Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design | Paper Citation Record

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This paper cites \ De Breuck , author H.-C.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design \ De Breuck , author H.-C

Reference 1

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Observation e6ee99c5-7607-47c7-99ca-e8b2d5850841 · outbound

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

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Crystal Diffusion Variational Autoencoder for Periodic Material Generation

Reference 2

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Observation bd781d9f-9223-4e3a-ad5b-0a3f8e842878 · outbound

This paper cites Crystal struc- ture prediction by joint equivariant diffusion.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Crystal struc- ture prediction by joint equivariant diffusion

Reference 3

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This paper cites A generative model for inor- ganic materials design.Nature, 639(8055):624–632,.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design A generative model for inor- ganic materials design.Nature, 639(8055):624–632,

Reference 4

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This paper cites FlowMM: Generating Materials with Riemannian Flow Matching.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design FlowMM: Generating Materials with Riemannian Flow Matching

Reference 5

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This paper cites CrystalFlow: a flow-based gen- erative model for crystalline materials.Nature Communications, 16(1):9267, 2025.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design CrystalFlow: a flow-based gen- erative model for crystalline materials.Nature Communications, 16(1):9267, 2025

Reference 6

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This paper cites All-atom Diffusion Transformers: Unified generative modelling of molecules and materials.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design All-atom Diffusion Transformers: Unified generative modelling of molecules and materials

Reference 7

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Observation c5cc44c0-f72b-4595-8dab-c7c777bd2494 · outbound

This paper cites Training Diffusion Models with Reinforcement Learning.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Training Diffusion Models with Reinforcement Learning

Reference 8

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This paper cites DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models

Reference 9

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This paper cites Accelerating inverse materials design using generative diffusion models with reinforcement learning.arXiv preprint arXiv:2511.03112.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Accelerating inverse materials design using generative diffusion models with reinforcement learning.arXiv preprint arXiv:2511.03112

Reference 10

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This paper cites arXiv preprint arXiv:2504.02367 (2025).

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design arXiv preprint arXiv:2504.02367 (2025)

Reference 11

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Observation 0efaf8bb-8e68-418c-84fd-9c8cce3157a1 · outbound

This paper cites Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Understanding Reinforcement Learning-Based Fine-Tuning of Diffusion Models: A Tutorial and Review

Reference 12

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Observation a13c0c30-89a3-4672-a394-40a4675d4b90 · outbound

This paper cites Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Latent Conservative Objective Models for Data-Driven Crystal Structure Prediction

Reference 13

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This paper cites A mobile robotic chemis t.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design A mobile robotic chemis t

Reference 14

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This paper cites Bayesian optimization.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Bayesian optimization

Reference 15

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This paper cites Cluster Computing 6(3), 215–226 (Jul 2003), https://doi.org/10.1023/A: 1023588520138.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Cluster Computing 6(3), 215–226 (Jul 2003), https://doi.org/10.1023/A: 1023588520138

Reference 16

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Observation 6cbdb506-291e-416b-b1c6-f8e66c3c1fc9 · outbound

This paper cites Advances in Neural Information Processing Systems (NeurIPS) , pages=.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Advances in Neural Information Processing Systems (NeurIPS) , pages=

Reference 17

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This paper cites The Journal of Chemical Physics , volume =.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design The Journal of Chemical Physics , volume =

Reference 18

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This paper cites Orb: A Fast, Scalable Neural Network Potential.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Orb: A Fast, Scalable Neural Network Potential

Reference 19

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Observation cb68efd0-cedb-4aed-a4d4-2dbc5aaa628b · outbound

This paper cites Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 20

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This paper cites 2005 , publisher =.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design 2005 , publisher =

Reference 21

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This paper cites Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm.npj Com- putational Materials, 6(1):138, 2020.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm.npj Com- putational Materials, 6(1):138, 2020

Reference 22

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This paper cites Springer, New York, 2nd edition, 2002.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Springer, New York, 2nd edition, 2002

Reference 23

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Observation c4f79a25-950e-4507-ac51-87451915a504 · outbound

This paper cites Matminer: An open source toolkit for materi- als data mining.Computational Materials Science, 152:60–69, 2018.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Matminer: An open source toolkit for materi- als data mining.Computational Materials Science, 152:60–69, 2018

Reference 24

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This paper cites data5, 1–8, DOI: 10.1038/sdata.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design data5, 1–8, DOI: 10.1038/sdata

Reference 25

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This paper cites APL Materials , author =.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design APL Materials , author =

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Observation 90ca694d-7a94-4330-bcfd-4b02319b6a50 · outbound

This paper cites Charting the complete elastic properties of inorganic crystalline compounds.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Charting the complete elastic properties of inorganic crystalline compounds

Reference 27

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Observation 281c2a0c-27a9-430f-95fd-605962e765c8 · outbound

This paper cites Petousis, D.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Petousis, D

Reference 28

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This paper cites On representing chemical environments.Phys- ical Review B—Condensed Matter and Materi- als Physics, 87(18):184115, 2013.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design On representing chemical environments.Phys- ical Review B—Condensed Matter and Materi- als Physics, 87(18):184115, 2013

Reference 29

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This paper cites Thermal-FIST: A package for heavy-ion collisions and hadronic equation of state.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Thermal-FIST: A package for heavy-ion collisions and hadronic equation of state

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Observation 3b8946c3-2c78-4030-a888-3e01e8e2b089 · outbound

This paper cites Nature Machine Intelligence5(9), 1031– 1041 (2023).

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Nature Machine Intelligence5(9), 1031– 1041 (2023)

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This paper cites BoTorch: A frame- work for efficient monte-carlo Bayesian optimiza- tion.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design BoTorch: A frame- work for efficient monte-carlo Bayesian optimiza- tion

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This paper cites GPyTorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design GPyTorch: Blackbox matrix-matrix Gaussian process inference with GPU acceleration

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This paper cites Multi-task Gaussian pro- cess prediction.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Multi-task Gaussian pro- cess prediction

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This paper cites Deep Gaussian processes.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Deep Gaussian processes

Reference 35

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Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Deep neural networks as point es- timates for deep Gaussian processes

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This paper cites First principles phonon calculations in materials science.Scripta Materialia, 108:1–5, 2015.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design First principles phonon calculations in materials science.Scripta Materialia, 108:1–5, 2015

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Observation 7657955a-4544-4ad3-817c-a38ee1b0fb1b · outbound

This paper cites Finite elastic strain of cubic crystals.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Finite elastic strain of cubic crystals

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This paper cites MADE: Benchmark environments for closed-loop materials discovery.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design MADE: Benchmark environments for closed-loop materials discovery

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This paper cites Gleason, Ali Ramlaoui, Andy Xu, Georgia Channing, Daniel Levy, Clémentine Fourrier, Nikita Kazeev, Chaitanya K.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Gleason, Ali Ramlaoui, Andy Xu, Georgia Channing, Daniel Levy, Clémentine Fourrier, Nikita Kazeev, Chaitanya K

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Observation c2b81fd2-d1e2-4e45-886b-adc26f273762 · outbound

This paper cites $\texttt{Spglib}$: a software library for crystal symmetry search.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design $\texttt{Spglib}$: a software library for crystal symmetry search

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Observation bc7060d4-5f21-49d5-8cec-687bd4a264d4 · outbound

This paper cites Efficient iter- ative schemes for ab initio total-energy calculations using a plane-wave basis set.Physical Review B, 54 (16):11169–11186, 1996.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Efficient iter- ative schemes for ab initio total-energy calculations using a plane-wave basis set.Physical Review B, 54 (16):11169–11186, 1996

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Observation 74e0717d-cf87-4eac-adf0-a44cb4cffc3b · outbound

This paper cites Python materials genomics (pymatgen): A robust, open- source python library for materials analysis.Com- putational Materials Science, 68:314–319, 2013.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Python materials genomics (pymatgen): A robust, open- source python library for materials analysis.Com- putational Materials Science, 68:314–319, 2013

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This paper cites Perdew, Kieron Burke, and Matthias Ernz- erhof.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Perdew, Kieron Burke, and Matthias Ernz- erhof

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This paper cites Bl¨ ochl.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Bl¨ ochl

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Observation fa8c86fb-c4ee-435d-a5b9-50e5c2eb144a · outbound

This paper cites From ultrasoft pseudopotentials to the projector augmented-wave method.Physical Review B, 59(3):1758–1775, 1999.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design From ultrasoft pseudopotentials to the projector augmented-wave method.Physical Review B, 59(3):1758–1775, 1999

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This paper cites Gregoire, and Yousung Jung.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Gregoire, and Yousung Jung

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Observation def396fd-5a8e-4a96-b6ac-9d55f8effe42 · outbound

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

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design A general-purpose machine learning framework for predicting properties of inorganic materials

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Resolution: verified exact; doi, observed 2026-06-30T00:34:04.213617Z

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Observation b00c6d44-bf17-4918-aff0-dd313ef61cd4 · outbound

This paper cites A bag- ging svm to learn from positive and unlabeled exam- ples.Pattern Recognition Letters, 37:201–209, 2014.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design A bag- ging svm to learn from positive and unlabeled exam- ples.Pattern Recognition Letters, 37:201–209, 2014

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Observation af317d37-4b44-4b95-ab52-bc58457483a5 · outbound

This paper cites Akiba, S.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Akiba, S

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Observation 5c42a485-39a5-4e6e-a9d7-c66f37b53c9f · outbound

This paper cites Predicting materi- als properties with little data using shotgun trans- fer learning.ACS Central Science, 5(10):1717–1730,.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Predicting materi- als properties with little data using shotgun trans- fer learning.ACS Central Science, 5(10):1717–1730,

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Observation 9ebb513f-7308-4830-8708-8307781700b1 · outbound

This paper cites Supplementary Fig. Sn.

Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design Supplementary Fig. Sn

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Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design upper bound

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