Paper Citation Record · LEDGER

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures

As of 13 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2411.13689.

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

pith.paper-citation-record.v1
2411.13689 v1

Coverage vector

measured 27 of 27 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

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External citation measurements

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Outbound references

Observation d1b006a9-a36d-4914-b6a7-06cbb918e9aa · outbound

This paper cites The fair guiding principles for scientific data management and stewardship.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures The fair guiding principles for scientific data management and stewardship

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This paper cites Community action on fair data will fuel a revolution in materials research.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Community action on fair data will fuel a revolution in materials research

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Fair data enabling new horizons for materials research

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This paper cites From raw data to fair data: the fairification workflow for health research.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures From raw data to fair data: the fairification workflow for health research

Reference 4

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Implemen- tation and relevance of fair data principles in biopharmaceutical r&d

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Ocean fair data services

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This paper cites Commen- tary: The materials project: A materials genome approach to accelerating materials innovation.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Commen- tary: The materials project: A materials genome approach to accelerating materials innovation

Reference 7

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This paper cites Materials design and discovery with high-throughput density functional theory: the open quan- tum materials database (oqmd).

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Materials design and discovery with high-throughput density functional theory: the open quan- tum materials database (oqmd)

Reference 8

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This paper cites The materials data facility: data services to advance materials science research.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures The materials data facility: data services to advance materials science research

Reference 9

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This paper cites Nomad: The fair concept for big data-driven materials science.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Nomad: The fair concept for big data-driven materials science

Reference 10

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Sim2ls: Fair simulation workflows and data

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This paper cites Materials Genome Initiative strategic plan.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Materials Genome Initiative strategic plan

Reference 12

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This paper cites Cost-benefit analysis for F AIR research data – Cost of not having F AIR research data.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Cost-benefit analysis for F AIR research data – Cost of not having F AIR research data

Reference 13

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This paper cites Active learning literature survey.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Active learning literature survey

Reference 14

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This paper cites On-the- fly closed-loop materials discovery via bayesian active learning.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures On-the- fly closed-loop materials discovery via bayesian active learning

Reference 15

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This paper cites On- the-fly autonomous control of neutron diffraction via physics-informed bayesian active learning.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures On- the-fly autonomous control of neutron diffraction via physics-informed bayesian active learning

Reference 16

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This paper cites Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Active learning guides discovery of a champion four-metal perovskite oxide for oxygen evolution electrocatalysis

Reference 17

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Neural network reactive force field for c, h, n, and o systems

Reference 18

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Active learning of uni- formly accurate interatomic potentials for materials simulation

Reference 19

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Uncertainty-driven dynamics for active learning of interatomic potentials

Reference 20

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Active learning and molecular dynamics simulations to find high melting temperature alloys

Reference 21

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This paper cites An active learning approach for the design of doped llzo ceramic garnets for battery applications.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures An active learning approach for the design of doped llzo ceramic garnets for battery applications

Reference 22

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures High entropy alloy melting point calculation, Mar 2020

Reference 23

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This paper cites Melting line of aluminum from simulations of coexisting phases.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Melting line of aluminum from simulations of coexisting phases

Reference 24

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Model interatomic potentials and lattice strain in a high- entropy alloy

Reference 25

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Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures The melting lines of model systems calculated from coexis- tence simulations

Reference 26

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This paper cites Active learning loop for meltheas using fair principles, Jul 2024.

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures Active learning loop for meltheas using fair principles, Jul 2024

Reference 27

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Pith citing papers

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