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Paper Citation Record · LEDGER

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants

As of 23 August 2026, this Paper Citation Record lists 60 of 60 outbound references and 0 inbound Pith citation observations for arXiv:2603.06396.

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

pith.paper-citation-record.v1
2603.06396 v2

Coverage vector

measured 60 of 60 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-15T13:53:02.803305Z

measured 60 of 60 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

60 of 60 outbound references displayed

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  • verified fuzzy0
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External citation measurements

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Outbound references

Observation 9b6ca8bd-c79e-4abd-bb52-0df07013f09f · outbound

This paper cites Strickland, B.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Strickland, B

Reference 1

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 8f1bea1a-0123-45e6-b210-74a1e248b01c · outbound

This paper cites Strickland, B.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Strickland, B

Reference 2

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Observation bd514774-1aaf-4101-9063-c165ad8c0a53 · outbound

This paper cites Strickland, M.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Strickland, M

Reference 3

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Observation d26f6578-c938-4f42-9321-54c30c8388cb · outbound

This paper cites Cohen-Boulakia, K.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Cohen-Boulakia, K

Reference 4

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Observation d2f06d9d-4d4b-4bf3-93b7-9d37cfbfc8aa · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 5

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Observation ff2b50fb-3f8b-485e-8caf-25103a8ea82f · outbound

This paper cites Foundation models for scientific discovery: From paradigm enhancement to paradigm transition.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Foundation models for scientific discovery: From paradigm enhancement to paradigm transition

Reference 6

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Observation b02a9e92-c918-4ea3-9054-64cde70dab7c · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 7

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Observation 497fd7f7-b2d9-44e6-873c-51c71e0b6e80 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 8

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Observation ffaf81a1-6dcd-4ec4-a5c1-e33bcc1a03c8 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 9

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Observation df3c6203-a3fc-4d58-9f10-035533173297 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 10

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Observation 340dfbf8-0dbf-4a45-a809-34fa11f0e511 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 11

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Observation f43aece3-b88b-4124-b0c6-6d1f7a8b9898 · outbound

This paper cites Singh, S.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Singh, S

Reference 12

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Observation 4c12060a-850c-4555-819c-c880ee419875 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 13

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Observation f3a44a0c-dbaa-4c6f-9447-de7b9cb8d913 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 14

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 58e929df-eb80-4ece-a7c4-c118cf1a6fb8 · outbound

This paper cites Farshidi, X.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Farshidi, X

Reference 15

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Observation 7c831fb8-c437-4328-a999-05b4137edf20 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 16

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Observation 65ae0e24-6595-46d3-9425-6f7382660daa · outbound

This paper cites Cambridge University Press, 2006.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Cambridge University Press, 2006

Reference 17

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Observation 0b6e4708-238f-46b3-999d-e461cf410430 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 18

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Observation ae39c332-2d1a-4db6-b8df-f27cb1b35fb6 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 19

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 226fb4f2-f75d-4683-aa26-be5b74af1971 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 20

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Observation 72aec41c-ac95-4845-98fd-e2145706c15f · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 21

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Observation 20840f3a-0a15-44ab-a224-664a11bd92a8 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 22

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Observation e6c5a022-dcb8-4fa6-b5b4-f0d601297120 · outbound

This paper cites Stodden and S.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Stodden and S

Reference 23

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Observation 105bfd1a-8571-416d-950d-64ec87973b66 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 24

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Observation 13113766-489f-48bc-a743-db1b07d697ce · outbound

This paper cites Freire, D.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Freire, D

Reference 25

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Observation 561943c0-849f-4cab-9a64-94ad876f8fa3 · outbound

This paper cites Missier, S.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Missier, S

Reference 26

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Observation 13d01681-b6a9-4a4d-8eb2-13ed8f35c520 · outbound

This paper cites Moreau, B.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Moreau, B

Reference 27

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Observation 3e21d4f3-4946-41a0-bcd3-cb5c9e7d761b · outbound

This paper cites Boettiger.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Boettiger

Reference 28

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Observation 592e2df1-0080-40eb-ba9e-6085803388c7 · outbound

This paper cites Grüning, J.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Grüning, J

Reference 29

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Observation 1eff9b71-1b82-477c-a841-01c5599a0efa · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 30

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Observation 7acbc29f-1dfe-4696-990e-bb91789bfd39 · outbound

This paper cites Hettrick, M.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Hettrick, M

Reference 31

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Observation 4a9edfc0-cba9-4e14-ae4b-95d36ff1e500 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 32

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arxiv_id, observed 2026-07-15T14:01:34.727780Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation cb06574c-13f2-416d-aa95-6c890801b2bb · outbound

This paper cites Ziemann, P.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Ziemann, P

Reference 33

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Observation 80170674-d28b-4d30-8a3a-ad0fbd981090 · outbound

This paper cites de Oliveira Andrade.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants de Oliveira Andrade

Reference 34

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Observation fcf405b0-5092-41c4-a7c2-d1ee5c7ec497 · outbound

This paper cites Yildiz and T.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Yildiz and T

Reference 35

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Observation da7a0d49-9c7b-44cf-ad13-3fb82b07194c · outbound

This paper cites From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants From Prompt to Pipeline: Large Language Models for Scientific Workflow Development in Bioinformatics

Reference 36

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Observation f5eab6f0-47af-4d93-9ad8-55e4c0bb9af7 · outbound

This paper cites Jiang, W.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Jiang, W

Reference 37

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Observation 63bdbf55-67f1-4827-9931-cc12be8a96d9 · outbound

This paper cites ReAct: Synergizing Reasoning and Acting in Language Models.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants ReAct: Synergizing Reasoning and Acting in Language Models

Reference 38

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Observation ec3820a2-00c3-4642-8b4d-295129c7223d · outbound

This paper cites Executable code actions elicit better LLM agents.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Executable code actions elicit better LLM agents

Reference 39

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Observation f7b0ef79-ad6b-476a-833a-98b88cc22899 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 40

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Observation 8b23d7bb-1e97-4efd-b915-2f59c1dea6a0 · outbound

This paper cites The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science

Reference 41

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Observation ed249a89-b2fc-4013-add9-615cf4d7a438 · outbound

This paper cites Ensuring Reproducibility in Generative AI Systems for General Use Cases: A Framework for Regression Testing and Open Datasets.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Ensuring Reproducibility in Generative AI Systems for General Use Cases: A Framework for Regression Testing and Open Datasets

Reference 42

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Observation f31edb32-fe48-4ff1-a6e4-4901e7d6728c · outbound

This paper cites Hosseini, S.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Hosseini, S

Reference 43

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Observation e6b03e24-db8a-4c9f-ad67-4c2a7676867a · outbound

This paper cites Guidelines for Empirical Studies in Software Engineering involving Large Language Models.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Guidelines for Empirical Studies in Software Engineering involving Large Language Models

Reference 44

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Observation 66525c7f-bfc7-4f33-b8b5-1164b978c313 · outbound

This paper cites Brucks and O.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Brucks and O

Reference 45

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Observation 2b7bec41-c668-4267-9365-f25227ee1e4e · outbound

This paper cites Unnatural language processing: How do language models handle machine-generated prompts?.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unnatural language processing: How do language models handle machine-generated prompts?

Reference 46

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Observation c59bfe77-48f0-4322-a84d-75485ccf2010 · outbound

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Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 47

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Observation 619dc1c2-b7f5-4450-ad0f-7b63955c32e6 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 48

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Observation 50d5aaec-ec8e-4baf-a5e3-1c86f80492f7 · outbound

This paper cites Defending Against Indirect Prompt Injection Attacks With Spotlighting.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Defending Against Indirect Prompt Injection Attacks With Spotlighting

Reference 49

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Observation d976c8fb-7479-4e45-bdce-834e0ff9409b · outbound

This paper cites Unveiling Large Language Model Supply Chain: Structure, Domain, and Vulnerabilities.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unveiling Large Language Model Supply Chain: Structure, Domain, and Vulnerabilities

Reference 50

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Observation 06f77a83-dead-4a96-908b-da0d3f2887b1 · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 51

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Observation 8c1739c1-1d49-4708-84d8-aad3aee167f9 · outbound

This paper cites Goecks, A.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Goecks, A

Reference 52

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

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Observation 2a7cca52-86b4-46f3-aea5-f7b475107c7b · outbound

This paper cites Köster and S.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Köster and S

Reference 53

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Observation 505e9ba3-4ff2-46ad-b14b-51a567398d53 · outbound

This paper cites Di Tommaso, M.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Di Tommaso, M

Reference 54

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Observation dc09d42d-b8fa-45f7-b041-88b53ad249ad · outbound

This paper cites Mölder, K.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Mölder, K

Reference 55

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Observation acf82646-408d-456e-92ed-32b091f3980f · outbound

This paper cites Kanwal, F.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Kanwal, F

Reference 56

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source=pdf_text observed=2026-07-15T13:53:02.803305Z digest=sha256:9674a66b8a3616dd3ef40064ccef0814904bb418b64cc95e7e9a0f81e421c53c

Observation e4218ead-8b6f-4234-a3a4-2d11f7608170 · outbound

This paper cites Using thematic analysis in psychology.Qualitative Research in Psychology, 3(2):77–101, 2006.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Using thematic analysis in psychology.Qualitative Research in Psychology, 3(2):77–101, 2006

Reference 57

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Observation 3c25fad8-c5cd-4df6-8182-90b903334773 · outbound

This paper cites Model context protocol specification, 2024.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Model context protocol specification, 2024

Reference 58

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Observation 03c74217-16a0-4119-a1cc-70677a6adc19 · outbound

This paper cites Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso, and Sven Nahnsen.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso, and Sven Nahnsen

Reference 59

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-07-15T13:53:02.803305Z digest=sha256:6f0752b469f3c1bed61ab8ef064d9690633e2b359d648452311482910a326f62

Observation 69d959a2-f764-4d56-8e6b-8b7d24a70f0e · outbound

This paper cites an unresolved cited work.

Long-range machine-learning potentials with environment-dependent charges enable predicting LO-TO splitting and dielectric constants Unresolved cited work

Reference 60

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