Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation | Paper Citation Record

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Unresolved cited work

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This paper cites A generative model for inorganic materials design.Nature, 2025.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation A generative model for inorganic materials design.Nature, 2025

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Jaakkola

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This paper cites DMFlow: Disordered materials generation by flow matching.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation DMFlow: Disordered materials generation by flow matching

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This paper cites Andersson, Abhijith S Parackal, Dong Qian, Rickard Armiento, and Fredrik Lindsten.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Andersson, Abhijith S Parackal, Dong Qian, Rickard Armiento, and Fredrik Lindsten

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This paper cites Continued challenges in high-throughput materials predictions: MatterGen predicts compounds from the training dataset.Mater.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Continued challenges in high-throughput materials predictions: MatterGen predicts compounds from the training dataset.Mater

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

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Gleason, Ali Ramlaoui, Andy Xu, Georgia Channing, Daniel Levy, Cl ´ementine Fourrier, Nikita Kazeev, Chaitanya K

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This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Gans trained by a two time-scale update rule converge to a local nash equilibrium

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This paper cites Elena, D ´avid P.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Elena, D ´avid P

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This paper cites Transport novelty dis- tance: A distributional metric for evaluating material generative models.arXiv preprint arXiv:2512.09514, 2025.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Transport novelty dis- tance: A distributional metric for evaluating material generative models.arXiv preprint arXiv:2512.09514, 2025

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This paper cites Computational Optimal Transport.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Computational Optimal Transport

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This paper cites Continuous SUN (stable, unique, and novel) metric for generative modeling of inorganic crystals.Machine Learning: Science and Technology, 7(3):035064, June 2026.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Continuous SUN (stable, unique, and novel) metric for generative modeling of inorganic crystals.Machine Learning: Science and Technology, 7(3):035064, June 2026

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This paper cites Crystal structure prediction by joint equivariant diffusion.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Crystal structure prediction by joint equivariant diffusion

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Space group constrained crystal generation

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This paper cites Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, and Zachary Ward Ulissi.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, and Zachary Ward Ulissi

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This paper cites Exploration of crystal chemical space using text-guided generative artificial intelligence.Nat Commun, 16, 2025.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Exploration of crystal chemical space using text-guided generative artificial intelligence.Nat Commun, 16, 2025

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This paper cites Guiding generative models to uncover diverse and novel crystals via reinforcement learning.arXiv preprint arXiv:2511.07158, 2025.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Guiding generative models to uncover diverse and novel crystals via reinforcement learning.arXiv preprint arXiv:2511.07158, 2025

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This paper cites Revisiting precision recall definition for genera- tive modeling.

Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Revisiting precision recall definition for genera- tive modeling

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Precision recall cover: A method for assessing generative models

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation The unreason- able effectiveness of deep features as a perceptual metric

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Mastej, and Aron Walsh

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Resolving the data ambiguity for periodic crystals

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Substitution-Based Analysis of Structural Novelty for Generative Models of Materials

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation The unification of representation learning and generative modelling, 2025

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation High-resolution image synthesis with latent diffusion models

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning.Journal of Cheminformat- ics, 17(1):90, 2025

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Geometric representation condition improves equivariant molecule generation

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Platonic representation of foundation machine learning inter- atomic potentials.arXiv preprint arXiv:2512.05349, 2025

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Elign: Equivariant diffusion model alignment from foundational machine learning force fields.arXiv preprint arXiv:2601.21985, 2026

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Unifying force pre- diction and molecular conformation generation through representation alignment

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Generative Pseudo-Force Fields for Molecular Generation

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Score-based generative modeling through stochastic differential equations

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Learning multi- scale local conditional probability models of images

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Visual autoregressive modeling: Scalable image generation via next-scale prediction

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation The optimal one dimensional periodic table: a modified pettifor chemical scale from data mining.New Journal of Physics, 18(9):093011, sep 2016

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Representation Learning with Contrastive Predictive Coding

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Jakob, Aron Walsh, Karsten Reuter, and Johannes T

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Castelli, David D

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Crystalite: A Lightweight Transformer for Efficient Crystal Modeling

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Building normalizing flows with stochastic interpolants

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Flow straight and fast: Learning to generate and transfer data with rectified flow

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Simple and ef- fective masked diffusion language models

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation E(n) equivariant graph neural networks

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation The mlip package: moment tensor potentials with mpi and active learning.Machine Learning: Science and Technology, 2(2):025002, dec 2020

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Courville

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Alaya, Aur ´elie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, L ´eo Gautheron, Nathalie T.H

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Sinkhorn distances: Lightspeed computation of optimal transport

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Expected sliced transport plans

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Multiscale strategies for computing optimal transport

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Improved precision and recall metric for assessing generative models

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Reliable fidelity and diversity metrics for generative models

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Pac-bayesian contrastive unsupervised representation learning

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation Conditional wasserstein distances with applications in bayesian ot flow matching, 2025

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Representations from Pretrained Machine-Learning Interatomic Potentials as Coarse Coordinates for Material Generation and Evaluation This is realized as a doubly stochastic matrix

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