Paper Citation Record · LEDGER

Machine learning potentials for modeling alloys across compositions

As of 7 August 2026, this Paper Citation Record lists 84 of 84 outbound references and 2 inbound Pith citation observations for arXiv:2506.12592.

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pith.paper-citation-record.v1
2506.12592 v2

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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Outbound references

Observation 89bb454a-23fb-41d2-909c-d93369de9790 · outbound

This paper cites an unresolved cited work.

Machine learning potentials for modeling alloys across compositions Unresolved cited work

Reference 1

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This paper cites Orb: A Fast, Scalable Neural Network Potential.

Machine learning potentials for modeling alloys across compositions Orb: A Fast, Scalable Neural Network Potential

Reference 2

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This paper cites MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures.

Machine learning potentials for modeling alloys across compositions MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures

Reference 3

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This paper cites High accuracy neural network interatomic potential for NiTi shape memory al- loy.

Machine learning potentials for modeling alloys across compositions High accuracy neural network interatomic potential for NiTi shape memory al- loy

Reference 4

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Observation d42ca6e8-de6e-4e7c-a34e-12809e816f0d · outbound

This paper cites Machine learning search for sta- ble binary Sn alloys with Na, Ca, Cu, Pd, and Ag.

Machine learning potentials for modeling alloys across compositions Machine learning search for sta- ble binary Sn alloys with Na, Ca, Cu, Pd, and Ag

Reference 5

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Machine learning potentials for modeling alloys across compositions Unresolved cited work

Reference 6

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Observation d4ebada1-2f50-43c9-b833-8503c1236ad0 · outbound

This paper cites Roadmap for the de- velopment of machine learning-based interatomic poten- tials.

Machine learning potentials for modeling alloys across compositions Roadmap for the de- velopment of machine learning-based interatomic poten- tials

Reference 7

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Observation c7782a4c-7779-4e84-bcbe-05b96d588642 · outbound

This paper cites Quantifying chemical short-range order in metallic alloys.

Machine learning potentials for modeling alloys across compositions Quantifying chemical short-range order in metallic alloys

Reference 8

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This paper cites General-purpose machine-learned potential for 16 elemental metals and their alloys.

Machine learning potentials for modeling alloys across compositions General-purpose machine-learned potential for 16 elemental metals and their alloys

Reference 9

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This paper cites Multifunc- tional high-entropy materials.

Machine learning potentials for modeling alloys across compositions Multifunc- tional high-entropy materials

Reference 11

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This paper cites High-entropy alloys.

Machine learning potentials for modeling alloys across compositions High-entropy alloys

Reference 12

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Observation 7a33e611-353f-4072-986f-d711dbe39958 · outbound

This paper cites High- entropy ceramics.

Machine learning potentials for modeling alloys across compositions High- entropy ceramics

Reference 13

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Machine learning potentials for modeling alloys across compositions Unresolved cited work

Reference 15

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This paper cites Nonequilibrium chemical short-range order in metallic alloys.

Machine learning potentials for modeling alloys across compositions Nonequilibrium chemical short-range order in metallic alloys

Reference 16

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Observation 74cce05e-bf16-4064-a58e-aab1a69f83c7 · outbound

This paper cites Simultaneous Discovery of Reaction Co- ordinates and Committor Functions Using Equivariant Graph Neural Networks.

Machine learning potentials for modeling alloys across compositions Simultaneous Discovery of Reaction Co- ordinates and Committor Functions Using Equivariant Graph Neural Networks

Reference 17

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This paper cites Scaling deep learning for materials discovery.

Machine learning potentials for modeling alloys across compositions Scaling deep learning for materials discovery

Reference 18

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This paper cites MLIP-3: Active learning on atomic environments with Moment Tensor Potentials.

Machine learning potentials for modeling alloys across compositions MLIP-3: Active learning on atomic environments with Moment Tensor Potentials

Reference 19

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Observation 8fbcae9b-0de7-4b38-b962-2769ff38f9ae · outbound

This paper cites Single- model uncertainty quantification in neural network po- tentials does not consistently outperform model ensem- bles.

Machine learning potentials for modeling alloys across compositions Single- model uncertainty quantification in neural network po- tentials does not consistently outperform model ensem- bles

Reference 20

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Observation d6686851-56c3-4e8e-a2bf-a06365faa81a · outbound

This paper cites Banerjee,Electronic structure prediction of medium and high entropy alloys across composition space, 2024,url:https://arxiv.org/abs/2410.08294.

Machine learning potentials for modeling alloys across compositions Banerjee,Electronic structure prediction of medium and high entropy alloys across composition space, 2024,url:https://arxiv.org/abs/2410.08294

Reference 21

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Observation bf6f2ee8-4d08-4cf0-a9bc-706c0b1f8023 · outbound

This paper cites Active learning of reac- tive Bayesian force fields applied to heterogeneous catal- ysis dynamics of H/Pt.

Machine learning potentials for modeling alloys across compositions Active learning of reac- tive Bayesian force fields applied to heterogeneous catal- ysis dynamics of H/Pt

Reference 22

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This paper cites A foundation model for atomistic materials chemistry.

Machine learning potentials for modeling alloys across compositions A foundation model for atomistic materials chemistry

Reference 23

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This paper cites Special quasirandom structures.

Machine learning potentials for modeling alloys across compositions Special quasirandom structures

Reference 24

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This paper cites Efficient stochastic generation of Special Quasirandom Structures.

Machine learning potentials for modeling alloys across compositions Efficient stochastic generation of Special Quasirandom Structures

Reference 25

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Observation 33bc389a-7ce6-46a4-93ed-f0b5a774f72c · outbound

This paper cites Performant imple- mentation of the atomic cluster expansion (PACE) and application to copper and silicon.

Machine learning potentials for modeling alloys across compositions Performant imple- mentation of the atomic cluster expansion (PACE) and application to copper and silicon

Reference 26

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This paper cites Some Alloys for Use at High Temperatures. Part IV.

Machine learning potentials for modeling alloys across compositions Some Alloys for Use at High Temperatures. Part IV

Reference 27

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Observation f5082810-dc24-41dd-8e98-b70e4067d8fb · outbound

This paper cites Characteristics of the Equilibrium Diagram and Processes of Solution and Pre- cipitation in the Cr-Ni System.

Machine learning potentials for modeling alloys across compositions Characteristics of the Equilibrium Diagram and Processes of Solution and Pre- cipitation in the Cr-Ni System

Reference 28

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This paper cites Adiabatic High Temperature Calorimeter for Measurement of Heats of Alloying.

Machine learning potentials for modeling alloys across compositions Adiabatic High Temperature Calorimeter for Measurement of Heats of Alloying

Reference 29

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Observation 7049e1d5-71b0-4a45-b759-b55dedd57e22 · outbound

This paper cites X-Ray Studies on the Nickel-Chromium System.

Machine learning potentials for modeling alloys across compositions X-Ray Studies on the Nickel-Chromium System

Reference 30

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This paper cites The Constitution of Nickel- Rich Alloys of the Ni-Cr-Ti System.

Machine learning potentials for modeling alloys across compositions The Constitution of Nickel- Rich Alloys of the Ni-Cr-Ti System

Reference 31

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This paper cites Activities and Phase Boundaries in the Cr-Ni System Using a Solid Electrolyte Technique.

Machine learning potentials for modeling alloys across compositions Activities and Phase Boundaries in the Cr-Ni System Using a Solid Electrolyte Technique

Reference 32

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This paper cites Remarks on the Ordering in the Ni-Cr Al- loys in the Vicinity of Ni 2Cr.

Machine learning potentials for modeling alloys across compositions Remarks on the Ordering in the Ni-Cr Al- loys in the Vicinity of Ni 2Cr

Reference 33

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Observation dee6aa94-d70a-41a8-8cb0-1691c3f61591 · outbound

This paper cites Superstructure and K-State in the Cr-Ni Sys- tem.

Machine learning potentials for modeling alloys across compositions Superstructure and K-State in the Cr-Ni Sys- tem

Reference 34

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This paper cites The Cr-Ni (Chromium-Nickel) system.

Machine learning potentials for modeling alloys across compositions The Cr-Ni (Chromium-Nickel) system

Reference 35

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This paper cites On the Properties of the Co-Cr Binary System.

Machine learning potentials for modeling alloys across compositions On the Properties of the Co-Cr Binary System

Reference 36

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Observation ef694221-97f3-4cf4-ad0d-e3c9da22bd2b · outbound

This paper cites On the Equilibrium Diagram of the Cobalt-Chromium System.

Machine learning potentials for modeling alloys across compositions On the Equilibrium Diagram of the Cobalt-Chromium System

Reference 37

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Machine learning potentials for modeling alloys across compositions Mobility of Interphase Boundary in Metals

Reference 38

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This paper cites Co- Cr Binary System: Experimental Re-Determination of the Phase Diagram and Comparison with the Diagram Cal- culated from the Thermodynamic Data.

Machine learning potentials for modeling alloys across compositions Co- Cr Binary System: Experimental Re-Determination of the Phase Diagram and Comparison with the Diagram Cal- culated from the Thermodynamic Data

Reference 39

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This paper cites A Study of the Range of Stability ofσPhase in Some Ternary Systems.

Machine learning potentials for modeling alloys across compositions A Study of the Range of Stability ofσPhase in Some Ternary Systems

Reference 40

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This paper cites Computer Cal- culation of Phase Diagrams of Co-Cr and Co-Mn Sys- tems.

Machine learning potentials for modeling alloys across compositions Computer Cal- culation of Phase Diagrams of Co-Cr and Co-Mn Sys- tems

Reference 41

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This paper cites The Co-Cr (Cobalt- Chromium) System.

Machine learning potentials for modeling alloys across compositions The Co-Cr (Cobalt- Chromium) System

Reference 42

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This paper cites Johansson and J.O.

Machine learning potentials for modeling alloys across compositions Johansson and J.O

Reference 43

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This paper cites X-Ray Examination of Al- loys of the Gold-Platinum System.

Machine learning potentials for modeling alloys across compositions X-Ray Examination of Al- loys of the Gold-Platinum System

Reference 44

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This paper cites The Gold-Platinum System.

Machine learning potentials for modeling alloys across compositions The Gold-Platinum System

Reference 45

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Observation cdc29f85-075c-46b1-9a73-37dbfcc1378a · outbound

This paper cites Precipita- tion in Gold-Platinum Alloys.

Machine learning potentials for modeling alloys across compositions Precipita- tion in Gold-Platinum Alloys

Reference 46

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This paper cites The System Gold-Platinum- Rhodium and the Binary Systems of Its Components.

Machine learning potentials for modeling alloys across compositions The System Gold-Platinum- Rhodium and the Binary Systems of Its Components

Reference 47

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This paper cites Two-Phase Boundary of the Au-Pt Sys- tem.

Machine learning potentials for modeling alloys across compositions Two-Phase Boundary of the Au-Pt Sys- tem

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.730132Z

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This paper cites Separation Curve and Criti- cal Point of the System Gold-Platinum.

Machine learning potentials for modeling alloys across compositions Separation Curve and Criti- cal Point of the System Gold-Platinum

Reference 49

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Observation bcf0c40c-d781-434a-984d-28a72dc54bac · outbound

This paper cites The Au-Pt (Gold- Platinum) System.

Machine learning potentials for modeling alloys across compositions The Au-Pt (Gold- Platinum) System

Reference 50

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Observation 14243a6b-9f8a-46f1-9bb1-848bca2433bd · outbound

This paper cites Modelling of Ni-Cr-Mo Based Alloys: Part I - Phase Stability.

Machine learning potentials for modeling alloys across compositions Modelling of Ni-Cr-Mo Based Alloys: Part I - Phase Stability

Reference 51

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Observation f30df606-a5ef-4388-882f-817dcf568ae1 · outbound

This paper cites Co-Cr (Cobalt-Chromium).

Machine learning potentials for modeling alloys across compositions Co-Cr (Cobalt-Chromium)

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.684011Z

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Observation f4a57767-235b-491b-80ed-b66aa4e9c728 · outbound

This paper cites Au-Pt (Gold- Platinum).

Machine learning potentials for modeling alloys across compositions Au-Pt (Gold- Platinum)

Reference 53

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Observation 8fb4ce73-9af0-45e5-8adf-6a6e6f6b0b49 · outbound

This paper cites Multi-cell Monte Carlo method for phase prediction.

Machine learning potentials for modeling alloys across compositions Multi-cell Monte Carlo method for phase prediction

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.654334Z

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Observation 8aea1521-d8a4-444b-90eb-c6d7eb5d0e6f · outbound

This paper cites Melting line of aluminum from simulations of coexist- ing phases.

Machine learning potentials for modeling alloys across compositions Melting line of aluminum from simulations of coexist- ing phases

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.639413Z

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This paper cites Quantitative phase-field model of alloy solidifica- tion.

Machine learning potentials for modeling alloys across compositions Quantitative phase-field model of alloy solidifica- tion

Reference 56

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Observation 2be2a106-6314-4dcf-8c3e-a6f5986ff79d · outbound

This paper cites Temperature dependence of the mechanical properties of equiatomic solid solution alloys with face- centered cubic crystal structures.

Machine learning potentials for modeling alloys across compositions Temperature dependence of the mechanical properties of equiatomic solid solution alloys with face- centered cubic crystal structures

Reference 57

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This paper cites Optimization of conflicting properties via en- gineering compositional complexity in refractory high en- tropy alloys.

Machine learning potentials for modeling alloys across compositions Optimization of conflicting properties via en- gineering compositional complexity in refractory high en- tropy alloys

Reference 58

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Observation 084acc82-75ce-4d61-a5b5-5559cd351b0f · outbound

This paper cites Short-Range Order and Atomic Displacements in Ni–20 at% Cr Single Crystals.

Machine learning potentials for modeling alloys across compositions Short-Range Order and Atomic Displacements in Ni–20 at% Cr Single Crystals

Reference 59

Resolution: verified exact; doi, observed 2026-08-07T00:51:21.611513Z

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Observation 6616dac4-e834-46bc-8f25-46d5b1579fd6 · outbound

This paper cites X-ray study of diffuse scat- tering in Ni–20 at% Cr.

Machine learning potentials for modeling alloys across compositions X-ray study of diffuse scat- tering in Ni–20 at% Cr

Reference 60

Resolution: verified exact; doi, observed 2026-08-07T00:51:21.595223Z

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Observation afb6b391-0cb9-4f4c-a191-1dcc0901da3f · outbound

This paper cites In situdiffuse scattering of neu- trons in alloys and application to phase diagram determi- nation.

Machine learning potentials for modeling alloys across compositions In situdiffuse scattering of neu- trons in alloys and application to phase diagram determi- nation

Reference 61

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This paper cites Analysis of short-range order in Cu3Au using X-ray pair distribution functions.

Machine learning potentials for modeling alloys across compositions Analysis of short-range order in Cu3Au using X-ray pair distribution functions

Reference 62

Resolution: verified exact; doi, observed 2026-08-07T00:51:21.567864Z

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Observation 870d7fda-085d-4415-94e1-c94e786c2f86 · outbound

This paper cites Non- local First-Principles Calculations in Cu-Au and Other In- termetallic Alloys.

Machine learning potentials for modeling alloys across compositions Non- local First-Principles Calculations in Cu-Au and Other In- termetallic Alloys

Reference 63

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Observation a51a8f83-6ef1-40f9-8c74-d8c79d6e2509 · outbound

This paper cites Density functional theory is straying from the path toward the exact functional.

Machine learning potentials for modeling alloys across compositions Density functional theory is straying from the path toward the exact functional

Reference 64

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This paper cites Assessing the performance of recent density functionals for bulk solids.

Machine learning potentials for modeling alloys across compositions Assessing the performance of recent density functionals for bulk solids

Reference 65

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This paper cites Thermophysical properties of Ni-containing single-phase concentrated solid solution al- loys.

Machine learning potentials for modeling alloys across compositions Thermophysical properties of Ni-containing single-phase concentrated solid solution al- loys

Reference 66

Resolution: verified exact; doi, observed 2026-08-07T00:51:21.517536Z

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This paper cites an unresolved cited work.

Machine learning potentials for modeling alloys across compositions Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:51:22.623570Z

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Observation 6b588bc6-82c5-4a1d-b662-c0d5492c2f17 · outbound

This paper cites An Approximate Theory of Order in Al- loys.

Machine learning potentials for modeling alloys across compositions An Approximate Theory of Order in Al- loys

Reference 68

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This paper cites Data-driven simulation and charac- terisation of gold nanoparticle melting.

Machine learning potentials for modeling alloys across compositions Data-driven simulation and charac- terisation of gold nanoparticle melting

Reference 69

Resolution: verified exact; doi, observed 2026-08-07T00:51:21.488456Z

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Observation e99a75ca-19a5-4b22-ba82-3253a3f91a93 · outbound

This paper cites Composition-dependent transformation-induced plastic- ity in Co-based complex concentrated alloys.

Machine learning potentials for modeling alloys across compositions Composition-dependent transformation-induced plastic- ity in Co-based complex concentrated alloys

Reference 70

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This paper cites Exceptional fracture toughness of CrCoNi-based medium- and high- entropy alloys at 20 kelvin.

Machine learning potentials for modeling alloys across compositions Exceptional fracture toughness of CrCoNi-based medium- and high- entropy alloys at 20 kelvin

Reference 71

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T00:51:22.608940Z

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Observation 898b027a-0855-45f8-a27e-d568a1c30d8d · outbound

This paper cites Stacking fault energy in concentrated al- loys.

Machine learning potentials for modeling alloys across compositions Stacking fault energy in concentrated al- loys

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.594586Z

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Observation d3c4743f-4705-4ad1-937e-c605c88a43cb · outbound

This paper cites Tunable stacking fault energies by tailoring local chem- ical order in CrCoNi medium-entropy alloys.

Machine learning potentials for modeling alloys across compositions Tunable stacking fault energies by tailoring local chem- ical order in CrCoNi medium-entropy alloys

Reference 73

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This paper cites Complex Strengthening Mechanisms in the NbMoTaW Multi-Principal Element Alloy.

Machine learning potentials for modeling alloys across compositions Complex Strengthening Mechanisms in the NbMoTaW Multi-Principal Element Alloy

Reference 74

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Observation 68b17cb5-b3d6-4554-b05b-d22feda7f45c · outbound

This paper cites Multi-scale investigation of short- range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys.

Machine learning potentials for modeling alloys across compositions Multi-scale investigation of short- range order and dislocation glide in MoNbTi and TaNbTi multi-principal element alloys

Reference 75

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Observation 985fef3f-5322-49fb-b7d1-e4a3b09990e1 · outbound

This paper cites Atomistic Simulations of Dislocation Mobility in Refractory High-Entropy Alloys and the Effect of Chemical Short-Range Order.

Machine learning potentials for modeling alloys across compositions Atomistic Simulations of Dislocation Mobility in Refractory High-Entropy Alloys and the Effect of Chemical Short-Range Order

Reference 76

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Observation c90e40fb-7c2c-4ad1-a948-78f13c45f4b8 · outbound

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

Machine learning potentials for modeling alloys across compositions Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.579749Z

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Observation 9a4d552b-6faf-41cd-9fde-5e2665e72664 · outbound

This paper cites Page 11 of 12.

Machine learning potentials for modeling alloys across compositions Page 11 of 12

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.563898Z

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Observation 6f851140-167b-4b6e-8265-83b939a5c511 · outbound

This paper cites Accelerating CALPHAD-based phase diagram predic- tions in complex alloys using universal machine learning potentials: Opportunities and challenges.

Machine learning potentials for modeling alloys across compositions Accelerating CALPHAD-based phase diagram predic- tions in complex alloys using universal machine learning potentials: Opportunities and challenges

Reference 79

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This paper cites Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G.

Machine learning potentials for modeling alloys across compositions Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith, Michael G

Reference 80

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This paper cites A generative model for inorganic materials design.

Machine learning potentials for modeling alloys across compositions A generative model for inorganic materials design

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-07T00:51:21.362139Z

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Observation 884f061b-7277-486f-8c89-31b5a2e17f67 · outbound

This paper cites Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approxima- tion.

Machine learning potentials for modeling alloys across compositions Accurate and Numerically Efficient r2SCAN Meta-Generalized Gradient Approxima- tion

Reference 82

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This paper cites CIDER: An Expres- sive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints.

Machine learning potentials for modeling alloys across compositions CIDER: An Expres- sive, Nonlocal Feature Set for Machine Learning Density Functionals with Exact Constraints

Reference 83

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Observation f2497a7b-9f9b-4ade-8bc1-8dbff7fb9c58 · outbound

This paper cites Training Machine-Learned Density Functionals on Band Gaps.

Machine learning potentials for modeling alloys across compositions Training Machine-Learned Density Functionals on Band Gaps

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.548256Z

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

Machine learning potentials for modeling alloys across compositions Can machines learn density functionals? Past, present, and future of ML in DFT

Reference 85

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This paper cites Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Struc- ture Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy.

Machine learning potentials for modeling alloys across compositions Data-Efficient Machine Learning Potentials from Transfer Learning of Periodic Correlated Electronic Struc- ture Methods: Liquid Water at AFQMC, CCSD, and CCSD(T) Accuracy

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:51:22.532443Z

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Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction Machine learning potentials for modeling alloys across compositions

Reference 2025

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Atomistic Modeling of Chemical Disorder in Materials: Bridging Classical Methods and AI-Assisted Approaches Machine learning potentials for modeling alloys across compositions

Reference 256

Resolution: verified exact; arxiv_id, observed 2026-05-20T08:38:09.272885Z

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