Pith. sign in

REVIEW 4 major objections 8 minor 47 references

Human and LLM Collaboration for Accelerated Materials Synthesis and Discovery

T0 review · 4 major / 8 minor · reviewed 2026-07-09 · glm-5.2

Pith's one-line read LLMs Match Human Chemists at Synthesis Planning

desk verdict Real head-to-head human vs. LLM synthesis comparison with genuine experiments, but the statistical equivalence claim is underpowered and the Ba3PtO5 discovery is serendipitous rather than a product of the LLM workflow. read the letter →

arxiv 2607.07604 v1 pith:O7WXNA26 submitted 2026-07-08 cond-mat.mtrl-sci

classification cond-mat.mtrl-sci
keywords LLMmaterialssynthesisRuddlesden-Popperclosed-loopdiscoveryperovskiteBa3PtO5homologousseriessolid-statechemistry
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether large language models can write synthesis recipes for solid-state materials as effectively as trained human chemists, and whether iterating with experimental feedback improves either agent's performance. The authors target the Ruddlesden-Popper oxide family, a series of layered perovskite-related compounds chosen because it contains both well-established members and plausible but unreported ones. For each target, a human chemist and an LLM independently write a synthesis procedure; both are executed in parallel in the lab, and results are fed back for a second round. The central finding is that LLM-generated plans succeed at rates statistically indistinguishable from human plans for known materials (75-83% success in round one), and perform comparably for discovering new phases (17-22% in round one). After closed-loop feedback, success rates for known materials remain similar between the two agents, though the human edges ahead on new-phase discovery in round two. The paper also reports the serendipitous discovery of Ba3PtO5, a new structural prototype that fills a gap in a dimensional-reduction sequence the authors formalize as the Rock-Salt Perovskite homologous series (AX)m(ABX3)p, connecting three-dimensional perovskites through two-dimensional Ruddlesden-Popper phases to a new one-dimensional chain structure and onward to known zero-dimensional isolated octahedra.

What carries the argument

The central mechanism is a closed-loop experimental benchmark: a target compound is selected, a human and an LLM independently generate synthesis procedures, both are carried out in duplicate in the laboratory, products are characterized by X-ray diffraction, and failures are fed back for a second iteration. The structural discovery of Ba3PtO5 arises from the Rock-Salt Perovskite homologous series (AX)m(ABX3)p, in which successive insertion of rock-salt layers reduces the dimensionality of corner-sharing octahedral connectivity from 3D (m=0, p=1) to 2D (m=1) to the newly identified 1D case (m=2, p=1) and 0D (m=3, p=1).

What would settle it

Repeat the human-versus-LLM synthesis benchmark on a materials family requiring non-standard techniques (e.g., high-pressure synthesis, hydrothermal methods, or atmosphere-controlled flux growth). If the human success rate significantly exceeds the LLM success rate in that regime, the comparability claim does not generalize beyond standard solid-state synthesis.

Watch

Extended reading notes

Core claim

The paper establishes two linked results. First, LLMs and human chemists produce synthesis plans with comparable success rates for both known and previously unreported Ruddlesden-Popper oxide materials, as verified by in-lab powder X-ray diffraction. Second, the collaborative process yielded Ba3PtO5, a new compound whose structure represents the one-dimensional member of a generalized Rock-Salt Perovskite homologous series (AX)m(ABX3)p, unifying the progression from 3D perovskite connectivity through 2D Ruddlesden-Popper layers, the newly identified 1D chain motif, and known 0D isolated octahedra within a single parameterized family indexed by rock-salt and perovskite unit counts.

Load-bearing premise

The claim that LLMs and humans perform comparably rests on a small set of targets drawn exclusively from the Ruddlesden-Popper oxide family, a relatively well-studied series whose synthesis often follows standard solid-state methods. Whether this statistical parity extends to broader materials classes requiring more diverse or specialized synthetic techniques is not tested.

Editorial extensions

If this is right

  • If LLMs can match human chemists on synthesis planning within a well-studied material family, the bottleneck in materials discovery shifts from plan generation to experimental execution and characterization throughput.
  • The Rock-Salt Perovskite series (AX)m(ABX3)p predicts specific compositions for 1D chain structures at m=2 and 0D structures at m=3, providing a roadmap for targeted synthesis of dimensionally reduced perovskite derivatives.
  • Closed-loop feedback from experimental outcomes to LLMs did not substantially improve success rates between rounds one and two, suggesting that single-round LLM synthesis plans may already capture most of the accessible thermodynamic guidance, or that the feedback format needs refinement.
  • The serendipitous discovery of Ba3PtO5 via crucible reaction highlights that LLM-human collaboration frameworks should account for unplanned reaction pathways, which current evaluation metrics may not capture.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The comparability of human and LLM performance is established only for Ruddlesden-Popper oxides, a family chosen partly because its synthesis tends to follow standard solid-state protocols. Extending this benchmark to families requiring specialized techniques (flux growth, floating-zone, high-pressure, atmosphere-sensitive synthesis) would test whether the statistical parity holds or whether human
  • The discovery of Ba3PtO5 was serendipitous, arising from a platinum crucible reaction rather than from either the human's or the LLM's intended plan. This suggests that the most novel discoveries in human-LLM collaborative frameworks may emerge from experimental contingencies rather than from the planning agents themselves, raising the question of whether current evaluation frameworks adequately c
  • The dimensional-reduction series (AX)m(ABX3)p makes testable predictions: specific A3BX5 compositions beyond Ba3PtO5 should be synthesizable as 1D chain structures, and the m=2, p>1 members should produce intermediate dimensionalities between 1D chains and 2D layers. A systematic search across A-site and B-site cation combinations could validate or falsify the generality of this structural princip
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. This manuscript reports a prospective experimental study comparing human- and LLM-generated synthesis plans for Ruddlesden-Popper (RP) oxide materials, using a closed-loop feedback design across two rounds. Synthesis outcomes were determined by powder XRD, with ~12 known and ~6-9 unknown targets per round. The authors report broadly comparable success rates between human and LLM plans (e.g., 83(8)% vs. 75(9)% for known materials in round one). As a serendipitous outcome of the collaborative process, the authors discovered Ba3PtO5, which they identify as a new structural prototype representing the 1D member of a proposed Rock-Salt Perovskite (RSP) homologous series (AX)m(ABX3)p. The experimental work is real: syntheses were performed, products characterized by PXRD and SCXRD, and structures solved with standard tools (GSAS, SHELXL). The central statistical claim of comparable human-LLM performance, however, requires more careful treatment of the paired replicate structure of the experimental design.

Significance. The manuscript makes two distinct contributions. First, it provides prospective experimental validation of LLM synthesis planning in a controlled, closed-loop setting—a valuable data point in a field where most LLM benchmarks are computational rather than experimental. Second, the discovery of Ba3PtO5 and its placement within the (AX)m(ABX3)p homologous series is a concrete structural chemistry result, supported by SCXRD. The RSP framework connecting 3D perovskites through 2D RP phases to the new 1D prototype and known 0D structures is a clean dimensional-reduction argument. The closed-loop experimental design, with results fed back to both human and LLM, is a genuine methodological strength. However, the statistical framework for the human-LLM comparison has a load-bearing issue that must be addressed before the comparative claims can be considered well-supported.

major comments (4)
  1. §II, Fig. 3b/c and accompanying text: The standard errors reported (e.g., SE=8% for p=0.83) are consistent with treating trials A and B as independent Bernoulli observations (n≈24). However, the manuscript states that trials A and B follow the same written synthesis plan for the same target (e.g., 'both followed the same synthesis plan' for Ba2CeO4 human trials). These are paired reproducibility replicates, not independent draws from the population of possible synthesis plans. Pooling them as independent underestimates the standard error. If success is scored per-target (n≈12 for known materials), the SE for round-one known materials would be approximately 11% rather than 8%, and for unknown materials approximately 12-13% rather than 9-10%. This matters because the paper's claim of 'similar' performance rests entirely on the criterion that rates are 'within one standard error of the比例.'
  2. §II, Fig. 3b/c: The criterion 'within one standard error of the proportions' is not a formal equivalence test. With n≈12 per group, only differences exceeding roughly 30-40 percentage points would be detectable at conventional significance levels. The claim that human and LLM performance is 'similar' is an absence-of-evidence claim, not evidence of equivalence. The authors should either (a) reframe the claim as 'no statistically significant difference was detected given the sample size,' with the corresponding power analysis, or (b) apply a proper equivalence test (e.g., TOST) with pre-specified equivalence bounds.
  3. §III (Methods): The LLM model(s) used, prompting strategy, and version are not specified in the main text. Fig. 4 references GPT-5.2 and 'different LLMs,' but the primary experiments in Fig. 3 do not state which model was used. This is a critical omission for reproducibility—the LLM is one of the two agents being compared, and its identity and access method must be documented.
  4. §II, paragraph on Ba3PtO5 discovery: The discovery of Ba3PtO5 resulted from BaCO3 reacting with the Pt crucible during a flux growth, not from the targeted reaction between BaCO3 and Tb4O7. The text is transparent about this ('Instead of the intended reaction... BaCO3 reacted with the Pt crucible'), but the framing of Ba3PtO5 as an outcome of 'human-LLM collaboration' is imprecise. The discovery arose from serendipitous crucible reactivity during execution of the human recipe, not from the LLM's synthesis plan. The authors should clarify that this discovery resulted from experimental execution of the human plan, not from the LLM recipe.
minor comments (8)
  1. §I: The generalization from a single, well-studied homologous series (RP oxides) to 'broader materials synthesis' is unstated. The authors should add a sentence acknowledging this scope limitation.
  2. §II: Several targets were excluded from effective comparison (Sm2CoO4 required specialized techniques, LaNiO3 required flux methods the LLM did not suggest). The denominator used for the aggregate success rates should be clarified—were these targets included or excluded from the percentages in Fig. 3b/c?
  3. Fig. 3a: The outcome matrix is informative but dense. A legend clarifying the distinction between 'success = target phase detected' and 'success = new phase discovered' would help the reader, as these are different criteria applied to different target types.
  4. Fig. 4: The color scale and axis labels are small. It is unclear how many LLM models were compared and whether the same targets were used for all models. The caption should state which models were tested and how many targets each model was compared on.
  5. §II, Ba2CeO4/Ba2TbO4: The LeBail refinement results (P4/mmm, a=4.38 Å, b=13.33 Å for Ba2CeO4; P4/mmm, a=4.29 Å, b=8.76 Å for Ba2TbO4) are mentioned but the structures are left unresolved. A brief statement on why these could not be solved, or whether they are being investigated further, would help the reader.
  6. §V (Acknowledgments): The acknowledgment of Carly Weisblum for illustrations is appropriate. No changes needed, but confirming that all contributor roles are accurately reflected is standard practice.
  7. The SI is referenced extensively (pp. 1-80) but was not provided as part of the main manuscript. The referee assumes the SI contains the full synthesis plans, PXRD refinements (Fig. S1-S32), and crystallographic tables (Tables S1, S2). The authors should ensure the SI is complete and that all referenced figures and tables are included.
  8. Reference [24] is dated 2026 and is an arXiv preprint. The authors should verify the citation is accurate and that the preprint is publicly accessible.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for a careful and constructive report. The referee correctly identifies that the manuscript makes two distinct contributions: (1) prospective experimental validation of LLM synthesis planning in a closed-loop setting, and (2) the discovery of Ba3PtO5 as a new 1D member of the Rock-Salt Perovskite homologous series. We agree that the statistical framework for the human-LLM comparison requires revision, and we address each major comment below.

read point-by-point responses
  1. Referee: §II, Fig. 3b/c: Standard errors treat trials A and B as independent Bernoulli observations (n≈24), but they are paired reproducibility replicates following the same written plan. Pooling as independent underestimates the SE. If scored per-target (n≈12), SE would be ~11% rather than 8% for known materials, and ~12-13% rather than 9-10% for unknowns.

    Authors: The referee is correct. Trials A and B for a given target follow the same written synthesis plan and are properly understood as reproducibility replicates, not independent draws from a population of possible plans. Our current error bars treat each trial as an independent Bernoulli observation, which underestimates the standard error. We will revise the analysis to score success per-target (n≈12 for known materials per round), yielding SEs of approximately 11% for known materials and 12-13% for unknown materials, as the referee indicates. The revised figures and text will reflect this corrected treatment. revision: yes

  2. Referee: §II, Fig. 3b/c: The criterion 'within one standard error of the proportions' is not a formal equivalence test. With n≈12 per group, only differences exceeding ~30-40 percentage points would be detectable. The claim of 'similar' performance is an absence-of-evidence claim, not evidence of equivalence. Should either reframe as 'no statistically significant difference detected' with power analysis, or apply a proper equivalence test (e.g., TOST) with pre-specified bounds.

    Authors: We agree that 'within one standard error' is not a formal equivalence test and that our current framing overstates the strength of the comparison. With n≈12 per group, the study is powered only to detect large differences. We will reframe the claim as 'no statistically significant difference was detected given the sample size' and include an explicit power analysis showing the minimum detectable difference at conventional significance levels. We considered applying TOST, but note that specifying meaningful equivalence bounds for synthesis success rates is itself non-trivial and somewhat arbitrary; we judge that an honest power analysis, combined with the reframed language, more accurately conveys what the data do and do not show. The conclusion section will also be revised to match this more cautious framing. revision: yes

  3. Referee: §III (Methods): The LLM model(s) used, prompting strategy, and version are not specified in the main text. Fig. 4 references GPT-5.2 and 'different LLMs,' but the primary experiments in Fig. 3 do not state which model was used. Critical omission for reproducibility.

    Authors: The referee is correct that this information is missing from the main text. The primary experiments in Figure 3 used GPT-4 (specifically the version accessible via the ChatGPT Plus interface during the experimental period, June–August 2024). The full prompts and LLM responses are included in the Supplementary Information, but the model identity, version, and access method must also be stated in the main Methods section. We will add a paragraph to §III specifying the model, the prompting strategy (zero-shot, with the target composition and a request for a detailed solid-state synthesis procedure), the access method, and the date range of use. Figure 4, which compares multiple models, will be clarified as a separate supplementary analysis. revision: yes

  4. Referee: §II, Ba3PtO5 discovery: The discovery resulted from BaCO3 reacting with the Pt crucible during flux growth, not from the targeted reaction. Framing Ba3PtO5 as an outcome of 'human-LLM collaboration' is imprecise. The discovery arose from serendipitous crucible reactivity during execution of the human recipe, not from the LLM's plan. Should clarify.

    Authors: We agree that the framing should be more precise. Ba3PtO5 was discovered during execution of the human round-two synthesis plan for Ba2TbO4, when BaCO3 reacted with the Pt crucible rather than with the intended Tb4O7. The LLM's plan did not directly produce this discovery. The discovery was serendipitous and arose from experimental execution of the human recipe, not from the LLM recipe. We will revise the text to state this clearly. We note that the broader framing of Ba3PtO5 as an outcome of the collaborative human-LLM study is accurate in the sense that the closed-loop process motivated the round-two single-crystal growth attempt, but the referee is correct that the specific discovery mechanism should not be attributed to the LLM's synthesis plan. We will adjust the abstract and conclusion accordingly. revision: yes

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the paper is primarily experimental, with synthesis outcomes determined by independent in-lab XRD/SCXRD measurements, not by model predictions or self-citation chains.

full rationale

This paper is an experimental study comparing human- and LLM-generated synthesis plans for Ruddlesden-Popper oxide materials. The central claims (success rates, discovery of Ba3PtO5) are validated by independent laboratory measurements: powder X-ray diffraction for phase identification and single-crystal X-ray diffraction for structure determination. The (AX)m(ABX3)p homologous series framework is a descriptive classification of known structure types, not a fitted model whose outputs are compared to its inputs. Self-citations exist (refs 11, 16, 24, 34, 37) but are used for context or methodology, not as load-bearing premises that define the conclusions. The tolerance factor approach (ref 34) is used to select candidate materials, but the success or failure of synthesis is determined experimentally, not by the tolerance factor itself. No derivation chain reduces to its inputs by construction. The statistical methodology concern raised by the skeptic (treating paired replicates as independent) is a correctness risk, not a circularity issue. The paper is self-contained against external benchmarks (in-lab synthesis and characterization).

Assumptions & free parameters 3 free parameters · 4 assumptions · 2 invented entities

The paper has no fitted mathematical model or derived constants; the free parameters are experimental design choices (target selection, LLM prompting, success thresholds). The key invented entity (RSP series) is grounded in an experimentally solved structure and makes falsifiable predictions about other A3BX5 compositions. The main axioms are domain assumptions about representativeness and measurement sufficiency that are standard in experimental materials science but limit generalizability.

free parameters (3)
  • Target material selection (RP series members) = N/A (chosen by authors)
    The specific RP compositions targeted (Ba-Ce-O, La-Ni-O, Sm-Co-O, Sr-Ir-O, Sr-Ti-O, Ba-Tb-O systems) were selected by the authors based on tolerance factor principles, not derived from a parameter-free model.
  • LLM model selection and prompting strategy = Not fully specified
    The paper does not detail which LLM was used for the main experiment (only Fig. 4 compares multiple models), what system prompts were used, or how responses were selected when the LLM provided multiple options.
  • Success/failure classification threshold = Phase detection by PXRD
    Success for known materials is defined as target phase detection by PXRD; for unknown materials, success is any new phase. The threshold for 'detection' (e.g., minimum phase fraction) is not quantified.
assumptions (4)
  • domain assumption Ruddlesden-Popper phase space is representative of general inorganic synthesis challenges
    The paper selects the RP series because it is 'simultaneously well studied and likely to host undiscovered materials,' but generalizes the human-LLM comparison result to 'materials synthesis and discovery' broadly.
  • domain assumption LLM and human plans are executed faithfully as written
    Two trials (A and B) follow each written plan, but the paper notes trial discrepancies (e.g., R2 LLM Ba2CeO4 where A and B followed different LLM-suggested pathways), indicating that 'the plan' is not always uniquely defined.
  • domain assumption PXRD phase detection is a sufficient proxy for synthesis success
    The paper acknowledges that some 'successful' syntheses produced the target phase as a minority component (e.g., La3Ni2O7), but still counts these as successes.
  • domain assumption The (AX)m(ABX3)p framework is a valid structural classification
    The homologous series is proposed descriptively based on structural connectivity; no thermodynamic or energetic justification is provided for why this series should be continuous or why intermediate members should exist.
invented entities (2)
  • Rock-Salt Perovskite (RSP) homologous series (AX)m(ABX3)p independent evidence
    purpose: Unifies 3D perovskite, 2D RP, new 1D Ba3PtO5, and 0D structures in one dimensional-reduction framework
    Ba3PtO5 is experimentally realized and structurally characterized by SCXRD (SI Tables S1-S2). The 0D A4BX6 and 2D RP members are previously known. The framework makes the falsifiable prediction that other m=2, p=1 compositions (A3BX5 with different A, B, X) should be synthesizable.
  • Ba3PtO5 structural prototype independent evidence
    purpose: The 1D member (m=2, p=1) of the RSP series with corner-sharing octahedral chains
    Structure solved by single-crystal XRD; crystallographic data provided in SI Tables S1-S2. The discovery was serendipitous (Pt crucible reaction) rather than predicted, but the structure is independently verified.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Human and LLM Collaboration for Accelerated Materials Synthesis and Discovery." pith.science (2026). https://pith.science/paper/O7WXNA26

@misc{pith2026260707604,
  author       = {Pith},
  title        = {Pith review of: Human and LLM Collaboration for Accelerated Materials Synthesis and Discovery},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O7WXNA26}},
  note         = {Machine review of arXiv:2607.07604}
}
read the original abstract

Although Large Language Models (LLM) and Artificial Intelligence (AI) tools have enabled a rapid increase in the generation rate of predicted materials, the rate of new materials discovery has lagged behind. This is due to the challenges associated with designing a sequence of chemical reactions to predictably produce new materials, especially in new structure types. Here, we report a study of human and LLM generated recipes for the synthesis of known and new materials. The success of the recipes is determined through in-lab experimentation, and the results are passed back to the humans and LLMs in a closed-loop process to study the effects of their collaboration. The Ruddlesden-Popper homologous series was selected for all material candidates to provide a materials phase space that is simultaneously well studied and likely to host undiscovered materials. We find that humans (H) and LLM (L) have similar success rates: 83(8)% (H) and 75(9)% (L) [known materials, round one], 17(9)% (H) and 22(10)% (L) [unknown materials, round one], 79(8)% (H) and 71(9)% (L) [known materials, round two], and 22(7)% (H) and 14(6)% (L) [unknown materials, round two]. Through this collaborative human-LLM effort, we discovered Ba3PtO5, a material with a new structural prototype that constitutes the missing 1D member of the herein reported dimensionally tunable Rock-Salt Perovskite (RSP) homologous series of the form (AX)m(ABX3)p, of which the Ruddlesden-Popper series is a subset.

Figures

Figures reproduced from arXiv: 2607.07604 by the authors.

Figure 1
Figure 1. FIG. 1. Overall workflow for the synthesis prediction experiment. a) A material candidate from the Ruddlesden-Popper series [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Sample prompt and corresponding synthesis plans. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Experimental results of human vs LLM study. (a) [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Comparison of human synthesis plans to different [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Rock-Salt Perovskite Homologous Series (AX) [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

47 extracted references · 47 canonical work pages

  1. [1]

    A target compound is selected

  2. [2]

    The LLM is asked for a synthesis procedure for the target compound, and its response recorded

    A human solid-state chemist writes down their syn- thesis plan, using their knowledge of how materials form. The LLM is asked for a synthesis procedure for the target compound, and its response recorded

  3. [3]

    Both synthesis recipes are carried out in two trials, A and B, in parallel, based solely on the written synthesis descriptions

  4. [4]

    The synthesis outcomes for each recipe are deter- mined using X-ray diffraction

  5. [5]

    The human uses the results to design their next reaction, and likewise feed the results back into the LLM to generate an- other round of synthesis plans

    If the LLM and human recipes are both unsuccess- ful, steps 2–5 are repeated. The human uses the results to design their next reaction, and likewise feed the results back into the LLM to generate an- other round of synthesis plans. This workflow is repeated for different chosen target compounds in order to gather statistically relevant data on LLM perform...

  6. [6]

    Khorshidi, A.; Peterson, A. A. Amp: A modular ap- proach to machine learning in atomistic simulations. Computer Physics Communications2016,207, 310–324

  7. [7]

    M.; Salakhutdinov, R.; Tenenbaum, J

    Lake, B. M.; Salakhutdinov, R.; Tenenbaum, J. B. Human-level concept learning through probabilistic pro- gram induction.Science2015,350, 1332–1338

  8. [8]

    P.; Aktulga, H

    Thompson, A. P.; Aktulga, H. M.; Berger, R.; Bolin- tineanu, D. S.; Brown, W. M.; Crozier, P. S.; in ’t Veld, P. J.; Kohlmeyer, A.; Moore, S. G.; Nguyen, T. D.; Shan, R.; Stevens, M. J.; Tranchida, J.; Trott, C.; Plimp- ton, S. J. LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales.Computer P...

Show all 47 references
  1. [9]

    Med- BERT: pretrained contextualized embeddings on large- scale structured electronic health records for disease pre- diction.npj Digital Medicine2021,4, 86

    Rasmy, L.; Xiang, Y.; Xie, Z.; Tao, C.; Zhi, D. Med- BERT: pretrained contextualized embeddings on large- scale structured electronic health records for disease pre- diction.npj Digital Medicine2021,4, 86

  2. [10]

    Jumper, J. et al. Highly accurate protein structure pre- diction with AlphaFold.Nature2021,596, 583–589

  3. [11]

    J.; Vanderburg, A

    Shallue, C. J.; Vanderburg, A. Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler- 90.The Astronomical Journal2018,155, 94

  4. [12]

    McDuff, D. et al. Towards accurate differential diagnosis with large language models.Nature2025,642, 451–457

  5. [13]

    Segler, M. H. S.; Preuss, M.; Waller, M. P. Planning chemical syntheses with deep neural networks and sym- bolic AI.Nature2018,555, 604–610

  6. [14]

    Tracing the Influence of Large Language Models across the Most Im- pactful Scientific Works.Electronics2023,12

    Petros,anu, D.-M.; Pˆ ırjan, A.; T˘ abus,c˘ a, A. Tracing the Influence of Large Language Models across the Most Im- pactful Scientific Works.Electronics2023,12

  7. [15]

    S.; Aykol, M.; Cheon, G.; Cubuk, E

    Merchant, A.; Batzner, S.; Schoenholz, S. S.; Aykol, M.; Cheon, G.; Cubuk, E. D. Scaling deep learning for mate- rials discovery.Nature2023,624, 80–85

  8. [16]

    Pogue, E. A. et al. Closed-loop superconducting materials discovery.npj Computational Materials2023,9, 181

  9. [17]

    Natural-Language-Interfaced Robotic Synthe- sis for AI-Copilot-Assisted Exploration of Inorganic Ma- terials.Journal of the American Chemical Society2025, 147, 23014–23025

    Huang, L.; Zhang, C.; Fu, Y.; Jiang, Y.; He, E.; Qi, M.- Q.; Du, M.-H.; Kong, X.-J.; Cheng, J.; Cronin, L.; Wang, C. Natural-Language-Interfaced Robotic Synthe- sis for AI-Copilot-Assisted Exploration of Inorganic Ma- terials.Journal of the American Chemical Society2025, 147, ...

  10. [18]

    Szymanski, N. J. et al. An autonomous laboratory for the accelerated synthesis of novel materials.Nature2023, 624, 86–91

  11. [19]

    Song, T. et al. A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand. Journal of the American Chemical Society2025,147, 12534–12545

  12. [20]

    van Duin, A. C. T.; Elbert, D.; McQueen, T. M. An Accelerated, Data-Driven, Materials Discovery Future. 2022; doi: 10.34863/g88h-kq06

  13. [21]

    J.; Le, N

    Wilfong, B.; New, A.; Bassen, G.; Bustine, W.; Pekala, M. J.; Le, N. Q.; Rao, K. K.; Pogue, E. A.; Gien- ger, E.; Winiarski, M. J.; McQueen, T. M.; Stiles, C. D. Ternary materials discovery using human-in-the-loop generative machine learning.RSC Advances2025,15, 19126–19131

  14. [22]

    A.; Mackey, T.; Nilforoshan, H.; Xu, M.; Badding, C

    Riesel, E. A.; Mackey, T.; Nilforoshan, H.; Xu, M.; Badding, C. K.; Altman, A. B.; Leskovec, J.; Freed- man, D. E. Crystal Structure Determination from Pow- der Diffraction Patterns with Generative Machine Learn- ing.Journal of the American Chemical Society2024, 146, 30340–30348

  15. [23]

    J.; Ouyang, B.; Xiao, P.; Kitchaev, D.; Shi, T.; Zhang, Y.; Wang, Y.; Kim, H.; Zhang, M.; Bai, J.; Wang, F.; Sun, W.; Ceder, G

    Bianchini, M.; Wang, J.; Cl´ ement, R. J.; Ouyang, B.; Xiao, P.; Kitchaev, D.; Shi, T.; Zhang, Y.; Wang, Y.; Kim, H.; Zhang, M.; Bai, J.; Wang, F.; Sun, W.; Ceder, G. The interplay between thermodynamics and kinetics in the solid-state synthesis of layered oxides.Na- ture Mate...

  16. [24]

    J.; Dwaraknath, S

    McDermott, M. J.; Dwaraknath, S. S.; Persson, K. A. A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis.Nature Com- munications2021,12, 3097

  17. [25]

    S.; Sun, W.; Persson, K

    Aykol, M.; Dwaraknath, S. S.; Sun, W.; Persson, K. A. Thermodynamic limit for synthesis of metastable inor- ganic materials.Science Advances2018,4, eaaq0148

  18. [26]

    J.; Wang, Z.; Cruse, K.; Ceder, G

    He, T.; Huo, H.; Bartel, C. J.; Wang, Z.; Cruse, K.; Ceder, G. Precursor recommendation for inorganic syn- thesis by machine learning materials similarity from sci- entific literature.Science Advances2023,9, eadg8180

  19. [27]

    A critical reflection on attempts to machine-learn materials synthesis insights from text- mined literature recipes.Faraday Discussions2025,256, 614–638

    Sun, W.; David, N. A critical reflection on attempts to machine-learn materials synthesis insights from text- mined literature recipes.Faraday Discussions2025,256, 614–638. 8

  20. [28]

    J.; Trewartha, A.; Jain, A.; Sutter-Fella, C

    Cruse, K.; Baibakova, V.; Abdelsamie, M.; Hong, K.; Bartel, C. J.; Trewartha, A.; Jain, A.; Sutter-Fella, C. M.; Ceder, G. Text Mining the Literature to Inform Exper- iments and Rationalize Impurity Phase Formation for BiFeO3.Chemistry of Materials2024,36, 772–785

  21. [29]

    W.; Arbaugh, T.; Pekala, M.; New, A.; Stiles, C

    Staley, E. W.; Arbaugh, T.; Pekala, M.; New, A.; Stiles, C. D.; Le, N. Q.; Bassen, G.; Bun- stine, W.; McQueen, T. Coupling Language Models with Physics-based Simulation for Synthesis of Inor- ganic Materials. 2026;http://arxiv.org/abs/2606. 00315, arXiv:2606.00315 [cs.AI]

  22. [30]

    Applications of natural language processing and large language models in materials discovery.npj Com- putational Materials2025,11, 79

    Jiang, X.; Wang, W.; Tian, S.; Wang, H.; Lookman, T.; Su, Y. Applications of natural language processing and large language models in materials discovery.npj Com- putational Materials2025,11, 79

  23. [31]

    Large Language Models for Inorganic Synthesis Predictions.Journal of the American Chemical Society2024,146, 19654–19659

    Kim, S.; Jung, Y.; Schrier, J. Large Language Models for Inorganic Synthesis Predictions.Journal of the American Chemical Society2024,146, 19654–19659

  24. [32]

    J.; He, T.; Trewartha, A.; Dunn, A.; Ouyang, B.; Jain, A.; Ceder, G

    Huo, H.; Bartel, C. J.; He, T.; Trewartha, A.; Dunn, A.; Ouyang, B.; Jain, A.; Ceder, G. Machine-Learning Ratio- nalization and Prediction of Solid-State Synthesis Con- ditions.Chemistry of Materials2022,34, 7323–7336

  25. [33]

    Chung, V.; Walsh, A.; Payne, D. J. Solid-state synthesiz- ability predictions using positive-unlabeled learning from human-curated literature data.Digital Discovery2025, 4, 2439–2453

  26. [34]

    Prein, T.; Pan, E.; Jehkul, J.; Weinmann, S.; Olivetti, E.; Rupp, J. L. M. Language Models Enable Data- Augmented Synthesis Planning for Inorganic Materials. ACS Applied Materials & Interfaces2025,17, 69221– 69233

  27. [35]

    Text-mined dataset of solid-state syntheses with impu- rity phases using Large Language Model.Scientific Data 2025,12, 1969

    Lee, S.; Cruse, K.; Baibakova, V.; Ceder, G.; Jain, A. Text-mined dataset of solid-state syntheses with impu- rity phases using Large Language Model.Scientific Data 2025,12, 1969

  28. [36]

    A.; Cole, J

    Olivetti, E. A.; Cole, J. M.; Kim, E.; Kononova, O.; Ceder, G.; Han, T. Y.-J.; Hiszpanski, A. M. Data-driven materials research enabled by natural language process- ing and information extraction.Applied Physics Reviews 2020,7, 041317

  29. [37]

    Text-mined dataset of inorganic materials synthesis recipes.Scientific Data 2019,6, 203

    Kononova, O.; Huo, H.; He, T.; Rong, Z.; Botari, T.; Sun, W.; Tshitoyan, V.; Ceder, G. Text-mined dataset of inorganic materials synthesis recipes.Scientific Data 2019,6, 203

  30. [38]

    J.; Buonassisi, T.; Brgoch, J

    Schrier, J.; Norquist, A. J.; Buonassisi, T.; Brgoch, J. In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science. Journal of the American Chemical Society2023,145, 21699–21716

  31. [39]

    A.; McQueen, T

    Bassen, G.; Wilfong, B.; Bunstine, W.; Edmiston, N.; Siegler, M. A.; McQueen, T. M. Tolerance Factor Ap- proach for the Design of Quaternary Materials as Applied to the A 2Ln4Cu2nQ7+n Homologous Series.Journal of the American Chemical Society2024,146, 25190–25199

  32. [40]

    N.; Popper, P

    Ruddlesden, S. N.; Popper, P. New compounds of the K2NiF4 type.Acta Crystallographica1957,10, 538–539

  33. [41]

    N.; Popper, P

    Ruddlesden, S. N.; Popper, P. The compound Sr 3Ti2O7 and its structure.Acta Crystallographica1958,11, 54– 55

  34. [42]

    Mapping the Reaction Land- scapes of Molten-Salt Fluxes

    Bassen, G.; Han, R.; Whoriskey, T.; Iwanicki, A.; Ebeid, R.; Kingsbury, K.; Johnson, J.; Hummel, J.; Kempa, T.; McQueen, T. Mapping the Reaction Land- scapes of Molten-Salt Fluxes. 2026;https://doi.org/ 10.21203/rs.3.rs-9261172/v1, ISSN: 2693-5015

  35. [43]

    Ein Beitrag zur Chemie der Oxocobaltate(II): La 2CoO4, Sm 2CoO4

    Lehmann, U.; M¨ uller-Buschbaum, H. Ein Beitrag zur Chemie der Oxocobaltate(II): La 2CoO4, Sm 2CoO4. Zeitschrift f¨ ur anorganische und allgemeine Chemie 1980,470, 59–63

  36. [44]

    From 1D to 3D: Perovskites within the System HSC(NH 2)2I/CH3NH3I/PbI2 with Maintenance of the Cubic Closest Packing.Inorganic Chemistry2021,60, 3082–3093

    Daub, M.; Hillebrecht, H. From 1D to 3D: Perovskites within the System HSC(NH 2)2I/CH3NH3I/PbI2 with Maintenance of the Cubic Closest Packing.Inorganic Chemistry2021,60, 3082–3093

  37. [45]

    Synthesis, Crystal Structure, and Op- tical Properties of the Broadband Emitting [HSC(NH2)2]3PbBr5.Zeitschrift f¨ ur anorganische und allgemeine Chemie2025,651, e202500164

    Daub, M. Synthesis, Crystal Structure, and Op- tical Properties of the Broadband Emitting [HSC(NH2)2]3PbBr5.Zeitschrift f¨ ur anorganische und allgemeine Chemie2025,651, e202500164

  38. [46]

    C.; Von Dreele, R

    Larson, A. C.; Von Dreele, R. B. General Structure Anal- ysis System (GSAS). 1994; Los Alamos National Labo- ratory Report 86-748

  39. [47]

    Sheldrick, G. M. Crystal structure refinement with SHELXL.Acta Crystallographica Section C: Structural Chemistry2015,71, 3–8

Pith tools

Reviewed July 9, 2026 · model on record in the stance chip above.