Paper Citation Record · LEDGER

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

As of 11 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 10 inbound Pith citation observations for arXiv:2501.09009.

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

pith.paper-citation-record.v1
2501.09009 v2

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:18:34.289780Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:31:33.378453Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:07:22.569489Z

Reference resolution

29 of 29 outbound references displayed

verified exact1
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External citation measurements

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Outbound references

Observation d20aa664-7ec1-48b1-bef3-9f3657b30a45 · outbound

This paper cites GPT-4 Technical Report.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians GPT-4 Technical Report

Reference 1

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Observation 80b1440f-2054-4d2d-9eaa-f405d6fce53b · outbound

This paper cites Filippo Bigi, Marcel Langer, and Michele Ceriotti.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Filippo Bigi, Marcel Langer, and Michele Ceriotti

Reference 3

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Observation 86c10033-ab57-428c-b9f0-9f2134095a24 · outbound

This paper cites 1 translate to considerably improved stability over time.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians 1 translate to considerably improved stability over time

Reference 4

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Observation 2a8650aa-2632-449c-ba98-ffa65aa3609e · outbound

This paper cites We include the training details below.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians We include the training details below

Reference 5

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Observation 69850f75-d396-4bc6-b246-a08d24318124 · outbound

This paper cites e3nn: Euclidean Neural Networks.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians e3nn: Euclidean Neural Networks

Reference 8

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Observation 5f96b7a6-29b8-4b91-ad90-a1e1d17d8555 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Distilling the Knowledge in a Neural Network

Reference 9

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Observation 2d430511-436b-45fe-9f24-ede1d51cbc2c · outbound

This paper cites A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines

Reference 11

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Observation 7d98fc8c-161d-4695-b734-30a71eb7f652 · outbound

This paper cites On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

Reference 14

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Observation bfb87371-543f-458c-a050-5e654a3865f1 · outbound

This paper cites Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products

Reference 15

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Observation 291878eb-e361-4763-a6e6-94bed60ded32 · outbound

This paper cites Transferring Knowledge from Large Foundation Models to Small Downstream Models.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Transferring Knowledge from Large Foundation Models to Small Downstream Models

Reference 18

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Observation f628220d-d757-4de6-9531-fbd56a633fac · outbound

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

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators

Reference 19

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Observation 1d7054d2-c2f2-4043-b1ad-4ce841ac85d7 · outbound

This paper cites URL https://dx.doi.org/10.1088/2632-2153/ac9955.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians URL https://dx.doi.org/10.1088/2632-2153/ac9955

Reference 21

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Observation 8291f699-7572-430b-9a95-87fde4a2fbc5 · outbound

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Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Unresolved cited work

Reference 23

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Observation d6664b2f-cbf9-42da-bc97-ed3840a11ef2 · outbound

This paper cites A slashed value indicates that different values were used across datasets.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians A slashed value indicates that different values were used across datasets

Reference 24

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Observation 6250c785-6d91-4f77-84c2-2986b37cbe35 · outbound

This paper cites As in the Hessian distillation setting, we pre-compute and save the teacher’s final node features over the dataset prior to training.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians As in the Hessian distillation setting, we pre-compute and save the teacher’s final node features over the dataset prior to training

Reference 26

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Observation 11e77e0d-ec9b-4e43-af18-5782579ca845 · outbound

This paper cites an unresolved cited work.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Unresolved cited work

Reference 27

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Observation 65de8356-4532-43bb-b255-800fcb8567fd · outbound

This paper cites Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

Reference 2000

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This paper cites We select 100 structures from the Monomers split of the SPICE dataset, and run optimization until all the per-atom force norms are below 0.05 eV/A.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians We select 100 structures from the Monomers split of the SPICE dataset, and run optimization until all the per-atom force norms are below 0.05 eV/A

Reference 2006

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This paper cites URL https: //link.aps.org/doi/10.1103/PhysRevLett.100.146401.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians URL https: //link.aps.org/doi/10.1103/PhysRevLett.100.146401

Reference 2008

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Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Unresolved cited work

Reference 2014

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Observation 1979547d-1562-4145-9e56-4ae4d1fc9d6a · outbound

This paper cites Training Compute-Optimal Large Language Models.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Training Compute-Optimal Large Language Models

Reference 2015

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Observation 8f7a7568-7671-409b-ad00-808693e5b8c6 · outbound

This paper cites Stefan Chmiela, V alentin V assilev-Galindo, Oliver T Unke, Adil Kabylda, Huziel E Sauceda, Alexandre Tkatchenko, and Klaus-Robert M¨uller.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Stefan Chmiela, V alentin V assilev-Galindo, Oliver T Unke, Adil Kabylda, Huziel E Sauceda, Alexandre Tkatchenko, and Klaus-Robert M¨uller

Reference 2017

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This paper cites URLhttps://doi.org/10.1063/1.5019779.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians URLhttps://doi.org/10.1063/1.5019779

Reference 2018

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Observation d70348f4-a73f-470a-81ab-7f03c0a103ed · outbound

This paper cites Scaling Laws for Neural Language Models.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Scaling Laws for Neural Language Models

Reference 2019

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Observation 972568b2-5087-48fe-b8e1-16a55d31f9d9 · outbound

This paper cites Accelerating Molecular Graph Neural Networks via Knowledge Distillation.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Accelerating Molecular Graph Neural Networks via Knowledge Distillation

Reference 2020

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This paper cites Open catalyst 2020 (oc20) dataset and community challenges.Acs Catalysis, 11(10):6059–6072,.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Open catalyst 2020 (oc20) dataset and community challenges.Acs Catalysis, 11(10):6059–6072,

Reference 2021

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This paper cites Ilyes Batatia, Philipp Benner, Y uan Chiang, Alin M.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians Ilyes Batatia, Philipp Benner, Y uan Chiang, Alin M

Reference 2022

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This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 2023

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This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians DINOv2: Learning Robust Visual Features without Supervision

Reference 2024

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Pith citing papers

Observation 82e3ff1a-41e6-41f0-825d-2c9a23ca2660 · inbound

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys cites this paper.

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 58

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Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials cites this paper.

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 4

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Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials cites this paper.

Teacher-student training improves accuracy and efficiency of machine learning interatomic potentials Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 10

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Observation 0d19b2a6-eeb5-472a-a64a-4a2427dc030e · inbound

Distillation of atomistic foundation models across architectures and chemical domains cites this paper.

Distillation of atomistic foundation models across architectures and chemical domains Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 41

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Observation 01dfac6d-6a1d-4af2-9a86-2679c9862abd · inbound

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications cites this paper.

Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 134

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A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations cites this paper.

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 3

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Observation e53f9973-7c98-43ef-b1b1-61656a72540c · inbound

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning cites this paper.

Compact SO(3) Equivariant Atomistic Foundation Models via Structural Pruning Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 43

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Observation 602bb326-cdba-4e0a-9ef0-942cfa052d46 · inbound

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows cites this paper.

Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 14

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7c21f5a7-dc32-478b-82de-1558f9ac8abd · inbound

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation cites this paper.

Machine Learning Interatomic Potentials: Advancing Open-Source Software for Efficient and Scalable Molecular Simulation Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 62

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Observation 87717ec6-1268-4b5b-9d89-10a7b802cef0 · inbound

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry cites this paper.

Distilling first-principles accuracy into compact machine learning potentials for condensed-phase chemistry Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-02T20:07:22.571178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.