AI-driven inverse design of materials: Past, present and future | Paper Citation Record

Observation f4ff0fc3-32f2-4c35-b6b3-ea02bb411999 · outbound

This paper cites Inverse design in search of materials with target functionalities,.

AI-driven inverse design of materials: Past, present and future Inverse design in search of materials with target functionalities,

Reference 1

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Observation 7052b64b-270c-4f74-9a03-ffac29abba1f · outbound

This paper cites Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review,.

AI-driven inverse design of materials: Past, present and future Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review,

Reference 2

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Observation b67a5875-f2a5-4021-b3bc-d0000b2475b4 · outbound

This paper cites Inverse design of materials by machine learning,.

AI-driven inverse design of materials: Past, present and future Inverse design of materials by machine learning,

Reference 3

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Observation 1b529710-e555-4142-9608-4486d15a4c0e · outbound

This paper cites Generative deep learning for the inverse design of materials.

AI-driven inverse design of materials: Past, present and future Generative deep learning for the inverse design of materials

Reference 4

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Observation ebee87aa-fec3-44a2-926c-efb1a0fb606c · outbound

This paper cites Physics-informed machine learning methods for inverse design of multi-phase materials with targeted me- chanical properties,.

AI-driven inverse design of materials: Past, present and future Physics-informed machine learning methods for inverse design of multi-phase materials with targeted me- chanical properties,

Reference 5

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Observation 5c0f7485-5e0d-473d-975f-74f58d5df29d · outbound

This paper cites Generative models for inverse design of inorganic solid materials,.

AI-driven inverse design of materials: Past, present and future Generative models for inverse design of inorganic solid materials,

Reference 6

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Observation 55a0c11d-37d4-4e84-b1bb-f9efbea0514d · outbound

This paper cites Inverse design of 3d cellular materials with physics-guided machine learning,.

AI-driven inverse design of materials: Past, present and future Inverse design of 3d cellular materials with physics-guided machine learning,

Reference 7

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Observation 95476a03-fbd1-4771-ba41-86b8df57d45e · outbound

This paper cites Further experiments with liquid helium,.

AI-driven inverse design of materials: Past, present and future Further experiments with liquid helium,

Reference 8

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Observation de2b3652-2e4a-48d9-bdb4-313a13cb0f12 · outbound

This paper cites Superconductivity at 39 k in magnesium diboride,.

AI-driven inverse design of materials: Past, present and future Superconductivity at 39 k in magnesium diboride,

Reference 9

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Observation e9aa7bb9-8d68-423e-9f5f-de59cca0b1b4 · outbound

This paper cites Inverse design of porous ma- terials using artificial neural networks,.

AI-driven inverse design of materials: Past, present and future Inverse design of porous ma- terials using artificial neural networks,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.810519Z

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Observation f15f8de6-54d5-4869-9ed7-973115ab18ef · outbound

This paper cites The dirac equation and the prediction of antimatter,.

AI-driven inverse design of materials: Past, present and future The dirac equation and the prediction of antimatter,

Reference 11

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Observation c1607adf-3a24-4468-8c3a-7081f73fb8a6 · outbound

This paper cites The apparent existence of easily de- flectable positives,.

AI-driven inverse design of materials: Past, present and future The apparent existence of easily de- flectable positives,

Reference 12

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Observation 97952884-d6ae-47d2-8b73-b30624a8bd87 · outbound

This paper cites Theory of superconductivity,.

AI-driven inverse design of materials: Past, present and future Theory of superconductivity,

Reference 13

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Observation df55483f-1236-4448-8240-ea7adb3bba6b · outbound

This paper cites Theory of the meissner effect in superconduc- tors,.

AI-driven inverse design of materials: Past, present and future Theory of the meissner effect in superconduc- tors,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.828221Z

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Observation 9c49ad4b-a0cc-4adc-8457-8e352561c160 · outbound

This paper cites Bound electron pairs in a degenerate fermi gas,.

AI-driven inverse design of materials: Past, present and future Bound electron pairs in a degenerate fermi gas,

Reference 15

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Observation b16492ff-24ea-4f7d-a007-eed8876e5374 · outbound

This paper cites Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,.

AI-driven inverse design of materials: Past, present and future Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set,

Reference 16

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Observation b4a62a69-a847-4163-8e4a-0bc353d3dd56 · outbound

This paper cites Generalized gradient approximation made simple,.

AI-driven inverse design of materials: Past, present and future Generalized gradient approximation made simple,

Reference 17

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Observation efed26ad-bf0a-4716-8701-1be97d79e7a1 · outbound

This paper cites Projector augmented-wave method,.

AI-driven inverse design of materials: Past, present and future Projector augmented-wave method,

Reference 18

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Observation c540f933-c53f-408b-8418-8dd8dfa71e13 · outbound

This paper cites Electric field effect in atomically thin carbon films,.

AI-driven inverse design of materials: Past, present and future Electric field effect in atomically thin carbon films,

Reference 19

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Observation 436a8153-88f3-4ae1-8219-fcd50ad37da6 · outbound

This paper cites Theoretical models incorporating electron correlation,.

AI-driven inverse design of materials: Past, present and future Theoretical models incorporating electron correlation,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.859475Z

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Observation ac0cefef-3436-4558-ab38-f6b6287d7e45 · outbound

This paper cites Has generative artificial in- telligence solved inverse materials design?.

AI-driven inverse design of materials: Past, present and future Has generative artificial in- telligence solved inverse materials design?

Reference 21

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Observation 3e9f2abf-bd19-4d88-adbb-7668875dc1b5 · outbound

This paper cites Attention is all you need,.

AI-driven inverse design of materials: Past, present and future Attention is all you need,

Reference 22

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Observation caef050f-ed6d-4d22-8d63-8aaac7f303a7 · outbound

This paper cites Crystal Diffusion Variational Autoencoder for Periodic Material Generation.

AI-driven inverse design of materials: Past, present and future Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.871545Z

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Observation d0cdda63-a803-4254-813f-0b66e8292ab0 · outbound

This paper cites Crystal structure prediction by joint equivariant diffusion,.

AI-driven inverse design of materials: Past, present and future Crystal structure prediction by joint equivariant diffusion,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.876062Z

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Observation 5d7be0a0-0420-423b-9a77-0fb1a70b1a90 · outbound

This paper cites MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials.

AI-driven inverse design of materials: Past, present and future MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.880220Z

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Observation 1f67ea1b-624b-48e5-8f26-15c04ccb9363 · outbound

This paper cites Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design,.

AI-driven inverse design of materials: Past, present and future Atomgpt: Atomistic generative pretrained transformer for forward and inverse materials design,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.884610Z

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Observation a2549705-f67f-4833-968a-e7c21b599ce2 · outbound

This paper cites Denoising diffusion probabilistic models for generative alloy design,.

AI-driven inverse design of materials: Past, present and future Denoising diffusion probabilistic models for generative alloy design,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.888438Z

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Observation b52994eb-b493-443c-955d-09fbd114851b · outbound

This paper cites Microstructure reconstruction of 2d/3d random materials via diffusion-based deep generative models,.

AI-driven inverse design of materials: Past, present and future Microstructure reconstruction of 2d/3d random materials via diffusion-based deep generative models,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.892346Z

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Observation a923f7ce-4bad-4976-829f-f25afed75d4a · outbound

This paper cites dZiner: Rational Inverse Design of Materials with AI Agents.

AI-driven inverse design of materials: Past, present and future dZiner: Rational Inverse Design of Materials with AI Agents

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:08.900615Z

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Observation f138d473-3a9d-4f2d-a663-0c5baf9e87f6 · outbound

This paper cites Scaling deep learning for materials discovery,.

AI-driven inverse design of materials: Past, present and future Scaling deep learning for materials discovery,

Reference 31

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Observation 92faac5e-fcb3-426c-8e0f-f5c5a7d63b50 · outbound

This paper cites Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.

AI-driven inverse design of materials: Past, present and future Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 32

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Observation 7c2026fd-8dc2-4caa-9c36-89de98b273b1 · outbound

This paper cites Inverse design of photonic and phononic topological insulators: a review,.

AI-driven inverse design of materials: Past, present and future Inverse design of photonic and phononic topological insulators: a review,

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Observation 62d6747b-07bc-41dd-adc2-7da78673301d · outbound

This paper cites Machine learning design for high- entropy alloys: models and algorithms,.

AI-driven inverse design of materials: Past, present and future Machine learning design for high- entropy alloys: models and algorithms,

Reference 34

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Observation e311df05-75b1-41a6-8f9b-a08ad02fe52c · outbound

This paper cites A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications.

AI-driven inverse design of materials: Past, present and future A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications

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Observation f6805eb3-0c43-45f4-b354-c97287c11e6c · outbound

This paper cites Graph neural networks for materials science and chemistry,.

AI-driven inverse design of materials: Past, present and future Graph neural networks for materials science and chemistry,

Reference 36

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Observation 25e80b42-76c9-4973-80a8-450cdfa43a12 · outbound

This paper cites Novel technologies and configurations of superconducting magnets for mri,.

AI-driven inverse design of materials: Past, present and future Novel technologies and configurations of superconducting magnets for mri,

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Observation 4979d788-202c-49c9-844f-2c4556bbabf5 · outbound

This paper cites High temperature su- perconductors for fusion magnets,.

AI-driven inverse design of materials: Past, present and future High temperature su- perconductors for fusion magnets,

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Observation 5c5c3fd3-f289-4994-8bf8-e997006c4eaf · outbound

This paper cites Superconducting qubit to optical photon transduction,.

AI-driven inverse design of materials: Past, present and future Superconducting qubit to optical photon transduction,

Reference 39

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Observation 6aede6d9-c172-4f1b-b74f-5009220b8f66 · outbound

This paper cites Building logical qubits in a superconducting quantum computing system,.

AI-driven inverse design of materials: Past, present and future Building logical qubits in a superconducting quantum computing system,

Reference 40

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Observation b220aa6a-5e50-404b-a4e5-0018e50e85f8 · outbound

This paper cites Quantum sensing,.

AI-driven inverse design of materials: Past, present and future Quantum sensing,

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Observation cfa72901-c6cd-4f62-8214-4139b0bab0bf · outbound

This paper cites The resistance of pure mercury at helium temperatures,.

AI-driven inverse design of materials: Past, present and future The resistance of pure mercury at helium temperatures,

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Observation 55f27425-e39f-4186-8eee-d228e513b6a2 · outbound

This paper cites Ein neuer effekt bei eintritt der supraleitfähigkeit,.

AI-driven inverse design of materials: Past, present and future Ein neuer effekt bei eintritt der supraleitfähigkeit,

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Observation a363f9b3-0b6f-412d-afb8-83ca9900b01d · outbound

This paper cites Superconductivity in nb–ge films above 22 k,.

AI-driven inverse design of materials: Past, present and future Superconductivity in nb–ge films above 22 k,

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Observation 35bf9761-54cb-48bd-8b37-5fecba0b65c9 · outbound

This paper cites Possible high t c super- conductivity in the ba- la- cu- o system,.

AI-driven inverse design of materials: Past, present and future Possible high t c super- conductivity in the ba- la- cu- o system,

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Observation 30612d59-7297-4f32-948b-2df0a7212b21 · outbound

This paper cites Superconductivity at 93 k in a new mixed-phase y-ba-cu-o compound system at ambient pressure,.

AI-driven inverse design of materials: Past, present and future Superconductivity at 93 k in a new mixed-phase y-ba-cu-o compound system at ambient pressure,

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Observation 3095d327-6ec6-48ab-8152-298baea2ab6c · outbound

This paper cites Superconductivity at 55 k in iron-based f-doped layered quaternary compound sm[o 1−xfx] feas,.

AI-driven inverse design of materials: Past, present and future Superconductivity at 55 k in iron-based f-doped layered quaternary compound sm[o 1−xfx] feas,

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Observation f2492b5a-f5f0-42e7-ac35-4abcd064a75d · outbound

This paper cites Record high 36 k transition temperature to the superconducting state of elemental scandium at a pressure of 260 gpa,.

AI-driven inverse design of materials: Past, present and future Record high 36 k transition temperature to the superconducting state of elemental scandium at a pressure of 260 gpa,

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Observation c79a17f5-31d3-4bec-92b0-8026cefd58f7 · outbound

This paper cites Signatures of superconductivity near 80 K in a nickelate under high pressure,.

AI-driven inverse design of materials: Past, present and future Signatures of superconductivity near 80 K in a nickelate under high pressure,

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Observation cb3ced3f-554d-4485-ba6a-c7833ef13029 · outbound

This paper cites Conventional superconductivity at 203 kelvin at high pressures in the sulfur hydride system,.

AI-driven inverse design of materials: Past, present and future Conventional superconductivity at 203 kelvin at high pressures in the sulfur hydride system,

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Observation 83976ba3-2e9d-49d2-8027-162b865f59d9 · outbound

This paper cites Machine learning modeling of superconducting critical temperature,.

AI-driven inverse design of materials: Past, present and future Machine learning modeling of superconducting critical temperature,

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Observation bdf4cd09-281f-49e9-babf-37382473c594 · outbound

This paper cites A general-purpose machine learning framework for predict- ing properties of inorganic materials,.

AI-driven inverse design of materials: Past, present and future A general-purpose machine learning framework for predict- ing properties of inorganic materials,

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Observation 23c45b7a-a92d-428c-8870-403cee02129e · outbound

This paper cites A deep learning approach to search for superconductors from electronic bands.

AI-driven inverse design of materials: Past, present and future A deep learning approach to search for superconductors from electronic bands

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Observation ae6eee45-be1b-45cc-a27d-45420604d91b · outbound

This paper cites Superband: an Electronic-band and Fermi surface structure database of superconductors.

AI-driven inverse design of materials: Past, present and future Superband: an Electronic-band and Fermi surface structure database of superconductors

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Observation d96157a6-225a-491d-9ad7-a2d87c3c7c16 · outbound

This paper cites The graph neural network model,.

AI-driven inverse design of materials: Past, present and future The graph neural network model,

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Observation e0f72bdc-8c1c-4a1c-8b25-a2f656301f36 · outbound

This paper cites A comprehensive survey on graph neural networks,.

AI-driven inverse design of materials: Past, present and future A comprehensive survey on graph neural networks,

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Observation 38843e50-7e9b-4df2-bbc9-57ec71e5f9dc · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

AI-driven inverse design of materials: Past, present and future Semi-Supervised Classification with Graph Convolutional Networks

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Observation c13fcfa5-d487-4cc8-93da-4f9cc13acbdb · outbound

This paper cites Inductive rep- resentation learning on large graphs,.

AI-driven inverse design of materials: Past, present and future Inductive rep- resentation learning on large graphs,

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Observation 017043aa-5bc8-4474-b3ba-8d54fdc7c740 · outbound

This paper cites Graph Attention Networks.

AI-driven inverse design of materials: Past, present and future Graph Attention Networks

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Observation 15fbf205-a8ae-4644-b77b-719730b8bc47 · outbound

This paper cites Convo- lutional neural networks on graphs with fast localized spec- tral filtering,.

AI-driven inverse design of materials: Past, present and future Convo- lutional neural networks on graphs with fast localized spec- tral filtering,

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Observation 75ca29ec-d6fc-4b29-80fe-45f2b5c4ab57 · outbound

This paper cites Designing high-tc super- conductors with bcs-inspired screening, density functional theory, and deep-learning,.

AI-driven inverse design of materials: Past, present and future Designing high-tc super- conductors with bcs-inspired screening, density functional theory, and deep-learning,

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Observation abf632e8-da47-4f59-8355-0ff43e9a5885 · outbound

This paper cites Atomistic line graph neural network for improved materials property predictions,.

AI-driven inverse design of materials: Past, present and future Atomistic line graph neural network for improved materials property predictions,

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Observation 973d56c7-51f8-4393-be3b-f502641f685a · outbound

This paper cites Data-driven design of high pressure hydride superconductors using dft and deep learn- ing,.

AI-driven inverse design of materials: Past, present and future Data-driven design of high pressure hydride superconductors using dft and deep learn- ing,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.064600Z

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Observation bbace50e-9524-4e12-a65a-0aec3cc415fa · outbound

This paper cites Machine learning model for predicting the critical transition temperature of hydride superconductors,.

AI-driven inverse design of materials: Past, present and future Machine learning model for predicting the critical transition temperature of hydride superconductors,

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.068872Z

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Observation f5a979d4-14e7-4fb0-a8b0-ae8f2f942e58 · outbound

This paper cites Machine learning accelerated discovery of superconduct- ing two-dimensional janus transition metal sulfhydrates,.

AI-driven inverse design of materials: Past, present and future Machine learning accelerated discovery of superconduct- ing two-dimensional janus transition metal sulfhydrates,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.073119Z

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Observation db864cd6-c3d0-4ff0-a71a-d5ce06c51fed · outbound

This paper cites Searching materials space for hydride superconductors at ambient pressure,.

AI-driven inverse design of materials: Past, present and future Searching materials space for hydride superconductors at ambient pressure,

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.077495Z

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Observation e4ae2216-d766-4c4b-8cee-8a4bb62074d2 · outbound

This paper cites 3dsc- a dataset of superconductors including crystal structures,.

AI-driven inverse design of materials: Past, present and future 3dsc- a dataset of superconductors including crystal structures,

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.081739Z

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Observation e8b6e8c6-fd85-4eaa-8120-101c1bfe6d81 · outbound

This paper cites High-resolution image synthesis with latent diffusion models,.

AI-driven inverse design of materials: Past, present and future High-resolution image synthesis with latent diffusion models,

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.086634Z

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Observation a67d0c0b-d660-4663-ae04-0bcc651ed6fb · outbound

This paper cites Denoising diffusion proba- bilistic models,.

AI-driven inverse design of materials: Past, present and future Denoising diffusion proba- bilistic models,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.090817Z

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Observation c1df12e9-f601-41de-a183-183dc8625c3e · outbound

This paper cites Generative modeling by estimating gradients of the data distribution,.

AI-driven inverse design of materials: Past, present and future Generative modeling by estimating gradients of the data distribution,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.094840Z

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Observation 3008dc76-5406-4e29-9e8c-360955e2847d · outbound

This paper cites Accurate structure prediction of biomolecular interactions with alphafold 3,.

AI-driven inverse design of materials: Past, present and future Accurate structure prediction of biomolecular interactions with alphafold 3,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.099520Z

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Observation d076d199-be55-4694-9405-946e6dfde354 · outbound

This paper cites InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors.

AI-driven inverse design of materials: Past, present and future InvDesFlow: An AI-driven materials inverse design workflow to explore possible high-temperature superconductors

Reference 72

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Observation 72fbcfa0-2c89-46d9-9744-5cc1b30bdbfc · outbound

This paper cites Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,.

AI-driven inverse design of materials: Past, present and future Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties,

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.108174Z

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Observation b2a74c30-446a-4f15-b7d6-a4894bd6d209 · outbound

This paper cites AI-accelerated Discovery of Altermagnetic Materials.

AI-driven inverse design of materials: Past, present and future AI-accelerated Discovery of Altermagnetic Materials

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.112660Z

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Observation 8031fabc-2a6e-4f73-9f2b-2811d9e01200 · outbound

This paper cites Graph networks as a universal machine learning framework for molecules and crystals,.

AI-driven inverse design of materials: Past, present and future Graph networks as a universal machine learning framework for molecules and crystals,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.116805Z

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Observation 7d3006d1-5169-4bc7-8472-4c667782732b · outbound

This paper cites Machine learning guided discovery of superconducting calcium borocarbides,.

AI-driven inverse design of materials: Past, present and future Machine learning guided discovery of superconducting calcium borocarbides,

Reference 76

Resolution: verified exact; doi, observed 2026-08-12T20:41:10.527861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 62ee4980-a643-4856-abfa-35b86ec55ec0 · outbound

This paper cites DPA-2: a large atomic model as a multi-task learner.

AI-driven inverse design of materials: Past, present and future DPA-2: a large atomic model as a multi-task learner

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.125201Z

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Observation 33849d40-1670-4ae7-8cbe-93a629cf3d9a · outbound

This paper cites Inverse design of next-generation superconductors using data-driven deep generative models,.

AI-driven inverse design of materials: Past, present and future Inverse design of next-generation superconductors using data-driven deep generative models,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.129622Z

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Observation 18579762-3723-469f-a556-f6d64804a0a1 · outbound

This paper cites Virtual node graph neural network for full phonon prediction,.

AI-driven inverse design of materials: Past, present and future Virtual node graph neural network for full phonon prediction,

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.134124Z

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Observation c20bf300-c1f1-4691-9b19-957dac6d58ec · outbound

This paper cites Accelerating the calculation of electron–phonon coupling strength with machine learning,.

AI-driven inverse design of materials: Past, present and future Accelerating the calculation of electron–phonon coupling strength with machine learning,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.138190Z

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Observation 14e46cb4-edb1-4433-af53-b3bee763d3f0 · outbound

This paper cites Scaling of transition temperature and cuo2 plane buckling in a high-temperature superconductor,.

AI-driven inverse design of materials: Past, present and future Scaling of transition temperature and cuo2 plane buckling in a high-temperature superconductor,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.142211Z

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Observation b102d9f5-fcf5-429c-adf2-18de1465a3e0 · outbound

This paper cites Relationship between crystal structure and superconduc- tivity in iron-based superconductors,.

AI-driven inverse design of materials: Past, present and future Relationship between crystal structure and superconduc- tivity in iron-based superconductors,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.146031Z

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Observation 57b7f1de-9122-4788-ad6d-b1f0bbfbc04c · outbound

This paper cites Anion height dependence of tc for the fe-based supercon- ductor,.

AI-driven inverse design of materials: Past, present and future Anion height dependence of tc for the fe-based supercon- ductor,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.150061Z

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Observation 2b62fb54-8088-428a-9e6e-c75824397a8c · outbound

This paper cites Influence of apical oxygen on the extent of in-plane exchange interaction in cuprate superconductors,.

AI-driven inverse design of materials: Past, present and future Influence of apical oxygen on the extent of in-plane exchange interaction in cuprate superconductors,

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.154126Z

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Observation 948b605e-c919-4848-bfc1-4a6755843bfa · outbound

This paper cites Bond sensitive graph neural networks for predicting high temperature superconductors,.

AI-driven inverse design of materials: Past, present and future Bond sensitive graph neural networks for predicting high temperature superconductors,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.158152Z

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Observation 6940d9ef-ea9e-41b9-b5be-a00f04f18db0 · outbound

This paper cites Closed-loop superconducting materials discovery,.

AI-driven inverse design of materials: Past, present and future Closed-loop superconducting materials discovery,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.162091Z

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Observation f6cab444-db99-40e0-a9be-39c10cfacfa5 · outbound

This paper cites an unresolved cited work.

AI-driven inverse design of materials: Past, present and future Unresolved cited work

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.171254Z

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Observation 3d32a4bb-03e4-497e-8907-c3123d85fefa · outbound

This paper cites Goldman, Handbook of modern ferromagnetic materials.

AI-driven inverse design of materials: Past, present and future Goldman, Handbook of modern ferromagnetic materials

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.175004Z

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Observation ecbcf7d3-8884-462b-ab12-0028ac16e094 · outbound

This paper cites Anti- ferromagnetic spintronics,.

AI-driven inverse design of materials: Past, present and future Anti- ferromagnetic spintronics,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.178858Z

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Observation 500ce9c5-9281-46de-81b0-0112266becb7 · outbound

This paper cites Emerging research landscape of altermagnetism,.

AI-driven inverse design of materials: Past, present and future Emerging research landscape of altermagnetism,

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.183083Z

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Observation df0c3975-9ec2-46f7-bb0d-3d2718becb63 · outbound

This paper cites Beyond conventional ferromagnetism and antiferromagnetism: A phase with nonrelativistic spin and crystal rotation symmetry,.

AI-driven inverse design of materials: Past, present and future Beyond conventional ferromagnetism and antiferromagnetism: A phase with nonrelativistic spin and crystal rotation symmetry,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.187115Z

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Observation 6aa8c782-439a-44b3-ae90-5ae5ca3687eb · outbound

This paper cites Editorial: Altermagnetism—a new punch line of fundamental magnetism,.

AI-driven inverse design of materials: Past, present and future Editorial: Altermagnetism—a new punch line of fundamental magnetism,

Reference 92

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Observation 8f9873dd-7e11-4d0d-b243-f9ba12188f3e · outbound

This paper cites Momentum- dependent spin splitting by collinear antiferromagnetic ordering,.

AI-driven inverse design of materials: Past, present and future Momentum- dependent spin splitting by collinear antiferromagnetic ordering,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.194999Z

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Observation 0c84065b-e36e-4c20-adee-febdda695eb4 · outbound

This paper cites Crystal time-reversal symmetry breaking and spontaneous Hall effect in collinear antiferromagnets,.

AI-driven inverse design of materials: Past, present and future Crystal time-reversal symmetry breaking and spontaneous Hall effect in collinear antiferromagnets,

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.199238Z

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Observation 7f0c8bca-6f8c-42c2-84b1-3e2751a5a4bd · outbound

This paper cites Giant momentum-dependent spin splitting in centrosymmetric low- Z antiferromagnets,.

AI-driven inverse design of materials: Past, present and future Giant momentum-dependent spin splitting in centrosymmetric low- Z antiferromagnets,

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.203238Z

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Observation e1c53e77-f457-492b-bdd0-65bcd4523982 · outbound

This paper cites Prediction of unconventional magnetism in doped FeSb 2,.

AI-driven inverse design of materials: Past, present and future Prediction of unconventional magnetism in doped FeSb 2,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.207238Z

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Observation e1a68ec5-8c98-45e8-8533-5a2b257ae2e5 · outbound

This paper cites Altermagnets and beyond: Nodal magnetically-ordered phases.

AI-driven inverse design of materials: Past, present and future Altermagnets and beyond: Nodal magnetically-ordered phases

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.211501Z

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Unavailable: canonical work link unavailable.

Observation b792646f-c594-4954-9a50-7588ee797391 · outbound

This paper cites Topological correspondence between magnetic space group representations and subdimensions,.

AI-driven inverse design of materials: Past, present and future Topological correspondence between magnetic space group representations and subdimensions,

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Resolution: verified exact; doi, observed 2026-08-12T20:41:10.390285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 30d93565-60da-4730-a31f-b839130d55f2 · outbound

This paper cites Hubert and R.

AI-driven inverse design of materials: Past, present and future Hubert and R

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.219882Z

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Observation 1d4ebef3-436f-4ff6-9958-c278a4449739 · outbound

This paper cites Giant and Tunneling Magne- toresistance in Unconventional Collinear Antiferromagnets with Nonrelativistic Spin-Momentum Coupling,.

AI-driven inverse design of materials: Past, present and future Giant and Tunneling Magne- toresistance in Unconventional Collinear Antiferromagnets with Nonrelativistic Spin-Momentum Coupling,

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.224016Z

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Observation d2e21a36-e765-4939-a6da-4f2b114f267b · outbound

This paper cites Topological superconductivity in two-dimensional altermagnetic metals,.

AI-driven inverse design of materials: Past, present and future Topological superconductivity in two-dimensional altermagnetic metals,

Reference 101

Resolution: unresolved; no resolver link, observed 2026-08-12T20:41:09.228296Z

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