Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T11:48:43.932356Z
Paper Citation Record · LEDGER
As of 20 August 2026, this Paper Citation Record lists 94 of 94 outbound references and 6 inbound Pith citation observations for arXiv:2502.02582.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-09T11:48:43.932356Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T08:49:42.337169Z
94 of 94 outbound references displayed
External citation measurements
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Observation d14fe712-ee1e-4298-a92f-4079caa8e220 · outbound
Open Materials Generation with Stochastic Interpolants Boeri, R
Reference 1
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Open Materials Generation with Stochastic Interpolants Gludovatz, A
Reference 2
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Open Materials Generation with Stochastic Interpolants Gludovatz, A
Reference 3
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 4
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Open Materials Generation with Stochastic Interpolants Strmcnik, P
Reference 5
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Observation ec29ca92-2ec4-44c5-9ad1-e840807f0d7d · outbound
Open Materials Generation with Stochastic Interpolants Nakaya and S
Reference 6
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Reference 7
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 8
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Open Materials Generation with Stochastic Interpolants Cantor, Progress in Materials Science 120, 100754 (2021)
Reference 9
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Observation a18fa397-a027-4e89-bfbf-41ac4d73b5af · outbound
Open Materials Generation with Stochastic Interpolants Potyrailo, K
Reference 10
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 11
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 12
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Observation cfc7ad49-601f-4bc4-aaf3-8cc2bf21b48b · outbound
Open Materials Generation with Stochastic Interpolants Curtarolo, G
Reference 13
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Observation 49cd65ca-2118-474a-ae76-60b25d427310 · outbound
Open Materials Generation with Stochastic Interpolants Bergerhoff, R
Reference 14
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 15
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Observation cac14693-c1ae-4558-bbff-ba16fdd4ea40 · outbound
Open Materials Generation with Stochastic Interpolants Blaiszik, K
Reference 16
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 17
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Open Materials Generation with Stochastic Interpolants Fuemmeler, G
Reference 18
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Open Materials Generation with Stochastic Interpolants Batzner, A
Reference 19
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Open Materials Generation with Stochastic Interpolants Batatia, D
Reference 20
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Open Materials Generation with Stochastic Interpolants Chen and S
Reference 21
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Observation 7555b1fe-2d83-412a-8eb8-f1ee530af13c · outbound
Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 22
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 23
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 24
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 25
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 26
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 27
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Open Materials Generation with Stochastic Interpolants Merchant, S
Reference 28
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Observation 9fb7cdf4-3d52-4958-b2bf-9ddec1a65711 · outbound
Open Materials Generation with Stochastic Interpolants Crystal Diffusion Variational Autoencoder for Periodic Material Generation
Reference 29
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 30
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 31
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Observation f7bda97e-d1df-450b-93c4-d2e32d9a0dae · outbound
Open Materials Generation with Stochastic Interpolants FlowMM: Generating Materials with Riemannian Flow Matching
Reference 32
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Observation 59a632b3-860c-4793-acb5-08514c7bde35 · outbound
Open Materials Generation with Stochastic Interpolants Stochastic Interpolants: A Unifying Framework for Flows and Diffusions
Reference 33
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Observation 6f149c2a-fb64-42ce-b0eb-f6f0467e91ff · outbound
Open Materials Generation with Stochastic Interpolants Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Reference 34
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Observation 6789a61e-fd43-403d-909c-8779010ab5f4 · outbound
Open Materials Generation with Stochastic Interpolants Score-Based Generative Modeling through Stochastic Differential Equations
Reference 35
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Observation 558c5821-467a-4b31-88ad-ed46f032ada4 · outbound
Open Materials Generation with Stochastic Interpolants Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
Reference 36
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Observation 0bb6c303-7a7b-4f08-8c89-1f473d71eb28 · outbound
Open Materials Generation with Stochastic Interpolants DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
Reference 37
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Observation 8eac0e3d-de7c-4a05-a3c5-4f679c48c30e · outbound
Open Materials Generation with Stochastic Interpolants Rectified Flow: A Marginal Preserving Approach to Optimal Transport
Reference 38
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Open Materials Generation with Stochastic Interpolants Flow Matching for Generative Modeling
Reference 39
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Open Materials Generation with Stochastic Interpolants Building Normalizing Flows with Stochastic Interpolants
Reference 40
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Open Materials Generation with Stochastic Interpolants Improving and generalizing flow-based generative models with minibatch optimal transport
Reference 41
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Open Materials Generation with Stochastic Interpolants Flow Matching on General Geometries
Reference 42
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Open Materials Generation with Stochastic Interpolants Discrete Flow Matching
Reference 43
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Open Materials Generation with Stochastic Interpolants Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design
Reference 44
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 45
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 46
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Open Materials Generation with Stochastic Interpolants AI4Science, A
Reference 47
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 48
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Open Materials Generation with Stochastic Interpolants WyCryst: Wyckoff Inorganic Crystal Generator Framework
Reference 49
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Open Materials Generation with Stochastic Interpolants Kazeev, R
Reference 50
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Open Materials Generation with Stochastic Interpolants Space Group Constrained Crystal Generation
Reference 51
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 52
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 53
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Open Materials Generation with Stochastic Interpolants Full workflow details are given in Appendix D 4
Reference 54
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Open Materials Generation with Stochastic Interpolants MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
Reference 55
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 56
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 57
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 58
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Open Materials Generation with Stochastic Interpolants Large-scale machine-learning-assisted exploration of the whole materials space
Reference 59
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Open Materials Generation with Stochastic Interpolants Schmidt, H.-C
Reference 60
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 61
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Open Materials Generation with Stochastic Interpolants Sriram, B
Reference 62
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Open Materials Generation with Stochastic Interpolants Fast protein backbone generation with SE(3) flow matching
Reference 63
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Open Materials Generation with Stochastic Interpolants SE(3)-Stochastic Flow Matching for Protein Backbone Generation
Reference 64
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Open Materials Generation with Stochastic Interpolants Stochastic interpolants with data-dependent couplings
Reference 65
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Open Materials Generation with Stochastic Interpolants Denoising Diffusion Probabilistic Models
Reference 66
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Open Materials Generation with Stochastic Interpolants Optimizing Noise Schedules of Generative Models in High Dimensionss
Reference 67
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 68
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Open Materials Generation with Stochastic Interpolants Song and S
Reference 69
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Open Materials Generation with Stochastic Interpolants Tune: A Research Platform for Distributed Model Selection and Training
Reference 70
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Open Materials Generation with Stochastic Interpolants Bergstra, D
Reference 71
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 72
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Open Materials Generation with Stochastic Interpolants $\texttt{Spglib}$: a software library for crystal symmetry search
Reference 73
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Open Materials Generation with Stochastic Interpolants Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
Reference 74
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Open Materials Generation with Stochastic Interpolants Depending on the specific task, there are several stochastic interpolants at once
Reference 75
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Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 76
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Open Materials Generation with Stochastic Interpolants [33], the loss function can become unstable around t = 0 and t = 1 for certain choices of γ(t)
Reference 77
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Open Materials Generation with Stochastic Interpolants Albergo et al
Reference 78
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Open Materials Generation with Stochastic Interpolants We do not attempt to generalize stochastic interpolants (SIs) to arbitrary manifolds as in Riemannian flow matching [42]
Reference 79
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Observation 78f9d9ff-cf45-4242-ba63-60c6fed35394 · outbound
Open Materials Generation with Stochastic Interpolants (The geodesic is the same as the linear interpolant wrapped back into the box.) 15 We perform this procedure for all choices of interpolants
Reference 80
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Open Materials Generation with Stochastic Interpolants As discussed, a parameterized neural network pθ 1|t(x1|xt) is learned, which attempts to predict the final sequence from the sequence at time t
Reference 81
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Observation 97aa2ad2-07ac-46c5-8872-41379e3fe036 · outbound
Open Materials Generation with Stochastic Interpolants For instance, Miller et al
Reference 82
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Observation 6df5f37b-6afe-4c87-b487-d43b6ec452b8 · outbound
Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 83
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Observation 5900b808-9c50-4b6f-a5ef-a635cf105bd0 · outbound
Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 84
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Observation 1a983475-0ae6-4e70-9807-48960d4fcf7a · outbound
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Reference 85
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Observation 0a17d0d5-7876-4db0-91df-64807317777d · outbound
Open Materials Generation with Stochastic Interpolants The notable differences between OMatG-Linear models and FlowMM are as follows: (1) Discrete flow matching on species for OMatG vs
Reference 86
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Observation 7110599e-2bf2-419a-9bb3-fefe647c8215 · outbound
Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 87
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Observation 7e7b2569-515c-4652-91f2-ae52b060be50 · outbound
Open Materials Generation with Stochastic Interpolants (C7) For the CSP task, the last line is left out
Reference 88
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Observation add2ff6a-bef1-4ed5-82e5-172aa00d08a9 · outbound
Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 89
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Observation 2e14eae9-0a2f-4fea-9d9c-b07ae78104a8 · outbound
Open Materials Generation with Stochastic Interpolants We show in Fig
Reference 90
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Observation eb0cdfb1-7b46-428e-aa1c-80250e01fabb · outbound
Open Materials Generation with Stochastic Interpolants The compositional validity is defined according to the SMACT software package [45]
Reference 91
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 212eae5b-7f58-42a3-9c92-d171f420586c · outbound
Open Materials Generation with Stochastic Interpolants For structural matching, we use CrystalNN, and for compositional matching, we use Magpie fingerprints
Reference 92
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 45cbb8fc-6b32-42ef-9f77-1add76f99733 · outbound
Open Materials Generation with Stochastic Interpolants Rates TABLE XII
Reference 93
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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 61fd6751-2084-4621-9bab-82461d26ecda · outbound
Open Materials Generation with Stochastic Interpolants Unresolved cited work
Reference 94
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 2c809e4e-0339-4cb3-89e2-ef4fb9c172a0 · inbound
FlowMol3: Flow Matching for 3D De Novo Small-Molecule Generation Open Materials Generation with Stochastic Interpolants
Reference 18
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 384673dc-0301-4cf1-bd31-e4f7e2b7cd38 · inbound
Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling Open Materials Generation with Stochastic Interpolants
Reference 5
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 6d8e33cb-1ea4-4220-b16d-1288bd62790a · inbound
Multimodal Crystal Flow: Any-to-Any Modality Generation for Unified Crystal Modeling Open Materials Generation with Stochastic Interpolants
Reference 5
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b38fd527-d66e-4d5f-8c79-6049df7afa9a · inbound
VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials Open Materials Generation with Stochastic Interpolants
Reference 24
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation fd4513c0-2eb5-4f72-ad20-04a78108e30d · inbound
Atomistic Language Models Understand and Generate Materials Open Materials Generation with Stochastic Interpolants
Reference 45
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 1b8241c3-317f-404b-ba95-1b6f3b6c8762 · inbound
SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery Open Materials Generation with Stochastic Interpolants
Reference 9
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.