Implicit Delta Learning of High Fidelity Neural Network Potentials | Paper Citation Record

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This paper cites Generalized Neural-Network Representation of High- Dimensional Potential-Energy Surfaces.

Implicit Delta Learning of High Fidelity Neural Network Potentials Generalized Neural-Network Representation of High- Dimensional Potential-Energy Surfaces

Reference 1

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Observation 57ccabca-4de1-4a36-a3f0-097a02f63697 · outbound

This paper cites Schoenholz, Patrick F.

Implicit Delta Learning of High Fidelity Neural Network Potentials Schoenholz, Patrick F

Reference 2

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Observation 132814dd-7ecc-499e-a977-9a8fd2d2b068 · outbound

This paper cites Finkler, Stefan Goedecker, and Jörg Behler.

Implicit Delta Learning of High Fidelity Neural Network Potentials Finkler, Stefan Goedecker, and Jörg Behler

Reference 3

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This paper cites E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials.

Implicit Delta Learning of High Fidelity Neural Network Potentials E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials

Reference 4

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Observation 01e49779-0832-45ec-a1dc-b83e52e4979a · outbound

This paper cites TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations.

Implicit Delta Learning of High Fidelity Neural Network Potentials TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations

Reference 5

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Observation 0be61aac-7f0e-4a5e-b426-b859eeb579a7 · outbound

This paper cites Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size.

Implicit Delta Learning of High Fidelity Neural Network Potentials Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size

Reference 6

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Observation 22b86c9a-1627-453c-96f1-231af9185f69 · outbound

This paper cites MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules.

Implicit Delta Learning of High Fidelity Neural Network Potentials MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules

Reference 7

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This paper cites Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations.

Implicit Delta Learning of High Fidelity Neural Network Potentials Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations

Reference 8

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Observation dd7f8472-28e7-4ef3-8af9-bc1bcc90ce04 · outbound

This paper cites Scalable bayesian uncertainty quantifi- cation for neural network potentials: Promise and pitfalls.

Implicit Delta Learning of High Fidelity Neural Network Potentials Scalable bayesian uncertainty quantifi- cation for neural network potentials: Promise and pitfalls

Reference 9

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Observation fbe39590-a692-484c-bfec-d78bd6c410b1 · outbound

This paper cites Molecular dynamics simulations with quantum mechanics/molecular mechanics and adaptive neural networks.

Implicit Delta Learning of High Fidelity Neural Network Potentials Molecular dynamics simulations with quantum mechanics/molecular mechanics and adaptive neural networks

Reference 10

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Observation 49a78ab0-63b0-4ef6-b8f4-5ce670e35c1c · outbound

This paper cites Charron, Gianni De Fabritiis, Frank Noé, and Cecilia Clementi.

Implicit Delta Learning of High Fidelity Neural Network Potentials Charron, Gianni De Fabritiis, Frank Noé, and Cecilia Clementi

Reference 11

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Observation fba0e783-fcf3-4569-8bad-666e3ed7b979 · outbound

This paper cites Deep coarse-grained potentials via relative entropy minimization.

Implicit Delta Learning of High Fidelity Neural Network Potentials Deep coarse-grained potentials via relative entropy minimization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.591858Z

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Observation 1544720c-9a51-4fd3-9ea3-b167c1a95a14 · outbound

This paper cites Learning neural network potentials from experimental data via differentiable trajectory reweighting.

Implicit Delta Learning of High Fidelity Neural Network Potentials Learning neural network potentials from experimental data via differentiable trajectory reweighting

Reference 13

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Observation bf5fa89a-c73a-4f92-a154-9cfa6d855754 · outbound

This paper cites Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators.

Implicit Delta Learning of High Fidelity Neural Network Potentials Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 14

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This paper cites Less is more: Sampling chemical space with active learning.

Implicit Delta Learning of High Fidelity Neural Network Potentials Less is more: Sampling chemical space with active learning

Reference 15

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Observation 7b260fc9-8490-4d6a-8c02-a3dc5799a0a1 · outbound

This paper cites Hyperactive learning for data-driven interatomic potentials.

Implicit Delta Learning of High Fidelity Neural Network Potentials Hyperactive learning for data-driven interatomic potentials

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.559769Z

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Observation bcf989c0-c06a-4b06-af58-924294743bd2 · outbound

This paper cites Gfn2-xtb—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions.

Implicit Delta Learning of High Fidelity Neural Network Potentials Gfn2-xtb—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.539996Z

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Observation 5a87c8dc-3195-462d-ba5d-0bc1673ad1b7 · outbound

This paper cites Dftb3: Extension of the self-consistent-charge density-functional tight-binding method (scc-dftb).

Implicit Delta Learning of High Fidelity Neural Network Potentials Dftb3: Extension of the self-consistent-charge density-functional tight-binding method (scc-dftb)

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.520973Z

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Observation 545287d6-90aa-459d-87a3-64da61e82b4d · outbound

This paper cites Optimization of parameters for semiempirical methods v: Modification of nddo approximations and application to 70 elements.

Implicit Delta Learning of High Fidelity Neural Network Potentials Optimization of parameters for semiempirical methods v: Modification of nddo approximations and application to 70 elements

Reference 19

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Observation f6b015c2-1381-4997-bfb9-8eedea1b4efc · outbound

This paper cites Big data meets quantum chemistry approximations: the δ-machine learning approach.

Implicit Delta Learning of High Fidelity Neural Network Potentials Big data meets quantum chemistry approximations: the δ-machine learning approach

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.478354Z

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Observation 78d33568-20b4-42e6-9d25-008bcef440a8 · outbound

This paper cites Multiscale quantum mechanics/molecular mechanics simulations with neural networks.

Implicit Delta Learning of High Fidelity Neural Network Potentials Multiscale quantum mechanics/molecular mechanics simulations with neural networks

Reference 21

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Observation c5b401fc-cc97-4764-94c5-ca9a7e43ba15 · outbound

This paper cites Orbnet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features.

Implicit Delta Learning of High Fidelity Neural Network Potentials Orbnet: Deep learning for quantum chemistry using symmetry-adapted atomic-orbital features

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.448610Z

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Observation 172c570b-54cb-4a70-9ae0-a2e1b83ea9fd · outbound

This paper cites Qmugs, quan- tum mechanical properties of drug-like molecules.

Implicit Delta Learning of High Fidelity Neural Network Potentials Qmugs, quan- tum mechanical properties of drug-like molecules

Reference 23

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Observation fba9092f-89a9-4cdc-8cc4-bad0214c0d1c · outbound

This paper cites SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials.

Implicit Delta Learning of High Fidelity Neural Network Potentials SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine Learning Potentials

Reference 24

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Observation 799df74f-36af-467c-919d-33f99b7e5766 · outbound

This paper cites Spice 2.0.1, April 2024.

Implicit Delta Learning of High Fidelity Neural Network Potentials Spice 2.0.1, April 2024

Reference 25

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This paper cites Robert A., and Tkatchenko Alexandre.

Implicit Delta Learning of High Fidelity Neural Network Potentials Robert A., and Tkatchenko Alexandre

Reference 26

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Observation f824234f-b9a2-4705-b9b3-0dc20c163ced · outbound

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Implicit Delta Learning of High Fidelity Neural Network Potentials Unresolved cited work

Reference 27

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Observation 6f340a85-154f-45fb-b76d-9d6cd4c3cc30 · outbound

This paper cites Machine learning in qm/mm molec- ular dynamics simulations of condensed-phase systems.

Implicit Delta Learning of High Fidelity Neural Network Potentials Machine learning in qm/mm molec- ular dynamics simulations of condensed-phase systems

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.417068Z

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Observation 1f33b18d-478b-46c7-81b3-ad1d1c26d774 · outbound

This paper cites Graph-convolutional neural networks for (qm) ml/mm molecular dynamics simulations.

Implicit Delta Learning of High Fidelity Neural Network Potentials Graph-convolutional neural networks for (qm) ml/mm molecular dynamics simulations

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.399975Z

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Observation 1d8a62d3-9307-409e-a1b1-ab9a0d9e604f · outbound

This paper cites Active learning of uniformly accurate interatomic potentials for materials simulation.

Implicit Delta Learning of High Fidelity Neural Network Potentials Active learning of uniformly accurate interatomic potentials for materials simulation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.372519Z

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Observation d21c76ae-be6b-4988-9969-a58aee7f32b6 · outbound

This paper cites On-the-fly active learning of interatomic potentials for large-scale atomistic simulations.

Implicit Delta Learning of High Fidelity Neural Network Potentials On-the-fly active learning of interatomic potentials for large-scale atomistic simulations

Reference 31

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Observation 203ad550-bc83-4a02-801a-3e129e955083 · outbound

This paper cites Smith, Benjamin T.

Implicit Delta Learning of High Fidelity Neural Network Potentials Smith, Benjamin T

Reference 32

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Observation 82a71642-7a44-4593-9ad6-6fad401671b8 · outbound

This paper cites Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning.

Implicit Delta Learning of High Fidelity Neural Network Potentials Learning Together: Towards foundational models for machine learning interatomic potentials with meta-learning

Reference 33

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:07:36.726016Z

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Observation 9296c5fe-263b-4b45-92e4-c5912a76c29e · outbound

This paper cites From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction.

Implicit Delta Learning of High Fidelity Neural Network Potentials From Molecules to Materials: Pre-training Large Generalizable Models for Atomic Property Prediction

Reference 34

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Observation e043bff5-fac1-4e99-a5f2-46d62bd6155d · outbound

This paper cites Less is more: Sampling chemical space with active learning.

Implicit Delta Learning of High Fidelity Neural Network Potentials Less is more: Sampling chemical space with active learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.338080Z

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Observation 3d15d800-a19f-4635-a932-9e48a3e813f0 · outbound

This paper cites The ani-1ccx and ani-1x data sets, coupled-cluster and density functional theory properties for molecules.

Implicit Delta Learning of High Fidelity Neural Network Potentials The ani-1ccx and ani-1x data sets, coupled-cluster and density functional theory properties for molecules

Reference 36

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Observation f0de13e5-f2e6-45a5-b1a1-17141dbe1e26 · outbound

This paper cites Ani-1: an extensible neural network potential with dft accuracy at force field computational cost.Chemical Science, 8(4):3192–3203, 2017.

Implicit Delta Learning of High Fidelity Neural Network Potentials Ani-1: an extensible neural network potential with dft accuracy at force field computational cost.Chemical Science, 8(4):3192–3203, 2017

Reference 37

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Observation 1975a789-7cd2-42b1-9313-15202f38d77f · outbound

This paper cites Quantum deep descriptor: Physically informed trans- fer learning from small molecules to polymers.

Implicit Delta Learning of High Fidelity Neural Network Potentials Quantum deep descriptor: Physically informed trans- fer learning from small molecules to polymers

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.252065Z

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Observation 9fe65195-c1ac-4f43-bb16-67434b938505 · outbound

This paper cites Transfer learning using attentions across atomic systems with graph neural networks (taag).

Implicit Delta Learning of High Fidelity Neural Network Potentials Transfer learning using attentions across atomic systems with graph neural networks (taag)

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.235570Z

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Observation bd525470-c3e2-4153-9524-184caa53d58e · outbound

This paper cites Transfer learning for chemically accurate interatomic neural network potentials.

Implicit Delta Learning of High Fidelity Neural Network Potentials Transfer learning for chemically accurate interatomic neural network potentials

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.216692Z

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Observation bdbb5f56-5b52-4b5c-a2c3-03b2d1a7c42e · outbound

This paper cites Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T).

Implicit Delta Learning of High Fidelity Neural Network Potentials Machine learning potentials from transfer learning of periodic correlated electronic structure methods: Application to liquid water with AFQMC, CCSD, and CCSD(T)

Reference 41

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Observation 4b2f3e64-ba16-422a-8dc6-c18fafa60596 · outbound

This paper cites Transfer-learned potential energy surfaces: Toward microsecond-scale molecular dynamics simulations in the gas phase at ccsd (t) quality.

Implicit Delta Learning of High Fidelity Neural Network Potentials Transfer-learned potential energy surfaces: Toward microsecond-scale molecular dynamics simulations in the gas phase at ccsd (t) quality

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.189271Z

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Observation 9c1a0efa-c011-452c-8d7e-cab5b0d39d09 · outbound

This paper cites Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting.

Implicit Delta Learning of High Fidelity Neural Network Potentials Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.162554Z

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Observation b8803245-995c-481f-83a2-c91aa50c8c43 · outbound

This paper cites Transfer Learning for Molecular Property Predictions from Small Data Sets.

Implicit Delta Learning of High Fidelity Neural Network Potentials Transfer Learning for Molecular Property Predictions from Small Data Sets

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:07:36.641765Z

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Observation f995c041-1771-4ee6-8833-aedfa00be4b6 · outbound

This paper cites Synthetic pre-training for neural- network interatomic potentials.

Implicit Delta Learning of High Fidelity Neural Network Potentials Synthetic pre-training for neural- network interatomic potentials

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.139714Z

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Observation 6c7bb5fb-87b0-4750-ac7d-e2856ee3d281 · outbound

This paper cites Pubchemqc b3lyp/6-31g//pm6 data set: The electronic structures of 86 million molecules using b3lyp/6-31g calculations.

Implicit Delta Learning of High Fidelity Neural Network Potentials Pubchemqc b3lyp/6-31g*//pm6 data set: The electronic structures of 86 million molecules using b3lyp/6-31g* calculations

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.113803Z

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Observation 3c46d6ab-d4ae-4c6b-8fce-d0371ae41107 · outbound

This paper cites Multixc-qm9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods.

Implicit Delta Learning of High Fidelity Neural Network Potentials Multixc-qm9: Large dataset of molecular and reaction energies from multi-level quantum chemical methods

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.092305Z

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Observation df70cde4-3634-418c-a6e1-d32b21acdf25 · outbound

This paper cites Open force field bespokefit: Automating bespoke torsion parametrization at scale., 2022.

Implicit Delta Learning of High Fidelity Neural Network Potentials Open force field bespokefit: Automating bespoke torsion parametrization at scale., 2022

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.072936Z

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Observation 34972aca-f5c7-4ff4-998d-c3f0f9ebdba5 · outbound

This paper cites an unresolved cited work.

Implicit Delta Learning of High Fidelity Neural Network Potentials Unresolved cited work

Reference 49

Resolution: verified exact; doi, observed 2026-08-11T20:07:37.055108Z

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Observation 83fdde50-2ddb-406f-8d52-25ad5b62519e · outbound

This paper cites Moussa Jonathan E.

Implicit Delta Learning of High Fidelity Neural Network Potentials Moussa Jonathan E

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:37.020559Z

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Observation 31b3f07b-d3e1-4d06-ba2d-d48da183ed19 · outbound

This paper cites Extended tight-binding quan- tum chemistry methods.

Implicit Delta Learning of High Fidelity Neural Network Potentials Extended tight-binding quan- tum chemistry methods

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T20:07:35.997051Z

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Observation b9aeb979-103e-4964-b87b-6b944ffa2e36 · outbound

This paper cites Neural scaling of deep chemical models.

Implicit Delta Learning of High Fidelity Neural Network Potentials Neural scaling of deep chemical models

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:36.995113Z

Source-reported events for the cited work

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Observation 152c423c-de61-41c1-ac29-38634183bbe1 · outbound

This paper cites Deep Learning Scaling is Predictable, Empirically.

Implicit Delta Learning of High Fidelity Neural Network Potentials Deep Learning Scaling is Predictable, Empirically

Reference 53

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This paper cites Scaling Laws for Neural Language Models.

Implicit Delta Learning of High Fidelity Neural Network Potentials Scaling Laws for Neural Language Models

Reference 54

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Implicit Delta Learning of High Fidelity Neural Network Potentials Scaling vision transform- ers

Reference 55

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This paper cites Explaining Neural Scaling Laws.

Implicit Delta Learning of High Fidelity Neural Network Potentials Explaining Neural Scaling Laws

Reference 56

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Observation c7100569-be57-4c98-abf4-486800a22d98 · outbound

This paper cites Scaling Laws for Transfer.

Implicit Delta Learning of High Fidelity Neural Network Potentials Scaling Laws for Transfer

Reference 57

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This paper cites Kingma and Jimmy Lei Ba.

Implicit Delta Learning of High Fidelity Neural Network Potentials Kingma and Jimmy Lei Ba

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:36.945286Z

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Observation 078630e4-0227-4e82-8904-1e02129317e9 · outbound

This paper cites Searching for Activation Functions.

Implicit Delta Learning of High Fidelity Neural Network Potentials Searching for Activation Functions

Reference 59

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Implicit Delta Learning of High Fidelity Neural Network Potentials Unresolved cited work

Reference 60

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Observation f3e902df-90e4-4579-b4d9-4c207650e8e3 · outbound

This paper cites On representing chemical environments.

Implicit Delta Learning of High Fidelity Neural Network Potentials On representing chemical environments

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:07:36.889118Z

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Observation 76f5baed-dc62-473c-9985-a6fb23ad302b · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Implicit Delta Learning of High Fidelity Neural Network Potentials UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 62

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Observation 105a901a-d21e-42b5-8ce9-91c5c8951900 · outbound

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Implicit Delta Learning of High Fidelity Neural Network Potentials Unresolved cited work

Reference 63

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Observation 0efcd2ba-4ae2-4060-b588-4c42e96aa548 · outbound

This paper cites doi:10.1016/j.cpc.2019.106949.

Implicit Delta Learning of High Fidelity Neural Network Potentials doi:10.1016/j.cpc.2019.106949

Reference 2020

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