Paper Citation Record · LEDGER

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials

As of 15 August 2026, this Paper Citation Record lists 86 of 86 outbound references and 4 inbound Pith citation observations for arXiv:2507.14302.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.14302 v1

Coverage vector

measured 86 of 86 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:14:04.857514Z

measured 90 of 90 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:49:41.948805Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-10T12:15:01.137692Z

Reference resolution

86 of 86 outbound references displayed

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verified fuzzy64
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02690dcc-9d5c-4144-9182-de04c1ca4d5c · outbound

This paper cites Combining machine learning and computational chemistry for predictive insights into chemical systems,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Combining machine learning and computational chemistry for predictive insights into chemical systems,

Reference 1

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Observation da3886ca-6280-472b-b039-182e014e1eef · outbound

This paper cites Machine learning force fields,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Machine learning force fields,

Reference 2

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Observation 886388b4-66f8-4194-b694-9e978d6ab4b8 · outbound

This paper cites Learning intermolecular forces at liquid–vapor interfaces,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Learning intermolecular forces at liquid–vapor interfaces,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:51.557467Z

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Observation e98dee59-ac6e-4c43-bebd-9c2303af83ec · outbound

This paper cites Incorporating long-range physics in atomic-scale machine learning,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Incorporating long-range physics in atomic-scale machine learning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:51.704078Z

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Observation fec1a74e-0439-4706-ace2-063ec3d5f5d0 · outbound

This paper cites Physics-inspired equivariant descriptors of nonbonded interactions,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Physics-inspired equivariant descriptors of nonbonded interactions,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:51.889186Z

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Observation 2ce0bd18-f19e-4c02-8411-af003a11c8f7 · outbound

This paper cites A deep potential model with long-range electrostatic interactions,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials A deep potential model with long-range electrostatic interactions,

Reference 6

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Observation ae86c640-2c39-4de9-bd6a-e9c81cc06962 · outbound

This paper cites Electrostatic interactions in atomistic and machine-learned potentials for polar materials.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Electrostatic interactions in atomistic and machine-learned potentials for polar materials

Reference 7

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Observation 4c252dec-4b16-49bf-abde-e2cda9642589 · outbound

This paper cites Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges,

Reference 8

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Observation 0e5ce8d0-e561-4441-99bb-b5d2169b8d7e · outbound

This paper cites A fourth-generation high- dimensional neural network potential with accurate electrostatics including non-local charge transfer,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials A fourth-generation high- dimensional neural network potential with accurate electrostatics including non-local charge transfer,

Reference 9

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Observation ce6407be-4cbe-40d6-ba0b-d0f112bc8250 · outbound

This paper cites Self-consistent determination of long-range electrostatics in neural network potentials,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Self-consistent determination of long-range electrostatics in neural network potentials,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:16.600338Z

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Observation 24677251-3d7c-4893-b3a7-0ca105dfd2d2 · outbound

This paper cites Discovering a transferable charge assignment model using machine learning,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Discovering a transferable charge assignment model using machine learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:16.345842Z

Source-reported events for the cited work

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Observation 9fc12a52-08c1-4905-941e-527e26c011b5 · outbound

This paper cites A predictive machine learning force-field framework for liquid electrolyte development,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials A predictive machine learning force-field framework for liquid electrolyte development,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:16.091929Z

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Observation 8c18abd1-52fe-42a9-a52a-6e6cd242f819 · outbound

This paper cites Incorporating long-range electrostatics in neural network potentials via variational charge equilibration from shortsighted ingredients,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Incorporating long-range electrostatics in neural network potentials via variational charge equilibration from shortsighted ingredients,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:15.825441Z

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Observation d38cabfc-0183-4a0f-b47c-21a57da03606 · outbound

This paper cites Capturing long-range interaction with reciprocal space neural network.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Capturing long-range interaction with reciprocal space neural network

Reference 14

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Observation 7d962923-fb39-4dfb-addf-ebf89ceb1feb · outbound

This paper cites Ewald-based long-range message passing for molecular graphs,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Ewald-based long-range message passing for molecular graphs,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:15.600913Z

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Observation 11a8e887-3ab9-4723-b0f8-87cbd331f6de · outbound

This paper cites Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Force-field-enhanced neural network interactions: from local equivariant embedding to atom-in-molecule properties and long-range effects,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:15.415842Z

Source-reported events for the cited work

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Observation d8e4e0d5-a667-4a2b-bcb6-bed726c0f2a0 · outbound

This paper cites Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:15.209309Z

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Observation 55e57eef-7726-4cb9-bd41-924989ce6cf5 · outbound

This paper cites Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Extending the RANGE of Graph Neural Networks: Relaying Attention Nodes for Global Encoding

Reference 18

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Observation 9d529a38-82bf-4bd3-a8a3-9c15ced0f917 · outbound

This paper cites Charge-constrained atomic cluster expansion,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Charge-constrained atomic cluster expansion,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.996930Z

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Observation b03f3354-88b2-4e0d-9f63-bd5e951cdacd · outbound

This paper cites Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Aimnet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.852715Z

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Observation 3969ee2f-7c07-45f6-b3df-3797622b680c · outbound

This paper cites Molecular simulations with a pretrained neural network and universal pairwise force fields,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Molecular simulations with a pretrained neural network and universal pairwise force fields,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.668998Z

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Observation d8f53b71-e973-47e2-9434-7a701bbc1a5c · outbound

This paper cites Density-based long-range electrostatic descriptors for machine learning force fields,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Density-based long-range electrostatic descriptors for machine learning force fields,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.484710Z

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Observation f005c5b0-edfc-40cb-9343-fd1fc5ae7eb6 · outbound

This paper cites A foundation model for accurate atomistic simulations in drug design,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials A foundation model for accurate atomistic simulations in drug design,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.403576Z

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Observation 5d7676d8-d162-4257-85dd-eb0011aec8af · outbound

This paper cites Fast and flexible long-range models for atomistic machine learning,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Fast and flexible long-range models for atomistic machine learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.317444Z

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Observation cda29447-6fdc-411a-b3f5-cffa8d10cb87 · outbound

This paper cites Latent ewald summation for machine learning of long-range interactions,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Latent ewald summation for machine learning of long-range interactions,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.244128Z

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Observation f697515d-0c43-4937-ba37-31d0556a8ea6 · outbound

This paper cites Learning charges and long-range interactions from energies and forces.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Learning charges and long-range interactions from energies and forces

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:14:06.337480Z

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Observation 65647b1d-91ca-4072-82bc-fd1ced5a15c2 · outbound

This paper cites Machine learning interatomic potential can infer electrical response.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Machine learning interatomic potential can infer electrical response

Reference 27

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Observation 48dc191d-78b5-4765-8ec4-c7bb831eaf97 · outbound

This paper cites Generalized neural- network representation of high-dimensional potential- energy surfaces,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Generalized neural- network representation of high-dimensional potential- energy surfaces,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.166096Z

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Observation cb1e335a-1a51-4d4f-9a79-ed93cfc44a50 · outbound

This paper cites Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.104771Z

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Observation 4c7fe8e0-5322-48e8-82f3-35f56dd3cdf2 · outbound

This paper cites The FES was computed from reweighting.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials The FES was computed from reweighting

Reference 30

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Observation 62a09745-889c-415f-87e5-b3b36c7e3efc · outbound

This paper cites Moment tensor potentials: A class of systematically improvable interatomic potentials,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Moment tensor potentials: A class of systematically improvable interatomic potentials,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:14.033106Z

Source-reported events for the cited work

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Observation 1a510bd1-177a-4110-8234-0a5c08362282 · outbound

This paper cites Atomic cluster expansion for accurate and transferable interatomic potentials,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Atomic cluster expansion for accurate and transferable interatomic potentials,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.950227Z

Source-reported events for the cited work

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Observation 9d1dfeb6-a06d-4277-a283-503d17a5817a · outbound

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

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials E (3)-equivariant graph neural networks for data- efficient and accurate interatomic potentials,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.867082Z

Source-reported events for the cited work

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Observation e111455e-c73c-46be-be87-2cd10469af5d · outbound

This paper cites Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Mace: Higher order equivariant message passing neural networks for fast and accurate force fields,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.768224Z

Source-reported events for the cited work

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Observation a9038303-3cd5-4a93-aab0-6d6d68e76800 · outbound

This paper cites Cartesian atomic cluster expansion for machine learning interatomic potentials,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Cartesian atomic cluster expansion for machine learning interatomic potentials,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.696994Z

Source-reported events for the cited work

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Observation ab361a2b-107c-4e9d-8e33-86172a7b0ab2 · outbound

This paper cites The importance of being scalable: Improving the speed and accuracy of neural network interatomic potentials across chemical domains,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials The importance of being scalable: Improving the speed and accuracy of neural network interatomic potentials across chemical domains,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.609106Z

Source-reported events for the cited work

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Observation b1df27e3-e431-40b8-b8a6-14de1c62fbe3 · outbound

This paper cites Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Materials Graph Library (MatGL), an open-source graph deep learning library for materials science and chemistry

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:14:06.125181Z

Source-reported events for the cited work

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Observation 785095c3-0cd8-4bed-a906-89159cc9557b · outbound

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

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:56.569049Z

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Observation 846abfeb-0cac-4256-a165-8bdd00ba6d48 · outbound

This paper cites Smooth, exact rotational symmetrization for deep learning on point clouds,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Smooth, exact rotational symmetrization for deep learning on point clouds,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.495087Z

Source-reported events for the cited work

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Observation a30127b2-02ae-4fe7-9b6c-22b893b28f77 · outbound

This paper cites Torchmd-net 2.0: Fast neural network potentials for molecular simulations,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Torchmd-net 2.0: Fast neural network potentials for molecular simulations,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.402956Z

Source-reported events for the cited work

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Observation 7a36ebb3-17cb-4fb1-ab45-1525864e8c45 · outbound

This paper cites Newtonnet: A newtonian message passing network for deep learning of interatomic potentials and forces,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Newtonnet: A newtonian message passing network for deep learning of interatomic potentials and forces,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.317400Z

Source-reported events for the cited work

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Observation df6595fe-8316-4e7a-b369-bb0c174b7527 · outbound

This paper cites Equiformer: Equivariant graph attention transformer for 3d atomistic graphs,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Equiformer: Equivariant graph attention transformer for 3d atomistic graphs,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.230695Z

Source-reported events for the cited work

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Observation 785f1e9c-a77b-487a-923c-8ccd91f43a8c · outbound

This paper cites Spice, a dataset of drug-like molecules and peptides for training machine learning potentials,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Spice, a dataset of drug-like molecules and peptides for training machine learning potentials,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.116565Z

Source-reported events for the cited work

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Observation e1776b33-d0a0-4acd-886c-a6f163200cac · outbound

This paper cites Mace-off: Short- range transferable machine learning force fields for organic molecules,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Mace-off: Short- range transferable machine learning force fields for organic molecules,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:13.037962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cd9dd717-36dc-4688-bebb-539a46bd04a1 · outbound

This paper cites Deepmd-kit: A deep learning package for many- body potential energy representation and molecular dynamics,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Deepmd-kit: A deep learning package for many- body potential energy representation and molecular dynamics,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:12.934463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bc9f8ebd-6079-4380-818e-bfb06c079288 · outbound

This paper cites Schnet: A continuous-filter convolutional neural network for modeling quantum interactions,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Schnet: A continuous-filter convolutional neural network for modeling quantum interactions,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:12.819215Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation eaa9baad-a7a4-48f2-b2fc-77eeaf4d35e4 · outbound

This paper cites Dynamical matrices, born effective charges, dielectric permittivity tensors, and interatomic force constants from density-functional perturbation theory,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Dynamical matrices, born effective charges, dielectric permittivity tensors, and interatomic force constants from density-functional perturbation theory,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:57.866290Z

Source-reported events for the cited work

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Observation e68de911-4e4b-4711-b402-97b5dec02136 · outbound

This paper cites A universal graph deep learning interatomic potential for the periodic table,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials A universal graph deep learning interatomic potential for the periodic table,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:12.670095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a9c89471-7d12-4beb-aceb-ba84e55a29c0 · outbound

This paper cites Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Chgnet as a pretrained universal neural network potential for charge-informed atomistic modelling,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:12.512219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4c6097a2-29b6-44d9-9c88-11066daa6629 · outbound

This paper cites Deconstructing classical water models at interfaces and in bulk,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Deconstructing classical water models at interfaces and in bulk,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:12.334764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 045104ba-53b0-42fa-811a-5d38073e6677 · outbound

This paper cites Short solvent model for ion correlations and hydrophobic association,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Short solvent model for ion correlations and hydrophobic association,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:12.164438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1c827f6c-f6f6-4b8e-b485-53f01d4ce840 · outbound

This paper cites Derivative learning of tensorial quantities—predicting finite temperature infrared spectra from first principles,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Derivative learning of tensorial quantities—predicting finite temperature infrared spectra from first principles,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:12.014123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d67226c6-7452-4d70-bdf6-222f0bf3ccc5 · outbound

This paper cites Intrinsic backbone preferences are fully present in blocked amino acids,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Intrinsic backbone preferences are fully present in blocked amino acids,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:11.853734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cf2432f8-c355-4f46-9bd5-a16400735680 · outbound

This paper cites Foundation Models for Atomistic Simulation of Chemistry and Materials.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Foundation Models for Atomistic Simulation of Chemistry and Materials

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-06T16:13:58.900110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd00ea29-62c0-48f5-9653-ea71c92ce548 · outbound

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

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Qmugs, quantum mechanical properties of drug-like molecules,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:11.724851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 28a13026-1d62-4dc8-a65c-575f1c4d5a5c · outbound

This paper cites The structure of water around the compressibility minimum,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials The structure of water around the compressibility minimum,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:11.497162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b35426d9-ff32-4aa9-a1bf-06a7e32327b8 · outbound

This paper cites an unresolved cited work.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Unresolved cited work

Reference 57

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:14:11.334262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a6d1e179-469f-4312-a271-e6c5e636c677 · outbound

This paper cites Quantum dynamics and spectroscopy of ab initio liquid water: The interplay of nuclear and electronic quantum effects,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Quantum dynamics and spectroscopy of ab initio liquid water: The interplay of nuclear and electronic quantum effects,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:11.122941Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d0976479-7676-4228-8868-02488f45f859 · outbound

This paper cites Quantum dynamics using path integral coarse-graining,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Quantum dynamics using path integral coarse-graining,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:10.923369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e8d0b3eb-b2f2-4cc9-9767-bba3365ebad0 · outbound

This paper cites an unresolved cited work.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Unresolved cited work

Reference 60

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:14:10.806264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 134a4355-9f8a-4224-850c-e2729d39e696 · outbound

This paper cites Nist chemistry webbook, nist standard reference database number 69,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Nist chemistry webbook, nist standard reference database number 69,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:10.741172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a231bbba-a278-4c8e-8707-1237a600b8df · outbound

This paper cites Machine learning force fields for molecular liquids: Ethylene carbonate/ethyl methyl carbonate binary solvent,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Machine learning force fields for molecular liquids: Ethylene carbonate/ethyl methyl carbonate binary solvent,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:10.661493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 30ba51f1-4771-46df-b083-2f1d2a5b8c44 · outbound

This paper cites De Novo Protein Design: Fully Automated Sequence Selection,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials De Novo Protein Design: Fully Automated Sequence Selection,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:10.462772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5f55364f-6e3e-43bf-88b2-4b08d25319c5 · outbound

This paper cites Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:10.307015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation feddb8c1-a14e-4e0f-b98d-b2501b0d7b47 · outbound

This paper cites Do Molecular Dynamics Force Fields Capture Conformational Dynamics of Alanine in Water?.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Do Molecular Dynamics Force Fields Capture Conformational Dynamics of Alanine in Water?

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:10.031582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 14dd417e-3814-4aab-8196-cb634d83de3f · outbound

This paper cites Solvation effects on alanine dipeptide: A MP2/cc-pVTZ//MP2/6-31G** study of ( ϕ, ψ) energy maps and conformers in the gas phase, ether, and water,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Solvation effects on alanine dipeptide: A MP2/cc-pVTZ//MP2/6-31G** study of ( ϕ, ψ) energy maps and conformers in the gas phase, ether, and water,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:09.797066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 56821cbc-7821-48a4-82a6-72cd4cfad5cf · outbound

This paper cites Improved side-chain torsion potentials for the Amber ff99SB protein force field,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Improved side-chain torsion potentials for the Amber ff99SB protein force field,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:09.641497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8f5c9d73-84bc-4bdc-8fa0-cbde3c89ee0d · outbound

This paper cites Commentary: The materials project: A materials genome approach to accelerating materials innovation,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Commentary: The materials project: A materials genome approach to accelerating materials innovation,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:09.490935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 956b794b-9e18-4e9c-be50-599c2a096149 · outbound

This paper cites The open molecules 2025 (omol25) dataset, evaluations, and models,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials The open molecules 2025 (omol25) dataset, evaluations, and models,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-06T16:14:01.032015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b498455-b29e-4d6e-b459-841220afbd7a · outbound

This paper cites The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysts,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:09.306398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5eeffbe3-ec4e-42a7-a049-998a46eee0d3 · outbound

This paper cites Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T16:14:01.332453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b885076-f71b-45d1-b851-4a2481866e40 · outbound

This paper cites Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Efficient Long-Range Machine Learning Force Fields for Liquid and Materials Properties

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:14:05.767093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4d709984-f964-49a2-a76b-f8ba8f03a918 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Pytorch: An imperative style, high-performance deep learning library,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:09.175982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5f765cb2-cca4-422f-8f07-b522bf6942aa · outbound

This paper cites Spice 2.0.1,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Spice 2.0.1,

Reference 74

Resolution: verified exact; raw_fallback, observed 2026-08-06T16:14:05.547397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3137ee04-ac77-4bdc-86d4-0d367d903874 · outbound

This paper cites Pyscf: the python-based simulations of chemistry framework,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Pyscf: the python-based simulations of chemistry framework,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:08.960329Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 94c991a9-8995-4ebc-8cc3-cc7b07ceb3f2 · outbound

This paper cites The atomic simulation environment—a Python library for working with atoms,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials The atomic simulation environment—a Python library for working with atoms,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:08.741095Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ac4ae995-0db4-47dd-b275-cd2ee9c8c372 · outbound

This paper cites Pubchem 2025 update,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Pubchem 2025 update,

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:08.566311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 086f1c7d-929b-4804-9814-8ac8f6db3a73 · outbound

This paper cites rdkit/rdkit: 2025 03 2 (q1 2025) release,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials rdkit/rdkit: 2025 03 2 (q1 2025) release,

Reference 78

Resolution: verified exact; raw_fallback, observed 2026-08-06T16:14:05.208858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 78339b09-17eb-4011-8e9b-710cc08e02e6 · outbound

This paper cites Gromacs: A message-passing parallel molecular dynamics implementation,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Gromacs: A message-passing parallel molecular dynamics implementation,

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:08.375574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3058501e-57ea-400e-84d4-806cb62c6754 · outbound

This paper cites Automatic atom type and bond type perception in molecular mechanical calculations,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Automatic atom type and bond type perception in molecular mechanical calculations,

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:08.189962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5202c426-cba7-4f08-ab16-bf77ce4a1c64 · outbound

This paper cites Construction of higher order symplectic integrators,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Construction of higher order symplectic integrators,

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:08.019690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dd1cc0d3-5fb4-4d67-a5ff-16cd07f20180 · outbound

This paper cites Comparison of simple potential functions for simulating liquid water,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Comparison of simple potential functions for simulating liquid water,

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:07.821070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f86e9493-35d0-4d05-90bd-e7a780ab0a99 · outbound

This paper cites Promoting transparency and reproducibility in enhanced molecular simulations,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Promoting transparency and reproducibility in enhanced molecular simulations,

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:07.651607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c3984368-3248-47ea-ab32-fa737a790b74 · outbound

This paper cites PLUMED 2: New feathers for an old bird,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials PLUMED 2: New feathers for an old bird,

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:07.582384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2881fa03-8ccf-4dbf-940e-9557ac14136c · outbound

This paper cites Metadynamics with adaptive gaussians,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Metadynamics with adaptive gaussians,

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:07.377620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 7297083c-3115-40dd-be84-7a1075d0d711 · outbound

This paper cites Structural relaxation made simple,.

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials Structural relaxation made simple,

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:14:07.143191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation feee2e65-26aa-4b80-a7bd-fb2ba6d2f9cd · inbound

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials cites this paper.

Modeling phase transformations in Mn-rich disordered rocksalt cathodes with machine learning interatomic potentials A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:41.948805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d66ba25e-4ef9-4a38-9ab9-31095f2c10e0 · inbound

Simultaneous Learning of Static and Dynamic Charges cites this paper.

Simultaneous Learning of Static and Dynamic Charges A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-05-21T16:54:16.411940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 71d45caa-54aa-4959-92fb-6ca69b984590 · inbound

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations cites this paper.

Accuracy and Efficiency Benchmarks of Pretrained Machine Learning Potentials for Molecular Simulations A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-16T11:30:52.654527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 924429c6-e568-4865-882c-cdc9d6606eb6 · inbound

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models cites this paper.

Six Open Questions in Machine-Learned Interatomic Potential Foundation Models A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials

Reference 154

Resolution: verified exact; arxiv_id, observed 2026-07-02T19:37:19.160570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.