Overfitting by design: neural network density functionals for water | Paper Citation Record

Observation da051b31-619f-4874-96b4-2a657111aeaa · outbound

This paper cites Nature Communications 2014 5:15(1), 1–7 (2014) https://doi.org/10.1038/ncomms4533.

Overfitting by design: neural network density functionals for water Nature Communications 2014 5:15(1), 1–7 (2014) https://doi.org/10.1038/ncomms4533

Reference 1

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.554166Z

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Observation 828a10f6-abf0-446f-aa31-e4635e4f8dc1 · outbound

This paper cites Photosynthesis Research102(2), 443–453 (2009) https://doi.org/10.1007/S11120-009-9404-8.

Overfitting by design: neural network density functionals for water Photosynthesis Research102(2), 443–453 (2009) https://doi.org/10.1007/S11120-009-9404-8

Reference 2

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.557971Z

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Observation 585c4d94-d6b1-4450-8369-ec5aa6a07b6e · outbound

This paper cites Wiley Interdisciplinary Reviews: Computational Molecular Science3(5), 438–448 (2013) https://doi.org/10.1002/WCMS.1125.

Overfitting by design: neural network density functionals for water Wiley Interdisciplinary Reviews: Computational Molecular Science3(5), 438–448 (2013) https://doi.org/10.1002/WCMS.1125

Reference 3

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.563061Z

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Observation 92a5ce29-2023-4241-befe-9ca20fefec61 · outbound

This paper cites Burke, Perspective on density functional theory, The Journal of Chemical Physics 136, 10.1063/1.4704546 (2012).

Overfitting by design: neural network density functionals for water Burke, Perspective on density functional theory, The Journal of Chemical Physics 136, 10.1063/1.4704546 (2012)

Reference 4

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.567165Z

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Observation f6e3680f-7d8a-4768-b78f-f2698909270d · outbound

This paper cites Zeitschrift fur Kristallographie220(5-6), 531–548 (2005) https://doi.org/10.

Overfitting by design: neural network density functionals for water Zeitschrift fur Kristallographie220(5-6), 531–548 (2005) https://doi.org/10

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T17:06:42.772731Z

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Observation 459aba27-2491-421d-b28c-91cf8664ec52 · outbound

This paper cites Journal of Physical Chemistry C113(43), 18962–18967 (2009) https://doi.org/10.1021/JP9077079.

Overfitting by design: neural network density functionals for water Journal of Physical Chemistry C113(43), 18962–18967 (2009) https://doi.org/10.1021/JP9077079

Reference 6

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.587972Z

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This paper cites The Journal of Chemical Physics 109(17), 7522–7545 (1998) https://doi.org/10.1063/1.477375 14.

Overfitting by design: neural network density functionals for water The Journal of Chemical Physics 109(17), 7522–7545 (1998) https://doi.org/10.1063/1.477375 14

Reference 7

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.593058Z

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Observation 4b731054-2188-482d-8483-cd658f186e33 · outbound

This paper cites Harry Moore, Nicholas J.

Overfitting by design: neural network density functionals for water Harry Moore, Nicholas J

Reference 8

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This paper cites Journal of Chemical Theory and Computation 21(4), 1667–1683 (2025) https://doi.org/10.1021/ACS.JCTC.4C01477.

Overfitting by design: neural network density functionals for water Journal of Chemical Theory and Computation 21(4), 1667–1683 (2025) https://doi.org/10.1021/ACS.JCTC.4C01477

Reference 9

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.575019Z

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Observation 153f5576-0601-47f5-ac95-f34497b689be · outbound

This paper cites Schuch, F.

Overfitting by design: neural network density functionals for water Schuch, F

Reference 10

Resolution: metadata mismatch; doi, observed 2026-05-12T04:11:22.534138Z

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Observation 9a3e9709-dc32-4b00-805b-623e23092463 · outbound

This paper cites Goerigk, A.

Overfitting by design: neural network density functionals for water Goerigk, A

Reference 11

Resolution: metadata mismatch; doi, observed 2026-05-12T04:11:22.528657Z

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Observation 96d8136c-105e-408c-91c2-b9e8cff5eb15 · outbound

This paper cites Physical Review B45(23), 13244 (1992) https: //doi.org/10.1103/PhysRevB.45.13244.

Overfitting by design: neural network density functionals for water Physical Review B45(23), 13244 (1992) https: //doi.org/10.1103/PhysRevB.45.13244

Reference 12

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.539893Z

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This paper cites and Burke, Kieron and Wang, Yue , month = dec, year =.

Overfitting by design: neural network density functionals for water and Burke, Kieron and Wang, Yue , month = dec, year =

Reference 13

Resolution: metadata mismatch; doi, observed 2026-05-12T04:11:22.450421Z

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Observation 92d0649d-b350-4576-9190-1be67b0b9ca7 · outbound

This paper cites The Journal of Chemical Physics98(2), 1372–1377 (1993) https://doi.org/10.

Overfitting by design: neural network density functionals for water The Journal of Chemical Physics98(2), 1372–1377 (1993) https://doi.org/10

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T17:06:42.751712Z

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Overfitting by design: neural network density functionals for water Unresolved cited work

Reference 15

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This paper cites AIP Conference Proceedings577(1), 1–20 (2001) https://doi.org/10.1063/1.1390175.

Overfitting by design: neural network density functionals for water AIP Conference Proceedings577(1), 1–20 (2001) https://doi.org/10.1063/1.1390175

Reference 16

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.447118Z

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This paper cites SoftwareX7, 1–5 (2018) https://doi.org/10.1016/J.SOFTX.2017.11.002.

Overfitting by design: neural network density functionals for water SoftwareX7, 1–5 (2018) https://doi.org/10.1016/J.SOFTX.2017.11.002

Reference 17

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.453975Z

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Observation f4950439-52bd-4c0a-9051-2de8e18b6f65 · outbound

This paper cites R., Wilson, S.

Overfitting by design: neural network density functionals for water R., Wilson, S

Reference 18

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This paper cites Nature Reviews Physics 2022 4:64(6), 357–358 (2022) https://doi.org/ 10.1038/s42254-022-00470-2.

Overfitting by design: neural network density functionals for water Nature Reviews Physics 2022 4:64(6), 357–358 (2022) https://doi.org/ 10.1038/s42254-022-00470-2

Reference 19

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.516662Z

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This paper cites APL Computational Physics1(2), 48 (2025) https://doi.org/10.1063/5.0297853.

Overfitting by design: neural network density functionals for water APL Computational Physics1(2), 48 (2025) https://doi.org/10.1063/5.0297853

Reference 20

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.443551Z

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Observation 3bdf0491-6d1c-46a7-8af5-29adf812da20 · outbound

This paper cites Can machines learn density functionals? Past, present, and future of ML in DFT.

Overfitting by design: neural network density functionals for water Can machines learn density functionals? Past, present, and future of ML in DFT

Reference 21

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:41:45.214707Z

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Observation b754cd0a-f823-493d-ae4f-2ca4c91ab5ee · outbound

This paper cites Nagai, R.

Overfitting by design: neural network density functionals for water Nagai, R

Reference 22

Resolution: metadata mismatch; doi, observed 2026-05-12T04:11:22.457329Z

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Observation 8bc807b5-6af1-42a1-bb8b-ed9bcd07bb83 · outbound

This paper cites Kasim and S.

Overfitting by design: neural network density functionals for water Kasim and S

Reference 23

Resolution: metadata mismatch; doi, observed 2026-05-12T04:11:22.427371Z

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This paper cites Physical Review Letters126(3), 036401 (2021) https://doi.org/10.1103/ PHYSREVLETT.126.036401.

Overfitting by design: neural network density functionals for water Physical Review Letters126(3), 036401 (2021) https://doi.org/10.1103/ PHYSREVLETT.126.036401

Reference 24

Resolution: malformed identifier; raw_fallback, observed 2026-05-12T17:06:42.760549Z

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This paper cites Science374(6573), 1385–1389 (2021) https://doi.org/10.1126/ SCIENCE.ABJ6511.

Overfitting by design: neural network density functionals for water Science374(6573), 1385–1389 (2021) https://doi.org/10.1126/ SCIENCE.ABJ6511

Reference 25

Resolution: malformed identifier; raw_fallback, observed 2026-05-12T17:06:42.762884Z

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Overfitting by design: neural network density functionals for water Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-05-12T17:06:42.768737Z

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Observation fd9a1ae4-b39e-4444-ba40-b484ff8a927e · outbound

This paper cites Physical Review Letters77(18), 3865 (1996) https://doi.org/10.

Overfitting by design: neural network density functionals for water Physical Review Letters77(18), 3865 (1996) https://doi.org/10

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T17:06:42.770663Z

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This paper cites Royal Society Open Science11(5) (2024) https://doi.org/10.1098/RSOS.231374.

Overfitting by design: neural network density functionals for water Royal Society Open Science11(5) (2024) https://doi.org/10.1098/RSOS.231374

Reference 28

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.431899Z

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This paper cites Machine 16 Learning: Science and Technology7(2), 25001 (2026) https://doi.org/10.1088/ 2632-2153/ae3c5a.

Overfitting by design: neural network density functionals for water Machine 16 Learning: Science and Technology7(2), 25001 (2026) https://doi.org/10.1088/ 2632-2153/ae3c5a

Reference 29

Resolution: malformed identifier; raw_fallback, observed 2026-05-12T17:06:42.746643Z

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This paper cites Nature Communications 2020 11:1 11(1), 1–10 (2020) https://doi.org/10.1038/s41467-020-17265-7.

Overfitting by design: neural network density functionals for water Nature Communications 2020 11:1 11(1), 1–10 (2020) https://doi.org/10.1038/s41467-020-17265-7

Reference 30

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.423753Z

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This paper cites Nature Communications 11(1), 5223 (2020) https://doi.org/10.1038/s41467-020-19093-1.

Overfitting by design: neural network density functionals for water Nature Communications 11(1), 5223 (2020) https://doi.org/10.1038/s41467-020-19093-1

Reference 31

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.498989Z

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This paper cites Journal of Chemical Physics144(22), 224101 (2016).

Overfitting by design: neural network density functionals for water Journal of Chemical Physics144(22), 224101 (2016)

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T17:06:42.753991Z

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This paper cites Advanced Materials31(46), 1902765 (2019) https://doi.org/10.1002/ADMA.201902765.

Overfitting by design: neural network density functionals for water Advanced Materials31(46), 1902765 (2019) https://doi.org/10.1002/ADMA.201902765

Reference 33

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.523709Z

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This paper cites Journal of Physical Chemistry A 124(4), 731–745 (2020) https://doi.org/10.1021/ACS.JPCA.9B08723.

Overfitting by design: neural network density functionals for water Journal of Physical Chemistry A 124(4), 731–745 (2020) https://doi.org/10.1021/ACS.JPCA.9B08723

Reference 34

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.488482Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 648158ff-57cb-4568-a4ba-986c3c65ed4c · outbound

This paper cites How accurate are dft forces? unexpectedly large uncertainties in molecular datasets.

Overfitting by design: neural network density functionals for water How accurate are dft forces? unexpectedly large uncertainties in molecular datasets

Reference 35

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:41:45.187242Z

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This paper cites Journal of Chemical Physics156(8), 84801 (2022) https://doi.org/10.1063/5.0076202.

Overfitting by design: neural network density functionals for water Journal of Chemical Physics156(8), 84801 (2022) https://doi.org/10.1063/5.0076202

Reference 36

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.492017Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a555fb56-1330-455b-9da1-81d3c5ebb066 · outbound

This paper cites $\xi$-torch: differentiable scientific computing library.

Overfitting by design: neural network density functionals for water $\xi$-torch: differentiable scientific computing library

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:41:45.193908Z

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Observation 808c40cf-5f8a-467a-ab67-4439f2662352 · outbound

This paper cites Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrian E.

Overfitting by design: neural network density functionals for water Smith, Roman Zubatyuk, Benjamin Nebgen, Nicholas Lubbers, Kipton Barros, Adrian E

Reference 38

Resolution: metadata mismatch; doi, observed 2026-05-12T04:11:22.484669Z

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This paper cites Nature Communications 2025 16:116(1), 11306 (2025) https: //doi.org/10.1038/s41467-025-66450-z.

Overfitting by design: neural network density functionals for water Nature Communications 2025 16:116(1), 11306 (2025) https: //doi.org/10.1038/s41467-025-66450-z

Reference 39

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.477665Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1514b518-f627-4cf8-8eb4-7643e9ea86d9 · outbound

This paper cites The Journal of Chemical Physics115(20), 9113–9125 (2001) https://doi.org/10.1063/ 17 1.1413524.

Overfitting by design: neural network density functionals for water The Journal of Chemical Physics115(20), 9113–9125 (2001) https://doi.org/10.1063/ 17 1.1413524

Reference 40

Resolution: malformed identifier; raw_fallback, observed 2026-05-12T17:06:42.758486Z

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Observation 24af07e3-1dc5-4c7f-8d88-e795f01dbe94 · outbound

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Overfitting by design: neural network density functionals for water Unresolved cited work

Reference 41

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.473935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5d38b042-18b7-4362-a5cf-ed7727952a12 · outbound

This paper cites Journal of Chemical Theory and Computation5(4), 1016–1026 (2009) https://doi.org/10.

Overfitting by design: neural network density functionals for water Journal of Chemical Theory and Computation5(4), 1016–1026 (2009) https://doi.org/10

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T17:06:42.748951Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f780deb3-4a84-4eec-bf9b-27ef6cca9f22 · outbound

This paper cites Theoretical Chemistry Accounts113(5), 267–273 (2005) https://doi.org/ 10.1007/S00214-005-0635-2.

Overfitting by design: neural network density functionals for water Theoretical Chemistry Accounts113(5), 267–273 (2005) https://doi.org/ 10.1007/S00214-005-0635-2

Reference 43

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.460453Z

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No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 561af3ef-47d8-4ff0-b578-855dd54091e9 · outbound

This paper cites Journal of Chemical Physics 156(16), 161103 (2022) https://doi.org/10.1063/5.0090862.

Overfitting by design: neural network density functionals for water Journal of Chemical Physics 156(16), 161103 (2022) https://doi.org/10.1063/5.0090862

Reference 44

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.464030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

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This paper cites Journal of Chemical Physics145(19) (2016) https://doi.org/10.

Overfitting by design: neural network density functionals for water Journal of Chemical Physics145(19) (2016) https://doi.org/10

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-12T17:06:42.764864Z

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Observation c0fd4311-afef-46d2-9085-1a94aef054e7 · outbound

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Overfitting by design: neural network density functionals for water Unresolved cited work

Reference 46

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.470079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8e7259d7-5d4c-4360-a5a3-62c08056e4d5 · outbound

This paper cites Nature Communications 2023 14:114(1), 799 (2023) https://doi.org/10.1038/ s41467-023-36094-y.

Overfitting by design: neural network density functionals for water Nature Communications 2023 14:114(1), 799 (2023) https://doi.org/10.1038/ s41467-023-36094-y

Reference 47

Resolution: malformed identifier; raw_fallback, observed 2026-05-12T17:06:42.766780Z

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This paper cites Nature Communications 2021 12:112(1), 6359 (2021) https://doi.org/10.1038/s41467-021-26618-9 18.

Overfitting by design: neural network density functionals for water Nature Communications 2021 12:112(1), 6359 (2021) https://doi.org/10.1038/s41467-021-26618-9 18

Reference 48

Resolution: verified exact; doi, observed 2026-05-12T04:11:22.481359Z

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