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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability

As of 19 August 2026, this Paper Citation Record lists 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2607.22573.

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2607.22573 v1

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Outbound references

Observation 014402f0-a5a5-4533-a328-f367f2c8ea5f · outbound

This paper cites Crystal diffusion variational autoencoder for periodic material generation, 2022.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Crystal diffusion variational autoencoder for periodic material generation, 2022

Reference 1

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Observation 824e90e5-c3f9-415b-8119-054fe5d9e4bc · outbound

This paper cites Crystal structure prediction by joint equivariant diffusion, 2023.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Crystal structure prediction by joint equivariant diffusion, 2023

Reference 2

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This paper cites Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin D.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin D

Reference 3

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This paper cites A generative model for inorganic materials design.Nature, 639:624–632, 2025.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability A generative model for inorganic materials design.Nature, 639:624–632, 2025

Reference 4

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

Reference 5

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

Reference 6

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This paper cites Saal, Scott Kirklin, Muratahan Aykol, Bryce Meredig, and C.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Saal, Scott Kirklin, Muratahan Aykol, Bryce Meredig, and C

Reference 7

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This paper cites Saal, Bryce Meredig, Alex Thompson, Jeff W.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Saal, Bryce Meredig, Alex Thompson, Jeff W

Reference 8

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This paper cites Garrity, Andrew C.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Garrity, Andrew C

Reference 9

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This paper cites Nomad: The fair concept for big data-driven materials science.MRS Bulletin, 43(9):676–682, 2018.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Nomad: The fair concept for big data-driven materials science.MRS Bulletin, 43(9):676–682, 2018

Reference 10

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This paper cites Yakutovich, Valeria Granata, Fer- nando Gargiulo, Marco Borelli, Martin Uhrin, Sebastiaan P.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Yakutovich, Valeria Granata, Fer- nando Gargiulo, Marco Borelli, Martin Uhrin, Sebastiaan P

Reference 11

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Observation 0eaf4e48-79b3-4007-9ced-f1c007089078 · outbound

This paper cites Chevrier, Kristin A.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Chevrier, Kristin A

Reference 12

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This paper cites Groves, Bjork Hammer, Cory Hargus, Eric D.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Groves, Bjork Hammer, Cory Hargus, Eric D

Reference 13

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

Reference 15

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This paper cites Aiida: automated interactive infrastructure and database for computational science.Computational Materials Science, 111:218–230, 2016.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Aiida: automated interactive infrastructure and database for computational science.Computational Materials Science, 111:218–230, 2016

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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This paper cites Plata, Pinku Nath, Demet Usanmaz, Jesus Carrete, Cormac Toher, Maarten de Jong, Mark Asta, Marco Fornari, Marco Buongiorno Nardelli, and Stefano Curtarolo.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Plata, Pinku Nath, Demet Usanmaz, Jesus Carrete, Cormac Toher, Maarten de Jong, Mark Asta, Marco Fornari, Marco Buongiorno Nardelli, and Stefano Curtarolo

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This paper cites Finding un- precedentedly low-thermal-conductivity half-heusler semiconductors via high-throughput materi- als modeling.Physical Review X, 4:011019, 2014.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Finding un- precedentedly low-thermal-conductivity half-heusler semiconductors via high-throughput materi- als modeling.Physical Review X, 4:011019, 2014

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This paper cites Castelli, Andrea Cepellotti, Giovanni Pizzi, and Nicola Marzari.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Castelli, Andrea Cepellotti, Giovanni Pizzi, and Nicola Marzari

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Observation ec470594-b1c0-45d4-bff6-1ae4ee286df2 · outbound

This paper cites Katcho, and Natalio Mingo.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Katcho, and Natalio Mingo

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This paper cites Anharmonic force constants extracted from first-principles molecular dynamics: applications to heat transfer simulations.Journal of the Physical Society of Japan, 83:074702, 2014.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Anharmonic force constants extracted from first-principles molecular dynamics: applications to heat transfer simulations.Journal of the Physical Society of Japan, 83:074702, 2014

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Abrikosov, and Sergei I

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Katsnelson, and Sven P

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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This paper cites An in- vertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning.Nature Communications, 14:7027, 2023.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability An in- vertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning.Nature Communications, 14:7027, 2023

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This paper cites Exploration of crystal chemical space using text-guided generative artificial intelligence.Nature Communications, 16:4379, 2025.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Exploration of crystal chemical space using text-guided generative artificial intelligence.Nature Communications, 16:4379, 2025

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability First principles phonon calculations in materials science.Scripta Materialia, 108:1–5, 2015

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability First-principles phonon calculations with phonopy and phono3py.Journal of the Physical Society of Japan, 92:012001, 2023

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Mattersim: A deep learning atomistic model across elements, temperatures and pressures, 2024

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Phononbench: A large-scale phonon- based benchmark for dynamical stability in crystal generation, 2025

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Band structure diagram paths based on crystallography.Computational Materials Science, 128:140–184, 2017

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Spglib: a software library for crystal symmetry search.Science and Technology of Advanced Materials: Methods, 4(1):2384822, 2024

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Grossman

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Graph networks as a universal machine learning framework for molecules and crystals.Chemistry of Materials, 31(9):3564–3572, 2019

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Atomistic line graph neural network for improved materials property predictions.npj Computational Materials, 7:185, 2021

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Observation d78ccd5c-01dc-4f73-a791-9f56f22b4177 · outbound

This paper cites Benchmarking mate- rials property prediction methods: the matbench test set and automatminer reference algorithm.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Benchmarking mate- rials property prediction methods: the matbench test set and automatminer reference algorithm

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Observation 945473e8-4507-4280-8a45-dbb4dc50551e · outbound

This paper cites an unresolved cited work.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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Observation 8ea1502e-4e37-47fb-be61-d61ef1de7368 · outbound

This paper cites Lawrence Zitnick, and Zachary Ulissi.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Lawrence Zitnick, and Zachary Ulissi

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This paper cites A general-purpose machine learning framework for predicting properties of inorganic materials.npj Computational Materials, 2:16028, 2016.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability A general-purpose machine learning framework for predicting properties of inorganic materials.npj Computational Materials, 2:16028, 2016

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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Observation 27853e02-dfd3-4e01-beb5-0cdd8d35f40f · outbound

This paper cites Schutt, Huziel E.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Schutt, Huziel E

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Observation 9ee551ba-e456-41e9-a0fd-10ce8c22ec2c · outbound

This paper cites A universal graph deep learning interatomic potential for the periodic table.Nature Computational Science, 2:718–728, 2022.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability A universal graph deep learning interatomic potential for the periodic table.Nature Computational Science, 2:718–728, 2022

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Observation db9b2a9a-f2ff-440f-abf4-104601660bc7 · outbound

This paper cites Bartel, and Gerbrand Ceder.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Bartel, and Gerbrand Ceder

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Observation 46634981-3fd4-4083-be76-47384b6c5555 · outbound

This paper cites Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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This paper cites Elena, David P.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Elena, David P

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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Observation e4571574-2665-47f1-8243-c9b47667356d · outbound

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PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Unresolved cited work

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Observation 6dfa009c-0f42-4cbb-abb4-d62d47c69f10 · outbound

This paper cites For label-threshold studies, users should keep the original completed label unchanged and store any threshold-dependent relabeling in a new derived column.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability For label-threshold studies, users should keep the original completed label unchanged and store any threshold-dependent relabeling in a new derived column

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Observation d75b69d4-ff08-43da-a2a5-89ce22a5ac28 · outbound

This paper cites Chemical complexity is measured by the number of distinct elements in the parsed formula.

PhononBench-MP40: a spectrum-resolved benchmark dataset for phonon stability Chemical complexity is measured by the number of distinct elements in the parsed formula

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