Materials Atlas / Research library

See how the field
asks its questions.

Explore methods, materials and open questions across the literature. Follow a reading note back to the source passages that informed it, then decide what deserves a closer look.

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198publication records

37article bodies retrievedSelected passages used

79abstracts only

82metadata only

109 automated extractions · 109 source rechecks by automation · 0 expert validations. These counts do not mean that every full paper was read. Snapshot: Nov 11, 2026.

Find a starting point

Follow a research thread.

Themes describe how records entered this collection. A paper can appear in more than one theme.

Start with the question

Search a material, method or gap. Reading notes are an entry point into the evidence.

Inspect what was accessed

An abstract, a review and an original experiment offer different kinds of context.

Return to the source

Check the original methods and conditions before carrying a claim into your own study.

Search the researchTitles, materials, methods and open questionsResearch themeSource accessSource type · AI classificationReading notesCollection context

198 publication records

Showing 1–12 of 198.

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Abstract onlyOriginal research · AI classificationJul 31, 2011

A lithium superionic conductor ↗

Reports Li10GeP2S12 with record room-temperature lithium conductivity.

Automated extraction + automated source recheckNot expert validated

Energy & electrolytesLi10GeP2S12 · LGPS sulfide electrolyte →

Inspect notes, evidence & limitations

How this source was classified

Abstract reports original material discovery.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Synthesis and characterization are not described in the abstract.
Basis: Abstract

Author-reported gaps

Solid electrolytes were previously limited to useful conductivity at 50-80 °C.
Basis: Abstract

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

Which synthesis conditions produce the new three-dimensional framework?
Basis: Abstract
How stable is the electrolyte under battery-relevant cycling?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Crossref contains publisher-deposited metadata; absence of an abstract or method is not evidence that the paper lacks it.
Abstract-only evidence; detailed protocols are unavailable.

Exact source scope & provenance

Record: doi:10.1038/nmat3066
DOI: 10.1038/nmat3066
Retrieved: Nov 10, 2026
Index context: A reference in the curated materials collection
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 10, 2026 / Nov 10, 2026
Extraction input SHA-256: d22d994f843da980f845004ffc4996fd89aba34609bb3b8476d4c32019e32152

Retained section locators

AbstractAbstracte995b8643cf9f6498b65450f2970e44bb0dc27a548ca385bfd406f24a5d4b556

Retrieved source responses

Provider response 1 ↗application/json693abb64fc0988b78e098319b77e081759112c1d859930c3b960e39aeea24508
Provider response 2 ↗application/json;charset=UTF-8d1ec801f73c345542b261f3670abb964f0d818c8fbb8f72e49249265d2f899c9

Abstract onlyType not establishedJan 1, 2026

Accelerating quantum materials characterization: hybrid active learning for autonomous spin wave spectroscopy ↗

The passage is a single abstract sentence describing a hybrid autonomous spectroscopy workflow.

Automated extraction + automated source recheckNot expert validated

Research methods

Inspect notes, evidence & limitations

How this source was classified

Insufficient passage detail for research classification.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Agnostic mapping localizes signal.
Basis: Abstract
Physics planning resolves models.
Basis: Abstract

Author-reported gaps

None identified in the retained passages. This does not establish absence from the full publication.

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How does the guarded audit layer affect autonomous decision quality?
Basis: Abstract
What validation benchmarks test the hybrid active learning workflow?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Crossref contains publisher-deposited metadata; absence of an abstract or method is not evidence that the paper lacks it.
Only one abstract sentence is available.
No explicit limitations or gaps are stated.
Method details are too sparse for protocol reconstruction.

Exact source scope & provenance

Record: doi:10.1039/d6dd00232c
DOI: 10.1039/d6dd00232c
Retrieved: Nov 10, 2026
Index context: Method / family search: autonomous materials discovery experimental validation
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 11, 2026 / Nov 11, 2026
Extraction input SHA-256: 958d14ece8f662eb8bc989a12725451de1194a1e9f5053723a8f6488c72a3661

Retained section locators

AbstractAbstractb72c25d8167ff04a9f7105e13963b7b444e7e171c7010bcc3d8b0fbb7ffa27d3

Retrieved source responses

Provider response 1 ↗application/json5943edf093c3b86fc7c14d806eeb5d32a5e8f3f2197981e4b4e73798437462ae

Article body retrievedOriginal research · AI classificationFeb 29, 2024

Advanced Fabrication of 3D Micro/Nanostructures of Gallium Oxide with a Tuned Band Gap and Optical Properties ↗

ULPING synthesized β-Ga2O3 nanostructures on GaP with tunable band gap.

Automated extraction + automated source recheckNot expert validated

Electronics

Inspect notes, evidence & limitations

How this source was classified

Reports original synthesis and characterization.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

ULPING synthesized nanoparticles on GaP using a pulsed fiber laser.
Basis: JATS body; paragraph 3
SEM, EDX, XPS, REELS, and XRD characterized morphology, composition, structure, and band gap.
Basis: JATS body; paragraph 5 · Abstract · JATS body; paragraph 4

Author-reported gaps

Further investigation is needed to study crystalline-structure findings.
Basis: JATS body; paragraph 21

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How do annealing conditions affect metaphosphoric acid removal and nanostructure preservation?
Basis: JATS body; paragraph 31
Which laser-parameter combinations best control heterojunction alignment?
Basis: JATS body; paragraph 31

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

The provider’s article body was retrieved. The extraction used selected, bounded passages; it is not a review of every paragraph, figure, table or supplement.

The retained source content was truncated to a bounded size.

Crossref contains publisher-deposited metadata; absence of an abstract or method is not evidence that the paper lacks it.
Full text was retrieved, but this tool returns bounded selected passages, not a claim that every method, table or supplement was reviewed.
Selected passages may omit methods or supplementary details.
Abstract-only evidence cannot establish unstated protocols.

Exact source scope & provenance

Record: doi:10.3390/mi15030347
DOI: 10.3390/mi15030347
Retrieved: Nov 10, 2026
Index context: Method / family search: gallium oxide semiconductor band gap mobility
Source license: https://creativecommons.org/licenses/by/4.0/ ↗
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 10, 2026 / Nov 10, 2026
Extraction input SHA-256: 1da76069d4174c938faa6908a3349d5703cab96f7a1ca094bd36b3f50896b85c

Retained section locators

AbstractAbstract907b3224bb06fbff953c73a4db9309383180105b5492d034e2a3e20bd338ccb3
MethodsJATS body; paragraph 38e30c5935f0f052aae095d68efacc3a6b890cc5536f56bf954201160b7b90893
MethodsJATS body; paragraph 4773c05915562c4404824228ac95e415cd696870b56133cac0dd3b4dfc401fa63
MethodsJATS body; paragraph 5b6a0c65d0f36abb8c12dabba44307e8661240df43bdd57c2c87464158ab41b49
ConclusionsJATS body; paragraph 31e366d8e1ddd1baf93d0f895894f34ac89697630dbb82fa8d724bc571ba23bb93
IntroductionJATS body; paragraph 14562b88d689f543bddf95280937c791642dffb5b60a5a0fba85abc01f18fb52b
IntroductionJATS body; paragraph 28a5f50dd6c9809e49378156c1bbc89b517e7246dc739dc63e7f0013eb09fd813
Article passageJATS body; paragraph 677f5780b35da6be457c83c941c9ce88b6db028dc6e4a9cf38c7d628764d3d32b
Article passageJATS body; paragraph 78331b1ceb3c92a4e52902f4831167b0411184be476e0e53077e5fc67d4709326
Article passageJATS body; paragraph 8b54545f6c085e9cd54470f24a7d2a6f6978c69ff3038d9ae8ca0d4623da183c2
Article passageJATS body; paragraph 9741037207d4d5d9fa388b89c6a7a0f8c6ec11b41228d85268e86f72d87307dc8
Article passageJATS body; paragraph 10ce9c98d41536304b05e97107d5c2b521369166e4c557db4b75fe1463dfc561d0
Article passageJATS body; paragraph 118510074ef8f1303237707727403d4793643df99b8905fe3e1cbb7a07ef962a88
Article passageJATS body; paragraph 129b5a82650b5ff2e281b9e6da639e0a57c8bc2b95bc6d83af485f5473227753bd
Article passageJATS body; paragraph 1367d058f164e8b2029af2d06b6bb5370c8672da04b13fc30e01dae323d4ac3c80
Article passageJATS body; paragraph 1440ba5c4a4119ccada3fc40b68430941903f36de9ece3d4b90222ddbd521295c6
Article passageJATS body; paragraph 152181272c199c7cd57eaf21bf65f08e7b1514ea0c41d4341e939e5ff04b992813
Article passageJATS body; paragraph 16a1223365c08fdab940ed3d18453b18bf29e6e709cf21c1faeb8bfa986009fc88
Article passageJATS body; paragraph 17e3567eae41543657fdea2b6769c0b43f47d09d7b4189c97c1392800b53adf13b
Article passageJATS body; paragraph 1838060d6932e199a2cdc6a573efc370c715ef83f180e94cf1881bb1255a81c472
Article passageJATS body; paragraph 19b53c23638d2255115237a37470e4fd6640d5e6701f1f3e3d24c6e0659e9fe4de
Article passageJATS body; paragraph 20b7100a59ec43fb830d19095125b7b3a1701f4f5df6d6a5de583df07fd308aa7d
Article passageJATS body; paragraph 21be54445610e87acf67ddd55cbc4988110d60ee3dca034e8ae7b6b8067b14cddb

Retrieved source responses

Provider response 1 ↗application/jsonb1403511e369f0abec3fdb6585253f54e710d830354239ae776691e3dfa5a4ca
Provider response 2 ↗application/json;charset=UTF-8c869446a8f8f1f811d48d39a22dc4eb4a6a1849a69d2594bdbfd08ac4e9a2249
Provider response 3 ↗application/xml6bb0827ed30fa9772c402c1146bc11b6c67f89437f04df0ec6ce915ea3ddb1eb

Abstract onlyOriginal research · AI classificationJun 11, 2026

Analytical Modeling on Investigating the Electric Field Shift Behavior of GaN Vertical FinFETs. ↗

Analytical surface-potential and ON-resistance models were developed for GaN vertical FinFETs and compared with TCAD.

Automated extraction + automated source recheckNot expert validated

Electronics

Inspect notes, evidence & limitations

How this source was classified

Presents a new analytical model and validation.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Solved a two-dimensional Poisson equation using parabolic estimation.
Basis: Abstract
Derived surface-potential and ON-resistance analytical models.
Basis: Abstract

Author-reported gaps

None identified in the retained passages. This does not establish absence from the full publication.

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How well do the models match measurements beyond TCAD?
Basis: Abstract
How does the model perform across other fin geometries?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Open-access full text was not obtained from Europe PMC; current content remains metadata/abstract only.
Only abstract evidence was available.
No full-text protocol details were accessible.

Exact source scope & provenance

Record: doi:10.1021/acsomega.6c03756
DOI: 10.1021/acsomega.6c03756
Retrieved: Nov 10, 2026
Index context: Method / family search: gallium oxide semiconductor band gap mobility
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 10, 2026 / Nov 10, 2026
Extraction input SHA-256: eac7d7f9ee0faf9ec384977d9fd7d2ec5b075af78ae720614ccc95dde9da4b17

Retained section locators

AbstractAbstract0b618ea67d0861701aacfc1d5053031e08b1608ac44b45b49a10fd686bf1328d

Retrieved source responses

Provider response 1 ↗application/json;charset=UTF-84bd797701109ac3a68a54e6ab826c5bab603e1934cc5d513a309e601a35c72ae
Provider response 2 ↗application/json;charset=UTF-8ebe3a582e170dcf1581b284f02d064e971f712a120147911c4fdaa13f6558fe2

Abstract onlyOriginal research · AI classificationMay 25, 2016

Anisotropic and Ultralow Phonon Thermal Transport in Organic–Inorganic Hybrid Perovskites: Atomistic Insights into Solar Cell Thermal Management and Thermoelectric Energy Conversion Efficiency ↗

Equilibrium molecular dynamics examined MAPbI3 thermal transport across phases.

Automated extraction + automated source recheckNot expert validated

Heat & transport

Inspect notes, evidence & limitations

How this source was classified

Original simulations report new thermal transport results.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Equilibrium molecular dynamics simulations estimated thermal conductivities.
Basis: Abstract
Atomistic interaction analysis identified electrostatic contributions.
Basis: Abstract

Author-reported gaps

None identified in the retained passages. This does not establish absence from the full publication.

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How do defects alter anisotropic thermal transport?
Basis: Abstract
How do experimental measurements compare across compositions?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Crossref contains publisher-deposited metadata; absence of an abstract or method is not evidence that the paper lacks it.
Abstract-only evidence limits protocol detail.
No explicit author limitation or unresolved gap is stated.

Exact source scope & provenance

Record: doi:10.1002/adfm.201600284
DOI: 10.1002/adfm.201600284
Retrieved: Nov 10, 2026
Index context: Method / family search: thermoelectric materials thermal transport experiment
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 11, 2026 / Nov 11, 2026
Extraction input SHA-256: 9f6f306b2fdd6f5667d4a1f2fa24523b956e3ba9ee05ef9e2fc17cadc0c0ec56

Retained section locators

AbstractAbstracta06cee7804b99b2bbb13e4f4a5d5e2457a29322eb9528cd389aa79561a24aa80

Retrieved source responses

Provider response 1 ↗application/json5d16328eb5418d278adb2ede5f3c705b9ef75fb4e1b84473e429902e81156104

Abstract onlyReview article · AI classificationMay 22, 2026

Applications of Density Functional Theory in Modern Materials Research ↗

Review of DFT principles, approximations, applications, advantages, and limitations.

Automated extraction + automated source recheckNot expert validated

Research methods

Inspect notes, evidence & limitations

How this source was classified

Summarizes DFT principles and applications.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Summarizes Hohenberg–Kohn theorems and Kohn–Sham formalism.
Basis: Abstract
Discusses LDA, GGA, hybrid functionals, and DFT+U.
Basis: Abstract

Author-reported gaps

None identified in the retained passages. This does not establish absence from the full publication.

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How can AI improve DFT for large-scale materials discovery?
Basis: Abstract
How can DFT better treat band gaps and correlated systems?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Crossref contains publisher-deposited metadata; absence of an abstract or method is not evidence that the paper lacks it.
Abstract-only evidence limits protocol details.
Provided passages do not state explicit unresolved research gaps.

Exact source scope & provenance

Record: doi:10.55677/csrb/03-v03i05y2026
DOI: 10.55677/csrb/03-v03i05y2026
Retrieved: Nov 10, 2026
Index context: Method / family search: density functional theory materials database uncertainty
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 11, 2026 / Nov 11, 2026
Extraction input SHA-256: 0c1f3f1b4f2c4174bce90143ea0621edaefc7cf0e26facc9f8e7eec15d70b02e

Retained section locators

AbstractAbstractdd59a98fe3aefc5944834c636412abd69edcd624022aca4d99e13d6d48db8855

Retrieved source responses

Provider response 1 ↗application/json57fcbed9b930912d25dc780d37c74d10e0f1a5b7b086dede845f4f6040e6294e

Abstract onlyOriginal research · AI classificationOct 9, 2026

Architecture-agnostic knowledge distillation for machine-learning interatomic potentials. ↗

Proposes architecture-agnostic distillation for compact MLIPs.

Automated extraction + automated source recheckNot expert validated

Research methods

Inspect notes, evidence & limitations

How this source was classified

Proposes a new distillation framework.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Joint atomic-energy and Hessian distillation.
Basis: Abstract
Matched training comparisons against a Hessian-distillation baseline.
Basis: Abstract

Author-reported gaps

Existing MLIP distillation suffers architecture mismatch and incomplete geometric supervision.
Basis: Abstract

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How well does the framework transfer to other chemical subdomains?
Basis: Abstract
How does it perform under different teacher-student architectures?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Full-text reuse/access is not established by this metadata record.
Abstract-only evidence.

Exact source scope & provenance

Record: doi:10.1016/j.neunet.2026.109742
DOI: 10.1016/j.neunet.2026.109742
Retrieved: Nov 10, 2026
Index context: Method / family search: machine learning interatomic potential materials molecular dynamics
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 11, 2026 / Nov 11, 2026
Extraction input SHA-256: 0fb14015463b7425a8c4d81a3c34f207f10b440ea7ed6cb3314b9391cab1c24e

Retained section locators

AbstractAbstract214dc764c65eade541cdd83a4df87e302b82b69afdc4cde7e07fc062b4724034

Retrieved source responses

Provider response 1 ↗application/json;charset=UTF-88b9f7ed03c1c1e2f1f3e777ae5cd7c3cadfa4db2c268518a30ae9a3bb9581254
Provider response 2 ↗application/json;charset=UTF-83fbf2a501446e79793085c4b430181d38b384dae83121a4cb596472cba103515

Article body retrievedReview article · AI classificationOct 10, 2026

Artificial intelligence-guided design of high-entropy materials from bulk to nanoscale. ↗

AI/ML can aid HEM design, but data quality and validation limit reliability.

Automated extraction + automated source recheckNot expert validated

Research methods

Inspect notes, evidence & limitations

How this source was classified

Viewpoint/minireview with cited prior studies.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Literature-based synthesis of AI/ML applications in HEM design.
Basis: Abstract · JATS body; paragraph 97 · JATS body; paragraph 100
Conceptual discussion of uncertainty quantification for predictive models.
Basis: JATS body; paragraph 68 · JATS body; paragraph 69 · JATS body; paragraph 70

Author-reported gaps

HEM datasets are often small, heterogeneous, and incomplete.
Basis: JATS body; paragraph 98

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How can provenance-aware databases improve benchmarking and validation?
Basis: JATS body; paragraph 99
Which models best transfer across composition, processing, and length scales?
Basis: JATS body; paragraph 100

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

The provider’s article body was retrieved. The extraction used selected, bounded passages; it is not a review of every paragraph, figure, table or supplement.

The retained source content was truncated to a bounded size.

Full text was retrieved, but this tool returns bounded selected passages, not a claim that every method, table or supplement was reviewed.
Selected passages only; not all sections reviewed.
No primary research results or new data were reported.

Exact source scope & provenance

Record: doi:10.1039/d6na00479b
DOI: 10.1039/d6na00479b
Retrieved: Nov 10, 2026
Index context: Method / family search: density functional theory materials database uncertainty
Source license: This article is licensed under a Creative Commons Attribution 3.0 Unported Licence . You can use material from this article in other publications without reques
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 11, 2026 / Nov 11, 2026
Extraction input SHA-256: 1724f0e7de108fb51f5d4a8a9d1eaafcfa1510926ab63f93e24a2db72d95ec45

Retained section locators

AbstractAbstract56b22682e4bf62d0eea8fc9c680663193679e95a3c494c0eae709bde93195736
Article passageJATS body; paragraph 16c115f37c9e92a33337f0741660fcaa4cf9de09c8b21e75aebd8a16a26bec0d79
Article passageJATS body; paragraph 179d67a5c93827a5736483ff1e798b03739ac37a84c101f0f90526a0f28088ff3e
Article passageJATS body; paragraph 18b9426da022c9ed10452d654172512578df7ebcae26879b2b8629b2cf8d4789a7
MethodsJATS body; paragraph 688e6e10baae080b1c0e4a8734adbad61e98c54958bcbf163c92e45c23ea19e049
MethodsJATS body; paragraph 69cb477333de7e22f0870dd83a2019c4cd2128dd441420a1a2881ad946a9effa81
MethodsJATS body; paragraph 708a3bf279a9403ad426f3236c8151e7145c75ca1d6a0867b1164f1797e5d24f67
ConclusionsJATS body; paragraph 973445ac0f2f2e029933d02149444823f6f4cade10bb6512eea137c88638c5b4be
ConclusionsJATS body; paragraph 98c4a074ba85ca311a13e4b11ec9dd707a97afffa80784f4e2b0883fcb73d82d9e
ConclusionsJATS body; paragraph 99b79f30329339083b4865c02910b8acd4c8709a703c403192feb2debbf40ec1f0
ConclusionsJATS body; paragraph 100c5b25b70dbc62ced2732c8513b403365bb2e2558b84e4b159e4c7567d682666d
Data availabilityJATS body; paragraph 1031d31e74a7ba67061076c59744c53c3bd5dc8168e20f33a5c31c882b2ae84f2c7
Data availabilityJATS body; paragraph 1041d31e74a7ba67061076c59744c53c3bd5dc8168e20f33a5c31c882b2ae84f2c7
IntroductionJATS body; paragraph 15aa8117e2daa71cb098f31035a69df75361ec9fd3b5aaf0fe074b156a8041f8c
Article passageJATS body; paragraph 23530794beed4f75da6c7ae813d02ad0b5b6ca9a07e99229c57e7d1836d14174a
Article passageJATS body; paragraph 3672a8153c6f81214f37c6d86bb8bd3120e7962725564893fa668981344b1883d
Article passageJATS body; paragraph 4ed661bc4273c4870fffb45db99f21c7fa2ee91a06af1375d4162f2a97f6fcf41
Article passageJATS body; paragraph 5b9bc36d68c9b363bbe8ce65c7e4ae9fa791266adbccfc4f8e1905003aa0d70ab
Article passageJATS body; paragraph 64be6541c339dc86b03d6b45701df36d80b3e164e99ee56cb08eb2023c96fa5e8
Article passageJATS body; paragraph 7ea8f99b0527dff681a6eee4efbf78a510b59d1aa05ac8bfb97c4a1b8207fc064
Article passageJATS body; paragraph 83e9e63255a08b6b00e53a5074492ff28ddfb103b477df495af6d450a1aefbbf2

Retrieved source responses

Provider response 1 ↗application/json;charset=UTF-813ef8268afbfc72dcffc3b5740749e4beacab876598f496d290c1acfef5a7a09
Provider response 2 ↗application/json;charset=UTF-8838265d6e17d158312600750fb2d2f55518128810e9d60872b2eafcec59570a0
Provider response 3 ↗application/xml7acabaa8905efdea2fa901f6b2a0ef046193a75d53e022de5cf10531ac91bc1d

Abstract onlyOriginal research · AI classificationOct 28, 2026

Autonomous Discovery of Multi-Material Metamaterials With Programmable Multimodal Thermal Responses via Reinforcement Learning. ↗

An RL-based inverse-design framework generated and validated multi-material architectures for target thermal deformation behaviors.

Automated extraction + automated source recheckNot expert validated

Research methods

Inspect notes, evidence & limitations

How this source was classified

Original inverse-design framework with experimental validation.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Transformer encoder plus reinforcement-learning agent for inverse design.
Basis: Abstract
Hybrid iterative optimization with adaptable structural parameterization.
Basis: Abstract

Author-reported gaps

Sparse-data out-of-distribution inverse design remains a bottleneck.
Basis: Abstract

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How does the framework perform across other material classes?
Basis: Abstract
How robust is the approach under broader manufacturing constraints?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Full-text reuse/access is not established by this metadata record.
Abstract-only evidence; detailed protocols and datasets are unavailable.

Exact source scope & provenance

Record: doi:10.1002/adma.75546
DOI: 10.1002/adma.75546
Retrieved: Nov 10, 2026
Index context: Method / family search: autonomous materials discovery experimental validation
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 11, 2026 / Nov 11, 2026
Extraction input SHA-256: e455630852b29187a522f94eb9eddeb1dce5152d6081642f25898a848562947a

Retained section locators

AbstractAbstract6c5fbbc6e3c94cb25c1d12c14dc997918d7cbf1096fe967a355534cae83e601c

Retrieved source responses

Provider response 1 ↗application/json;charset=UTF-855a1e9d3e49c8548d3bec106501a9b343777b68b5e84b346face16290aea9bf0
Provider response 2 ↗application/json;charset=UTF-88eb9e790b2a35882b90a9d7616d4ade1e21108ff4684b64742ea10d79c871cdb

Abstract onlyOriginal research · AI classificationSep 22, 2026

Ba4Al4SnS12: balancing bandgap and nonlinear optical performance in thioaluminate via multifunctional group engineering. ↗

They synthesized Ba4Al4SnS12 and measured bandgap, SHG, and phase matching.

Automated extraction + automated source recheckNot expert validated

Light & optics

Inspect notes, evidence & limitations

How this source was classified

Abstract reports successful synthesis and experiments.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Experimental synthesis of Ba4Al4SnS12.
Basis: Abstract
Theoretical analysis of tetrahedral-group contributions to bandgap and NLO coefficient.
Basis: Abstract

Author-reported gaps

None identified in the retained passages. This does not establish absence from the full publication.

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

Can multifunctional group engineering generalize to other chalcogenide systems?
Basis: Abstract
What structure-property trends govern the bandgap-nonlinearity tradeoff here?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Full-text reuse/access is not established by this metadata record.
Abstract-only evidence limits protocol and validation details.

Exact source scope & provenance

Record: doi:10.1039/d6dt01763k
DOI: 10.1039/d6dt01763k
Retrieved: Nov 10, 2026
Index context: Method / family search: nonlinear optical chalcogenide crystal synthesis
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 11, 2026 / Nov 11, 2026
Extraction input SHA-256: a84d7debe0eab53f71631846b7bc80ff24a58ef4b88828b1f57fa8c9e997cff8

Retained section locators

AbstractAbstractbeb4815c8fea0126d30b054cb063146d4cb0396517d69a6a1285f0771cf57ae0

Retrieved source responses

Provider response 1 ↗application/json;charset=UTF-85a6f96a2149b055edad7c1224c280d5ebea115cbab556ad88c755c546e50cf7c
Provider response 2 ↗application/json;charset=UTF-81ca17e1a4016c5f9990344ff90273ef48f7b009e13be9aba4285cfd7aa8cc289

Abstract onlyOriginal research · AI classificationSep 1, 2016

Band Gap Engineering of Wurtzite-Type Narrow Band Gap Oxide Semiconductor β-CuGaO 2 ↗

Band gap engineering of β-CuGaO2 was demonstrated through alloying, widening the gap across visible wavelengths.

Automated extraction + automated source recheckNot expert validated

Electronics

Inspect notes, evidence & limitations

How this source was classified

Abstract reports original alloying experiments.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Alloyed Cu(Ga1-xAlx)O2 and (Cu1-xLix)GaO2 by ion exchange under vacuum.
Basis: Abstract
Measured optical band gaps and valence-band XPS across alloy compositions.
Basis: Abstract

Author-reported gaps

None identified in the retained passages. This does not establish absence from the full publication.

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How do alloying changes affect device performance and stability?
Basis: Abstract
What structural limits exist beyond the reported composition ranges?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Crossref contains publisher-deposited metadata; absence of an abstract or method is not evidence that the paper lacks it.
Evidence comes only from the abstract.
Detailed protocols and uncertainties are unavailable.
No explicit author limitation is stated in the passage.

Exact source scope & provenance

Record: doi:10.1149/ma2016-02/31/2064
DOI: 10.1149/ma2016-02/31/2064
Retrieved: Nov 10, 2026
Index context: Method / family search: gallium oxide semiconductor band gap mobility
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 10, 2026 / Nov 10, 2026
Extraction input SHA-256: 1d99896960eac8ff241815285584aafe1fbc12dc893f0bb907f4cb1a0fb73ba8

Retained section locators

AbstractAbstract3d16c452e9096c0c2cb0a3ee46bf6935e35cbc40798822e4ef20f028f33c5c40

Retrieved source responses

Provider response 1 ↗application/jsonb1403511e369f0abec3fdb6585253f54e710d830354239ae776691e3dfa5a4ca

Abstract onlyOriginal research · AI classificationJul 10, 2026

Beyond Wisdom in III-V Optoelectronics: Physics-Enhanced Neural Networks Unveil the Role of Cubic Boron Arsenide. ↗

Abstract-only original research proposes a BAs-III-V platform and predicts bandgaps with PhysenNet.

Automated extraction + automated source recheckNot expert validated

Heat & transport

Inspect notes, evidence & limitations

How this source was classified

Original research methods and device designs reported.

Automated and tentative. Publication type is separate from scientific validity or peer-review status.

Methods in the retained passages

Extracted structural information from synthetic BAs crystals.
Basis: Abstract
Developed physics-enhanced deep neural networks for bandgap prediction.
Basis: Abstract

Author-reported gaps

None identified in the retained passages. This does not establish absence from the full publication.

Questions to investigate next

AI-proposed research questions, not conclusions or recommendations from the authors.

How does the workflow perform on full-text data?
Basis: Abstract
Can it generalize beyond the two demonstrated devices?
Basis: Abstract

Data & supporting resources

No verified dataset link was retained. This is not evidence that supporting data do not exist.

Limits of this reading

Only the abstract informed these notes. Unreported experimental details cannot be inferred from it.

Full-text reuse/access is not established by this metadata record.
Evidence is abstract-only.
Selected passages do not establish detailed protocols.
Reported performance is not independently verifiable here.

Exact source scope & provenance

Record: doi:10.1002/adma.202519860
DOI: 10.1002/adma.202519860
Retrieved: Nov 10, 2026
Index context: Method / family search: boron arsenide thermal conductivity measurement
Source license: Not established in this snapshot; original source terms apply.
Extraction model: gpt-5.4-mini-2026-03-17
Extraction / recheck: Nov 10, 2026 / Nov 10, 2026
Extraction input SHA-256: 4d08da251df4f39f6e99807bda532839e8a8ae8cbc9f358e571a39b68c3ef0aa

Retained section locators

AbstractAbstract0ed4d6f3365fc25a157768991ed84a2e634c1165d34f6e437d00185997ec9fbc

Retrieved source responses

Provider response 1 ↗application/json;charset=UTF-82cd86459adfb46359d3a43d96117bc7c5b4ede97bed58dc0dbb9d2ef446346e0
Provider response 2 ↗application/json;charset=UTF-8256366ca90ad4c1bec5ff1458fc3f19c8bf00c16c7a818b8c37d8fac418a5913

Collection methods, coverage & audit trail

Source type is not review status.

The extraction tentatively classifies 88 records as original research, 15 as review articles and 2 as secondary summaries. 93 remain unclassified or have no extraction. None have an expert validation recorded here.

Initial materials-methods research corpus. Automated extraction from explicitly bounded retrieved passages; not a claim of expert review or every full paper being read.

Model: gpt-5.4-mini-2026-03-17. Automated rechecks inspect retained passages again; they do not provide independent human review. Public notes do not redistribute article bodies.

Full-text status means the provider article body was retrieved; only bounded selected passages were used for extraction.
Abstract-only and metadata-only records are labeled and cannot establish unreported methods.
Automated source-type classification does not establish peer-review, correction or retraction status.
No fabricated human/expert review. AI suggestions are separate from author-reported gaps.
Paper identities retain DOI or preprint version. A preprint and later journal publication may remain separate when their relationship is not verified.

1 original seed reference remains unresolved. A missing source is not replaced with a guessed DOI.

Snapshot SHA-256 56b474d53b54bc4bf5da40efdcd573dde12c5aa102ad3fdcafe83a18fc57d601

Queries used to collect context

Energy & electrolytes: solid electrolyte ionic conductivity impedance spectroscopy (8 returned results before identity deduplication)
Heat & transport: boron arsenide thermal conductivity measurement (8 returned results before identity deduplication)
Light & optics: nonlinear optical crystal phase matching borate (8 returned results before identity deduplication)
Quantum materials: kagome superconductivity charge density wave (8 returned results before identity deduplication)
Magnetism: two dimensional van der Waals magnetism crystal (8 returned results before identity deduplication)
Electronics: gallium oxide semiconductor band gap mobility (8 returned results before identity deduplication)
Structures & mechanics: MAX phase mechanical properties synthesis (8 returned results before identity deduplication)
Catalysis: molybdenum carbide electrocatalysis synthesis (8 returned results before identity deduplication)
Research methods: machine learning interatomic potential materials molecular dynamics (8 returned results before identity deduplication)
Research methods: density functional theory materials database uncertainty (8 returned results before identity deduplication)
Energy & electrolytes: sulfide solid electrolyte synthesis lithium conductivity (8 returned results before identity deduplication)
Heat & transport: thermoelectric materials thermal transport experiment (8 returned results before identity deduplication)
Making materials: materials synthesis reproducibility processing structure properties (8 returned results before identity deduplication)
Research methods: autonomous materials discovery experimental validation (8 returned results before identity deduplication)
Light & optics: nonlinear optical chalcogenide crystal synthesis (8 returned results before identity deduplication)
Quantum materials: topological materials Weyl semimetal experiment (8 returned results before identity deduplication)
Research methods: materials informatics open data benchmarks (8 returned results before identity deduplication)
Energy & electrolytes: lithium argyrodite impedance spectroscopy conductivity (8 returned results before identity deduplication)
Making materials: ceramic sintering grain boundary conductivity (8 returned results before identity deduplication)
Structures & mechanics: high entropy alloy mechanical properties experiment (8 returned results before identity deduplication)