Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning | Paper Citation Record

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Unresolved cited work

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Uhrin, S

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Mathew, J

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Ganose, J

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Kirklin, J

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Curtarolo, W

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Curtarolo, W

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Schmidt, N

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Schmidt, H.-C

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Schmidt, T

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Talirz, S

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Hohenberg and W

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Kohn and L

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning AI-Driven Expansion and Application of the Alexandria Database

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Zhang, D

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Kingsbury, A

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Malosso, F

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Bosoni, L

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Prandini, A

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Stevanovi´ c, S

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Friedrich, D

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Friedrich and S

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Hautier, S

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Interpretable machine learning to understand the performance of semi local density functionals for materials thermochemistry

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Priya and N

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Woods-Robinson, D

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Mueller, G

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Kirklin, J

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Aykol, S

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Warford, F

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Smooth, exact rotational symmetrization for deep learning on point clouds

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Pushing the limits of unconstrained machine-learned interatomic potentials

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Riebesell, R

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Chorna, D

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Breiman, Machine Learning45, 5 (2001)

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning de Miranda Nascimento, F

Reference 80

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Reference 83

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Rzyman, Z

Reference 85

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning (CRC Press, 2007)

Reference 86

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Reference 87

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning Kresse and J

Reference 88

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Reference 89

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Reference 91

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning In contrast to these constant cutoffs, the MC3D follows the SSSP [39] protocol where a set of recommended cutoffs for each element were established

Reference 92

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Correcting DFT formation energies towards experimental accuracy using foundational MLIPs and latent-feature delta-learning calc” to calculate formation energies are “Pure DFT

Reference 93

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