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Paper Citation Record · LEDGER

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org

As of 22 August 2026, this Paper Citation Record lists 100 of 144 outbound references and 0 inbound Pith citation observations for arXiv:2512.11935.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2512.11935 v2

Coverage vector

measured 100 of 144 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:56:28.268568Z

measured 100 of 100 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

100 of 144 outbound references displayed

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Outbound references

Observation 26c05817-420a-4057-b297-b4e6cd314b3f · outbound

This paper cites Scientific discovery in the age of artificial intelligence.Nature, 620(7972):47–60, 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Scientific discovery in the age of artificial intelligence.Nature, 620(7972):47–60, 2023

Reference 1

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Observation f2f0249a-a408-4412-b647-3e77d85db022 · outbound

This paper cites Recent advances and applications of deep learning methods in materials science.npj Computational Materials, 8(1):59, 2022.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Recent advances and applications of deep learning methods in materials science.npj Computational Materials, 8(1):59, 2022

Reference 2

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Observation e452299b-81ab-482b-8453-440fd6d010c2 · outbound

This paper cites The fair guiding principles for scientific data management and stewardship.Scientific data, 3(1):1–9, 2016.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org The fair guiding principles for scientific data management and stewardship.Scientific data, 3(1):1–9, 2016

Reference 3

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Observation 439c46ec-c8c9-49a1-8e14-e1e797b35a73 · outbound

This paper cites Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities.Information Fusion, 50:71–91, 2019.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities.Information Fusion, 50:71–91, 2019

Reference 4

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Observation 55890762-37a5-4312-af6d-b2d31a55c3b9 · outbound

This paper cites Chatgpt for good? on opportunities and challenges of large language models for education.Learning and individual differences, 103:102274, 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Chatgpt for good? on opportunities and challenges of large language models for education.Learning and individual differences, 103:102274, 2023

Reference 5

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Observation 3e9857b9-c09b-4d0d-bf8c-b7fb22f53949 · outbound

This paper cites A comprehensive overview of large language models.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org A comprehensive overview of large language models

Reference 6

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Observation 2a81ec4b-3cab-4fcd-80d3-83ea69e420ad · outbound

This paper cites GPT-4 Technical Report.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org GPT-4 Technical Report

Reference 7

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Observation 747a5888-1a91-47fe-9d69-ecb7e7e422eb · outbound

This paper cites 14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackathon.Digital discovery, 2(5):1233–1250, 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org 14 examples of how llms can transform materials science and chemistry: a reflection on a large language model hackathon.Digital discovery, 2(5):1233–1250, 2023

Reference 8

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Observation c61375da-4b62-4f68-a80b-382a65071a07 · outbound

This paper cites Leveraging large language models for predictive chemistry.Nature Machine Intelligence, 6(2):161–169, 2024.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Leveraging large language models for predictive chemistry.Nature Machine Intelligence, 6(2):161–169, 2024

Reference 9

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Observation fe949388-703b-4022-a322-8a645e09b592 · outbound

This paper cites Chemnlp: a natural language-processing-based library for materials chemistry text data.The Journal of Physical Chemistry C, 127(35):17545–17555, 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Chemnlp: a natural language-processing-based library for materials chemistry text data.The Journal of Physical Chemistry C, 127(35):17545–17555, 2023

Reference 10

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Observation 519a496a-c93e-41b8-94c2-4040fc3d24eb · outbound

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AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 11

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Observation 508e61ec-03b6-46fd-bb17-33d1e7dd3bff · outbound

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AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 12

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Observation 7e054dd6-397c-4847-9b11-3af9a7f2da0f · outbound

This paper cites Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design.The Journal of Physical Chemistry Letters, 15(27):6909–6917, 2024.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design.The Journal of Physical Chemistry Letters, 15(27):6909–6917, 2024

Reference 13

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Observation 071ffd69-678f-476d-9e3a-74978362f865 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 14

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source=pdf_text observed=2026-08-03T16:56:18.669536Z digest=sha256:f0dd0ec8c3ec9e14f83978b08b228607a1f66b32b060359d621a78cb5c67aaa3

Observation 6bfa94e9-b8b7-44a4-a126-db0c7137b0e3 · outbound

This paper cites Crystal structure generation with autoregressive large language modeling.Nature Communications, 15(1):10570, 2024.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Crystal structure generation with autoregressive large language modeling.Nature Communications, 15(1):10570, 2024

Reference 15

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Observation b966a6fe-c3d1-4b15-9360-b9c74d21ad5a · outbound

This paper cites Detecting hallucinations in large language models using semantic entropy.Nature, 630(8017):625–630, 2024.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Detecting hallucinations in large language models using semantic entropy.Nature, 630(8017):625–630, 2024

Reference 16

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Observation efb7f8d9-c57c-4e5f-8818-90637524c701 · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1–55, 2025.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1–55, 2025

Reference 17

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Observation 6aefc704-9845-49c5-983b-47a836540893 · outbound

This paper cites Towards Mitigating Hallucination in Large Language Models via Self-Reflection.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Towards Mitigating Hallucination in Large Language Models via Self-Reflection

Reference 18

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Observation d9bc3041-4c5d-4fbb-9c91-7290b4934175 · outbound

This paper cites Towards Understanding Sycophancy in Language Models.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Towards Understanding Sycophancy in Language Models

Reference 19

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Observation d33a035e-1108-4ecb-a5e6-346d519d216e · outbound

This paper cites Holistic Evaluation of Language Models.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Holistic Evaluation of Language Models

Reference 20

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Observation 0022afde-4036-4bf5-b37d-4ef8c6cfce5e · outbound

This paper cites Galactica: A Large Language Model for Science.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Galactica: A Large Language Model for Science

Reference 21

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Observation 059aee25-814d-4b92-a6bd-2fee5f83697b · outbound

This paper cites Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files

Reference 22

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Observation 82bead61-1d40-4287-b209-77089efb2793 · outbound

This paper cites ChemCrow: Augmenting large-language models with chemistry tools.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org ChemCrow: Augmenting large-language models with chemistry tools

Reference 23

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Observation bdbb4337-b449-4e8b-a209-5b661032ccb1 · outbound

This paper cites Autonomous chemical research with large language models.Nature, 624(7992):570–578, 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Autonomous chemical research with large language models.Nature, 624(7992):570–578, 2023

Reference 24

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Observation 6fc1dbc3-8b83-4372-85a6-338967a98be1 · outbound

This paper cites LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions

Reference 25

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Observation 0a7cfeb3-ba6b-439b-8121-9670afa11c46 · outbound

This paper cites Fine-Tuned Language Models Generate Stable Inorganic Materials as Text.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Fine-Tuned Language Models Generate Stable Inorganic Materials as Text

Reference 26

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Observation e607027f-9b13-491b-92f2-4fc1431e3685 · outbound

This paper cites From ai for science to agentic science: A survey on autonomous scientific discovery.arXiv preprint arXiv:2508.14111, 2025.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org From ai for science to agentic science: A survey on autonomous scientific discovery.arXiv preprint arXiv:2508.14111, 2025

Reference 27

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Observation 41e46a31-f065-4c98-a1de-5d2cd051ffd4 · outbound

This paper cites ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs

Reference 28

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Observation 21d3d52b-575a-4919-a27c-f64ba232e2bf · outbound

This paper cites Toolformer: Language models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36:68539–68551, 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Toolformer: Language models can teach themselves to use tools.Advances in Neural Information Processing Systems, 36:68539–68551, 2023

Reference 29

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Observation f9089aa9-b30d-4408-9820-074415a18503 · outbound

This paper cites John Wiley & Sons, 2005.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org John Wiley & Sons, 2005

Reference 30

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Observation 352fe5e0-eda4-41f3-993d-f8b67262ff31 · outbound

This paper cites AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org AtomAgents: Alloy design and discovery through physics-aware multi-modal multi-agent artificial intelligence

Reference 31

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Observation 6c4e9fcb-9e51-4aea-a4c8-38bbae99283a · outbound

This paper cites Au- tonomous universal research assistant (aura): Agentic ai meets nanohub’s fair workflows and data.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Au- tonomous universal research assistant (aura): Agentic ai meets nanohub’s fair workflows and data

Reference 32

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Observation 2578bf27-a7dc-44f1-9e46-b7ec8a1f391d · outbound

This paper cites LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation

Reference 33

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Observation 79c528fc-6202-4b54-b797-5255d2c6faef · outbound

This paper cites Scitoolagent: a knowledge-graph-driven scientific agent for multitool integration.Nature Computational Science, pages 1–11, 2025.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Scitoolagent: a knowledge-graph-driven scientific agent for multitool integration.Nature Computational Science, pages 1–11, 2025

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Observation 8333ccb9-f193-47ba-acb5-1af6c7ef20d3 · outbound

This paper cites Towards agentic science for advancing scientific discovery.Nature Machine Intelligence, pages 1–3, 2025.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Towards agentic science for advancing scientific discovery.Nature Machine Intelligence, pages 1–3, 2025

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source=pdf_text observed=2026-08-03T16:56:21.368636Z digest=sha256:96936406471147b505d47aa7bdc7cb9f7a5438c03c2f85f61cf64c125f6f43c3

Observation f6da1faf-82f8-4edf-9f26-a67d27117b27 · outbound

This paper cites Pricing4apis: A rigorous model for restful api pricings.Computer Standards & Interfaces, 91:103878, 2025.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Pricing4apis: A rigorous model for restful api pricings.Computer Standards & Interfaces, 91:103878, 2025

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Observation 3fab6e6f-caed-43b1-b6a5-4cdef4073aa2 · outbound

This paper cites Cost transparency of enterprise ai adoption.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Cost transparency of enterprise ai adoption

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source=pdf_text observed=2026-08-03T16:56:21.624084Z digest=sha256:b198c4e45dbcef57338aebc4efa7550ef408b241dbdb64cb926b69a1cea0d8f9

Observation 4bdb44d7-b1f3-4b95-a2b0-9815edcec709 · outbound

This paper cites ” O’Reilly Media, Inc.”, 2011.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org ” O’Reilly Media, Inc.”, 2011

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Observation 26b6d436-649f-4eb6-b973-ab0ee3fdc200 · outbound

This paper cites The joint automated repository for various integrated simulations (jarvis) for data-driven materials design.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org The joint automated repository for various integrated simulations (jarvis) for data-driven materials design

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source=pdf_text observed=2026-08-03T16:56:21.883563Z digest=sha256:faa1201e0867675517b26f14b25949c646b84553cd52d7f7a602b7f5ed91d663

Observation 079a843c-7dc0-434c-8048-37c9865e411c · outbound

This paper cites Recent progress in the jarvis infrastructure for next-generation data-driven materials design.Applied Physics Reviews, 10(4), 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Recent progress in the jarvis infrastructure for next-generation data-driven materials design.Applied Physics Reviews, 10(4), 2023

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Observation 36f55a55-8330-4201-b558-0867f11c345f · outbound

This paper cites The JARVIS Infrastructure is All You Need for Materials Design.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org The JARVIS Infrastructure is All You Need for Materials Design

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source=pdf_text observed=2026-08-03T16:56:22.113800Z digest=sha256:a76b4efd8bca92c0ecb2265684e0ab9b823742186951e72290b11da8af6d5159

Observation 06acba17-02bd-4ecd-8f5b-beb1f408ff7e · outbound

This paper cites Atomistic line graph neural network for improved materials property predictions.npj Computational Materials, 7(1):185, 2021.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Atomistic line graph neural network for improved materials property predictions.npj Computational Materials, 7(1):185, 2021

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Observation 494a5562-7a1e-4fe3-becc-01940a73a755 · outbound

This paper cites Unified graph neural network force-field for the periodic table: solid state applications.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unified graph neural network force-field for the periodic table: solid state applications

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Observation 3cdd4205-575f-47d6-88b5-7292b9143958 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 44

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source=pdf_text observed=2026-08-03T16:56:22.538876Z digest=sha256:381f7e9d037759b377b761b6d1f143943769f56b8743f5f53449716e52598676

Observation 51f07ce8-2b5e-47ab-bfae-914e6dc196d7 · outbound

This paper cites openai-agents-python: A lightweight, powerful framework for multi-agent workflows.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org openai-agents-python: A lightweight, powerful framework for multi-agent workflows

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source=pdf_text observed=2026-08-03T16:56:22.626247Z digest=sha256:990694c23fe26f49934ee70c69802dc33a69b01b7d3d424bbc9808b6e77fd033

Observation e5fe3c01-7b47-4ac5-b499-3b0e23193f2a · outbound

This paper cites Agentic AI for Science (AAI4Science) Hackathon 2025.https://github.com/ atomgptlab/aai4science, 2025.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Agentic AI for Science (AAI4Science) Hackathon 2025.https://github.com/ atomgptlab/aai4science, 2025

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source=pdf_text observed=2026-08-03T16:56:22.688708Z digest=sha256:0ad4f28d93892d76b6f91fe8b738bbb311f814a4c958c179a3424e36f4154ec4

Observation f1b76e07-71ab-474a-abf8-f6b427877faf · outbound

This paper cites A survey on large language model based autonomous agents.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org A survey on large language model based autonomous agents

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source=pdf_text observed=2026-08-03T16:56:22.740441Z digest=sha256:d31a7300731439858a423c7b1526a91a8814140379364f1a1e6088e7936e8080

Observation 97029f17-d1b9-4371-880c-033ec9a11b2d · outbound

This paper cites Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey.IEEe Access, 2025.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Agentic ai: Autonomous intelligence for complex goals–a comprehensive survey.IEEe Access, 2025

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source=pdf_text observed=2026-08-03T16:56:22.847991Z digest=sha256:8da734b5dbe3b96355320f55fd6a86cc3aa39970ad2a4dff3566787c339d4d22

Observation ec40c203-eb69-49ba-b655-57c1775dffb0 · outbound

This paper cites Commentary: The materials project: A materials genome approach to accelerating materials innovation.APL materials, 1(1), 2013.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Commentary: The materials project: A materials genome approach to accelerating materials innovation.APL materials, 1(1), 2013

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source=pdf_text observed=2026-08-03T16:56:22.966223Z digest=sha256:e5f2df64bb630f7e71575cc12d4d745421276ca8b629176c308dc5d01bb92078

Observation 694615c8-d0ff-42a3-b922-2aa8c1098fea · outbound

This paper cites Aflow: An automatic framework for high-throughput materials discovery.Computational Materials Science, 58:218–226, 2012.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Aflow: An automatic framework for high-throughput materials discovery.Computational Materials Science, 58:218–226, 2012

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Observation f643c726-d56f-484b-90ee-48498c67341d · outbound

This paper cites The open quantum materials database (oqmd): assessing the accuracy of dft formation energies.npj Computational Materials, 1(1):1–15, 2015.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org The open quantum materials database (oqmd): assessing the accuracy of dft formation energies.npj Computational Materials, 1(1):1–15, 2015

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source=pdf_text observed=2026-08-03T16:56:23.232253Z digest=sha256:383a3c0d4b491eadd4d71d75c48c72a26247aa6fe3b6f6d3aa090d49b648e106

Observation c4c44736-31d0-4c64-a415-ca47e2a9637d · outbound

This paper cites Optimade, an api for exchanging materials data.Scientific data, 8(1):217, 2021.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Optimade, an api for exchanging materials data.Scientific data, 8(1):217, 2021

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source=pdf_text observed=2026-08-03T16:56:23.325538Z digest=sha256:350e2f19038550a142046857e69f3680e4f21ce899e6d085d6bbb45abdc44f59

Observation 6d88158d-e16c-4d45-8d9e-cc0d5f6c2c1e · outbound

This paper cites Protein database searches for multiple alignments.Pro- ceedings of the National Academy of Sciences, 87(14):5509–5513, 1990.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Protein database searches for multiple alignments.Pro- ceedings of the National Academy of Sciences, 87(14):5509–5513, 1990

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source=pdf_text observed=2026-08-03T16:56:23.458186Z digest=sha256:938fbca9448cf3f152f082fd72a2b4319476372f3cfd62e7adc5ac508e4a5aa8

Observation 7f77e353-cddf-4557-984b-9c8f331e6b33 · outbound

This paper cites Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.Nucleic acids research, 50(D1):D439–D444, 2022.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Alphafold protein structure database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.Nucleic acids research, 50(D1):D439–D444, 2022

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source=pdf_text observed=2026-08-03T16:56:23.618017Z digest=sha256:a8ce6da9053917aaea2bfee7f15916f0e80cbe514d736f301ec8816feac34adc

Observation 8c716709-237f-432a-a578-7cb6831afe29 · outbound

This paper cites Chips-ff: Evaluating universal machine learning force fields for material properties.ACS Materials Letters, 7(6):2105–2114, 2025.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Chips-ff: Evaluating universal machine learning force fields for material properties.ACS Materials Letters, 7(6):2105–2114, 2025

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source=pdf_text observed=2026-08-03T16:56:23.863034Z digest=sha256:c2d73a71f6b4fd0f3e6efa763280bf8ff9a91bbb553cbb6e01de3f2fb659a2d8

Observation cc882606-e313-4328-af2f-4571994ceaff · outbound

This paper cites Evolutionary-scale prediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130, 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Evolutionary-scale prediction of atomic-level protein structure with a language model.Science, 379(6637):1123–1130, 2023

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Observation cb0e683c-4acc-400c-a1f6-3850ed9807a3 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Llama 2: Open Foundation and Fine-Tuned Chat Models

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Observation 60bf38a9-e3b3-4f8c-a7fe-fffd0e6c3490 · outbound

This paper cites DeepSeek-V3 Technical Report.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org DeepSeek-V3 Technical Report

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Observation 32be7f36-ed35-45bc-930b-c9e4809c84ca · outbound

This paper cites Qwen Technical Report.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Qwen Technical Report

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source=pdf_text observed=2026-08-03T16:56:24.257910Z digest=sha256:1e3854607ce48a5d2b2b31868886b6218a982e9f1d468e58a845c7dfe2c0433c

Observation 2626eb19-ffef-40a8-b51b-f4b4aa7f01ac · outbound

This paper cites Gemma: Open Models Based on Gemini Research and Technology.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Gemma: Open Models Based on Gemini Research and Technology

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Observation 7420bf05-d9d8-4cdc-b062-a7fee0a143f1 · outbound

This paper cites Kimi k1.5: Scaling Reinforcement Learning with LLMs.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Kimi k1.5: Scaling Reinforcement Learning with LLMs

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Observation d1a85bf3-ea75-4242-974e-f23c1e3060f9 · outbound

This paper cites GPT-OSS-20B: An open-source 20-billion-parameter large language model, 2024.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org GPT-OSS-20B: An open-source 20-billion-parameter large language model, 2024

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Observation 0d743bf3-bebd-4b37-bda8-891196eb9231 · outbound

This paper cites Phi-4 Technical Report.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Phi-4 Technical Report

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source=pdf_text observed=2026-08-03T16:56:24.618331Z digest=sha256:e0d0a5cd13186667ffc572e0d3265fca52d80416331c681623cebf5e31ebbb68

Observation a3a3cb90-189d-4a9e-8de2-6c6392df30f9 · outbound

This paper cites Temperature and compositional dependence of the energy band gap of algan alloys.Applied Physics Letters, 87(24), 2005.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Temperature and compositional dependence of the energy band gap of algan alloys.Applied Physics Letters, 87(24), 2005

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Observation 6b60e159-b924-41b1-9b23-1b98165c8ebf · outbound

This paper cites Jarvis-leaderboard: a large scale benchmark of materials design methods.npj Computational Materials, 10(1):93, 2024.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Jarvis-leaderboard: a large scale benchmark of materials design methods.npj Computational Materials, 10(1):93, 2024

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Observation f3724a34-b6fe-4ef0-a8cf-04b09d6de3dc · outbound

This paper cites WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org WTU-EVAL: A Whether-or-Not Tool Usage Evaluation Benchmark for Large Language Models

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Observation 0a41662b-540b-476d-9ee9-3bfb6ad53693 · outbound

This paper cites SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org SMARTCAL: An Approach to Self-Aware Tool-Use Evaluation and Calibration

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source=pdf_text observed=2026-08-03T16:56:24.904829Z digest=sha256:96fdbe0d37a25a92247d73520c73d1dcec0cbda9400a09c3f170b7fa20759bf5

Observation 284cd21e-84cf-4c84-a033-8afe98f622cf · outbound

This paper cites Advancing tool-augmented large language models via meta-verification and reflection learning.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Advancing tool-augmented large language models via meta-verification and reflection learning

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Observation 4333d217-ff60-41a4-8fb8-7b6d0c09ce17 · outbound

This paper cites Ollama: Local large language model runtime, 2023.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Ollama: Local large language model runtime, 2023

Reference 69

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source=pdf_text observed=2026-08-03T16:56:25.045991Z digest=sha256:e2d6a2a8769333174f78f113916c98a0e0aec7e65ad0ea82c50142c9819ec85b

Observation c120c993-30a9-40ac-ae71-4c99ec67cf90 · outbound

This paper cites Whats the capital of US?.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Whats the capital of US?

Reference 70

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source=pdf_text observed=2026-08-03T16:56:25.113965Z digest=sha256:1da0fc46871ef16a70d6259847999044dd92cb51f0916cf703cb38a8642d1690

Observation ea6dd617-f2c0-4847-8c6f-21d71585df3c · outbound

This paper cites γ‑Al₂O₃ is the most widely used form for catalysis because of its high surface area (~200 m² g⁻¹).

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org γ‑Al₂O₃ is the most widely used form for catalysis because of its high surface area (~200 m² g⁻¹)

Reference 71

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source=pdf_text observed=2026-08-03T16:56:25.204516Z digest=sha256:d714c7d010752ad4e2b5892ab0714a0786afa0274ae27bf6cdac4a9e9e164270

Observation e35c5b35-4160-4519-89ea-6d0d9dad325a · outbound

This paper cites Glass‑ceramics contain nanocrystalline α‑Al₂O₃ embedded in a glassy matrix, combining high hardness with low thermal expansion.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Glass‑ceramics contain nanocrystalline α‑Al₂O₃ embedded in a glassy matrix, combining high hardness with low thermal expansion

Reference 72

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source=pdf_text observed=2026-08-03T16:56:25.302771Z digest=sha256:c72a6682d653656c1895ffcaff78b1cc540106ac7b497d861f97dcfd59ad2c45

Observation f62ed777-0978-423d-a763-f1fe8e75e6cd · outbound

This paper cites ALD is the most common route for conformal Al₂O₃ thin films; the typical precursor is trimethylaluminum (TMA) with water or ozone.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org ALD is the most common route for conformal Al₂O₃ thin films; the typical precursor is trimethylaluminum (TMA) with water or ozone

Reference 73

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source=pdf_text observed=2026-08-03T16:56:25.355758Z digest=sha256:18cf037872b438dde3894d386d9b68747324451ed9363c81ae1703f83ac2c5d3

Observation f4387d1a-ecfb-41be-a5b0-fc8afe2bea73 · outbound

This paper cites Composite materials combine the high hardness of Al₂O₃ with the toughness or thermal conductivity of the secondary phase.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Composite materials combine the high hardness of Al₂O₃ with the toughness or thermal conductivity of the secondary phase

Reference 74

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source=pdf_text observed=2026-08-03T16:56:25.443398Z digest=sha256:8a319f3563274017cb7726511c8c43f546fa7d1449d39ea4c23979d6b1fdeeb2

Observation 3a6bf007-6e5a-4d62-b958-6d44e92cfffc · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 75

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source=pdf_text observed=2026-08-03T16:56:25.526972Z digest=sha256:c370d8ec4a153d3e5e3dc70cd6f37ae774023b2c684f0026a377e09f43950ceb

Observation c6e60f1b-facc-4a28-9846-3aa1d8b34b0b · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 76

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source=pdf_text observed=2026-08-03T16:56:25.616781Z digest=sha256:9a49e87037768473a4edc10fcfa6aa879e69df5e2df673693ac95331b2fefe97

Observation 249a4d82-ba75-41fb-ba9c-dbd3cd0ced55 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 77

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source=pdf_text observed=2026-08-03T16:56:25.712334Z digest=sha256:576609763fd345121fe036c9b8658119d715bed84f1796449d8c56e2da715d6e

Observation ed32603a-38bf-4f91-a6a1-49a60c66f095 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 78

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source=pdf_text observed=2026-08-03T16:56:25.778283Z digest=sha256:65f097d22e3c73ab92339d80ed9779e53aa72b812f0e3891348e88bbd4142621

Observation 69a9f6b2-2331-4fbf-ad00-2ad4e510e279 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 79

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source=pdf_text observed=2026-08-03T16:56:25.853040Z digest=sha256:8a50f955bc61c22dae98493954623b4a60f4f7ba4cf44ab7a0a512e6b8324a61

Observation 486239d8-1394-44e2-b0b3-3f9dc4e2d2fc · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 80

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source=pdf_text observed=2026-08-03T16:56:25.892063Z digest=sha256:e497dd8ade63f9f029131e9f3ebeff7dc23820d26bff021bbfb015c22d0bfaac

Observation c1582355-0042-472f-8082-bf5ce1f29672 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 81

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source=pdf_text observed=2026-08-03T16:56:25.949612Z digest=sha256:9d6e71f16f04d10653a84bf7a2d6c2da0e0aa9886dbb20c0c651984070e73036

Observation 51f45232-5229-4add-86b5-57fe01cd5544 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 82

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source=pdf_text observed=2026-08-03T16:56:26.102116Z digest=sha256:dd820b7180e4b966d2fd8ac6674d7f2b24b5ed4466941be74d99ff3e54198b07

Observation 4d022fc4-15f4-43d6-9967-a928c62a670e · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 83

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source=pdf_text observed=2026-08-03T16:56:26.230075Z digest=sha256:18c9984c3454deb1041c6b0a541b52a10bd5a9152d37d7235b990002b3d21816

Observation 5da4a640-a035-4ce3-a309-596939737adc · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 84

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source=pdf_text observed=2026-08-03T16:56:26.375593Z digest=sha256:b6b16fa1dadc430e93c8f5cbf7a8124a0f4672783232687e4773e8faea384ba4

Observation c800d234-0fc2-423f-a91d-b2e2b1a46bc7 · outbound

This paper cites First‑principles study of Al₂O₃ polymorphs,.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org First‑principles study of Al₂O₃ polymorphs,

Reference 85

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source=pdf_text observed=2026-08-03T16:56:26.487966Z digest=sha256:5bc7cbc18307bd12b3628e02fc32e70e350ff99de13008d0d46e64ce96404b2a

Observation c2ef74c2-64d1-4e14-9073-8faf161eca18 · outbound

This paper cites High‑temperature phase diagram of Al₂O₃,.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org High‑temperature phase diagram of Al₂O₃,

Reference 86

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source=pdf_text observed=2026-08-03T16:56:26.586313Z digest=sha256:b74dc01642d265ecb8cb9a5e58688ff22687ed1e251d11ed2ab1092c0029148d

Observation 145d6db0-ac7b-462d-bc32-ba55c8909c98 · outbound

This paper cites Al₂O₃ thin films by ALD: A review,.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Al₂O₃ thin films by ALD: A review,

Reference 87

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source=pdf_text observed=2026-08-03T16:56:26.657294Z digest=sha256:04f6801ee5854db371f1165f2ac12292471225ddc7ac71ea26eab94ce7d4e023

Observation 07b741da-d5b7-4c7d-9c80-aa84284f6cc5 · outbound

This paper cites Doping of Al₂O₃: Effects on optical and electronic properties,.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Doping of Al₂O₃: Effects on optical and electronic properties,

Reference 88

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source=pdf_text observed=2026-08-03T16:56:26.711091Z digest=sha256:01bb340b8ebdaeffd59b3120a546ba47da20d6c9d2bbc63de15b8dbc49e61d22

Observation e5ecd1fb-8e8d-4702-ac83-63ca29a6cd61 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 89

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source=pdf_text observed=2026-08-03T16:56:26.796673Z digest=sha256:2a84f6af7807f83ff25fb0cfc8f0503a5650f47c4f6a1e2711fccd23a9a6c9e3

Observation cd28e10b-0e25-4811-96f1-6d0ca42a8624 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 90

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source=pdf_text observed=2026-08-03T16:56:26.951995Z digest=sha256:a3e637f36b5c1f3950ef8bee5ac3679f4dd8452307a8160f7ed2c3acdd4a6ae3

Observation 9bc40312-ea57-44fb-90f6-a413e86596f3 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 91

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source=pdf_text observed=2026-08-03T16:56:27.131373Z digest=sha256:e3e729451cffe4c1265c57cfa607e40d285fa0f820575fbb1d541c1acf40a983

Observation e49c1513-04e8-47a5-b4f8-6cefafc2208a · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 92

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source=pdf_text observed=2026-08-03T16:56:27.253742Z digest=sha256:982c148c73504b25c1600ad645900aceed8fa28dfa27bad3910e2a7a3ca3b971

Observation 550df11a-a57a-40b3-8cb5-7326b63d22ea · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 93

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source=pdf_text observed=2026-08-03T16:56:27.400346Z digest=sha256:616edddf846b7e4a79d34b208c012af8003348edfccd51ec0557c25d11de2d1b

Observation 110ad93f-a23c-4a38-bf49-51ed802a13e7 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 94

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source=pdf_text observed=2026-08-03T16:56:27.526696Z digest=sha256:042dedad631cc47c07c7f4444c6d2a586452f8177bef798cab85402c7fee1150

Observation 18b80d88-8812-4b62-8605-38b1f3285bc1 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 95

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source=pdf_text observed=2026-08-03T16:56:27.645264Z digest=sha256:34dd6655a435004ca6f991d7ccfe5169c6061f9df497839dd5dcd33341e4c2ed

Observation 776a6d84-913c-4a5c-8689-7939216b6f3f · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 96

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source=pdf_text observed=2026-08-03T16:56:27.780269Z digest=sha256:ec7dbac2d40c45b8bda32537cf15c6588681d18ceac5e774adb50137a9b6a373

Observation 105a44d8-28aa-4410-ae4e-20aeced36422 · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 97

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source=pdf_text observed=2026-08-03T16:56:27.888052Z digest=sha256:0a70cb246d2c3b276443e9e917a47b7ac4f38bd18961bc3f5051c695c6a0de5b

Observation b060384f-3f6a-4952-99c8-1468ee987283 · outbound

This paper cites What are the formation energies of SiC, AlN, MgO?.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org What are the formation energies of SiC, AlN, MgO?

Reference 98

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source=pdf_text observed=2026-08-03T16:56:27.984014Z digest=sha256:8e248740a0ac3f13aa0473c52deaf2b91762211b359bbcc7192134e7bf8d54f3

Observation a9debc73-3cd8-43cb-bfcd-6c0fea3f384a · outbound

This paper cites an unresolved cited work.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Unresolved cited work

Reference 99

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no resolver link, observed 2026-08-03T16:56:28.125086Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T16:56:28.125086Z digest=sha256:7ae7a3d20f33e85f494de0730b59dd847881ecaaf307689778e373ae328fdb5b

Observation e5bf113d-cff3-49c4-bd2b-a1bf435f4d20 · outbound

This paper cites Among materials with bulk modulus > 150 GPa, which has the lowest ehull?.

AGAPI-Agents: An Open-Access Agentic AI Platform for Accelerated Materials Design on AtomGPT.org Among materials with bulk modulus > 150 GPa, which has the lowest ehull?

Reference 100

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source=pdf_text observed=2026-08-03T16:56:28.268568Z digest=sha256:dbbfde0ca1ac12c526bf6975ef7126db89d77328cf9b6be1f4527169fe6f426a

Pith citing papers

No inbound Pith citation observations are available.