, enabling nanosecond, several-thousand-atom molecular dynamics of the solid-liquid interface. The mechanistic driver is the protonation of accessible SiO4 oxygen atoms: these oxygen sites accept the protons released by water dissociation, and the resulting hydroxide ions are captured by undercoordinated calcium atoms. The analysis mechanics include species labeling from MD trajectories (adsorbed versus solvated versus surface-bound water, hydroxide","core_discovery":"The paper's central finding is that chemistry at the γ-belite (010)-water interface follows a simple proton-transfer rule: accessible oxygen atoms of surface SiO4 tetrahedra become protonated, and the degree of water dissociation is determined by how many such oxygens are exposed. On the T2 termination, essentially all 32 SiO4 tetrahedra carry a proton (32.5 on average), and about 31 of the resulting hydroxide ions adsorb at surface and subsurface calcium sites, completing their octahedral coordination; the T3 termination, with less accessible oxygens, dissociates far less water. Independently, two surface defects appear on T2: type I, a calcium displacement coupled to a SiO4 rotation, is st","pith_inferences":["The same protonation rule should apply to other belite polymorphs and calcium silicates: any termination exposing non-bridging SiO4 oxygens should dissociate water more readily, offering a route to tune hydration reactivity by surface modification rather than bulk doping.","The water-stabilized type II dimer implies that surface calcium mobility on belite in water may be higher than vacuum models suggest; if so, dissolution and early hydration products could nucleate preferentially at such defect sites.","The predicted 3.75 Å Ca-Ca dimer distance and bridging hydroxide pattern are concrete fingerprints that surface-sensitive experiments—grazing-incidence diffraction or vibrational spectroscopy of wet belite—could search for."],"forward_implications":["The most stable T3 termination dissociates little water, while the only slightly less stable T2 termination dissociates much more because its SiO4 oxygens are accessible; termination-specific exposure, not bulk chemistry, controls initial reactivity.","The type I defect—a calcium displacement plus SiO4 rotation—is stable in vacuum and, when seeded, reconstructs the entire T2 surface and lowers its cleavage energy.","The type II defect—a 3.75 Å calcium dimer—is unstable in vacuum, becomes stable when four water molecules (two dissociated) bridge the two calcium atoms, and a fully dimer-covered T2 surface persists in aqueous molecular dynamics.","The full/empty-row T5 termination has a vacuum cleavage energy close to that of T2, but its calcium rows do not remain intact in water, suggesting that water reshapes even near-degenerate surface structures.","The persistence of all observed surface features in long simulations indicates substantial barriers to surface rearrangement, so multiple surface motifs can coexist at the belite-water interface."],"fun_headline_variants":["Belite's wet surface splits H2O only at exposed Si-O sites","Water-induced defects stabilize new states on belite's T2 face","Proton transfer rule governs water dissociation on belite","Surface Si-O exposure controls belite's water splitting dose"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing premise is that the neural network potential, trained on T2 and T3 interfaces, remains accurate for the structures it did not train on—especially the T5 full/empty-row termination and the water-stabilized type II calcium dimer—so the reported defect statistics and stabilities come from the true energy surface.","fun_headline_variants_meta":{"raw":{"variants":["Belite's wet surface splits H2O only at exposed Si-O sites","Water-induced defects stabilize new states on belite's T2 face","Proton transfer rule governs water dissociation on belite","Surface Si-O exposure controls belite's water splitting dose"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000319,"raw_usage":{"total_tokens":1655,"prompt_tokens":777,"completion_tokens":878,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":521,"completion_tokens_details":{"reasoning_tokens":806}},"tokens_in":521,"tokens_out":878,"duration_ms":9969,"temperature":1.0,"reasoning_tokens":806,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-03T17:21:20.200530+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run unbiased density-functional-theory molecular dynamics, or use a neural-network potential that explicitly includes T5 and type-II-dimer configurations in its training data, on the fully dimer-covered T2 surface and on the T5 termination in water. If the type II dimers dissociate within picoseconds, or if the T5 full/empty rows remain intact instead of breaking, the paper's central stability assignments are wrong. A complementary observational check would be to look for the predicted 3.75 Å Ca-Ca distance and the bridging hydroxide-water pattern at the wet (010) surface by surface-sensitive","supporting_citations":[],"review_version":1}