Paper Citation Record · LEDGER

Accelerating Battery Material Optimization through iterative Machine Learning

As of 18 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.18162.

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

pith.paper-citation-record.v1
2505.18162 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

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measured 41 of 41 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

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

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 01e5ead2-f38a-4f26-b3dd-8f0fe343b5cd · outbound

This paper cites an unresolved cited work.

Accelerating Battery Material Optimization through iterative Machine Learning Unresolved cited work

Reference 1

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Observation 4c367ffe-efb5-4ce4-82e3-03710b0bdd07 · outbound

This paper cites Unravelling supply chain complexity in maintenance operations of battery production.

Accelerating Battery Material Optimization through iterative Machine Learning Unravelling supply chain complexity in maintenance operations of battery production

Reference 2

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Observation 897b3f4c-1b1f-4d9b-8d7c-4f54c78d81aa · outbound

This paper cites High-capacity oxide cathode beyond 300 mAh/g: focus review.

Accelerating Battery Material Optimization through iterative Machine Learning High-capacity oxide cathode beyond 300 mAh/g: focus review

Reference 3

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This paper cites Opportunities and challenges of lithium ion batteries in automotive applications.

Accelerating Battery Material Optimization through iterative Machine Learning Opportunities and challenges of lithium ion batteries in automotive applications

Reference 4

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This paper cites Nano-rods in Ni-rich layered cathodes for practical batteries.

Accelerating Battery Material Optimization through iterative Machine Learning Nano-rods in Ni-rich layered cathodes for practical batteries

Reference 5

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Accelerating Battery Material Optimization through iterative Machine Learning Unresolved cited work

Reference 6

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Accelerating Battery Material Optimization through iterative Machine Learning Unresolved cited work

Reference 7

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This paper cites Assessing the feasibility of the Inflation Reduction Act’s EV critical mineral targets.

Accelerating Battery Material Optimization through iterative Machine Learning Assessing the feasibility of the Inflation Reduction Act’s EV critical mineral targets

Reference 8

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This paper cites Critically assessing sodium-ion technology roadmaps and scenarios for techno -economic competitiveness against lithium -ion batteries.

Accelerating Battery Material Optimization through iterative Machine Learning Critically assessing sodium-ion technology roadmaps and scenarios for techno -economic competitiveness against lithium -ion batteries

Reference 9

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Accelerating Battery Material Optimization through iterative Machine Learning Unresolved cited work

Reference 10

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Observation 3f18cd64-cd20-47c2-aa03-a4a589626289 · outbound

This paper cites Materials and processing of lithium -ion battery cathodes.

Accelerating Battery Material Optimization through iterative Machine Learning Materials and processing of lithium -ion battery cathodes

Reference 11

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Observation 514a6d09-258f-4639-8bb5-ddb1dfd8d3e1 · outbound

This paper cites Human -and machine -centred designs of molecules and materials for sustainability and decarbonization.

Accelerating Battery Material Optimization through iterative Machine Learning Human -and machine -centred designs of molecules and materials for sustainability and decarbonization

Reference 12

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This paper cites Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing.

Accelerating Battery Material Optimization through iterative Machine Learning Optimizing high energy density sulfur cathodes: A multivariate approach to electrode formulation and processing

Reference 13

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This paper cites How to optimize materials and devices via design of experiments and machine learning: Demonstration using organic photovoltaics.

Accelerating Battery Material Optimization through iterative Machine Learning How to optimize materials and devices via design of experiments and machine learning: Demonstration using organic photovoltaics

Reference 14

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Observation a6f50c3e-f712-44b0-9ed7-a92afc00bcc1 · outbound

This paper cites Machine learning assisted synthesis of lithium -ion batteries cathode materials.

Accelerating Battery Material Optimization through iterative Machine Learning Machine learning assisted synthesis of lithium -ion batteries cathode materials

Reference 15

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Observation 9330ed20-49dc-4df3-95b1-6de7563c8c68 · outbound

This paper cites Maximizing the energy density and stability of Ni -rich layered cathode materials with multivalent dopants via machine learning.

Accelerating Battery Material Optimization through iterative Machine Learning Maximizing the energy density and stability of Ni -rich layered cathode materials with multivalent dopants via machine learning

Reference 16

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Observation adf27257-1595-402c-a312-51d2f5e93cd7 · outbound

This paper cites Prediction on Discharging Properties of Nickel –Manganese Materials for High‐Performance Sodium ‐Ion Batteries via Machine Learning Methods.

Accelerating Battery Material Optimization through iterative Machine Learning Prediction on Discharging Properties of Nickel –Manganese Materials for High‐Performance Sodium ‐Ion Batteries via Machine Learning Methods

Reference 17

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This paper cites Capacity prediction of K-ion batteries: a machine learning based approach for high throughput screening of electrode materials.

Accelerating Battery Material Optimization through iterative Machine Learning Capacity prediction of K-ion batteries: a machine learning based approach for high throughput screening of electrode materials

Reference 18

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This paper cites Ab Initio Design of Ni ‐Rich Cathode Material with Assistance of Machine Learning for High Energy Lithium‐Ion Batteries.

Accelerating Battery Material Optimization through iterative Machine Learning Ab Initio Design of Ni ‐Rich Cathode Material with Assistance of Machine Learning for High Energy Lithium‐Ion Batteries

Reference 19

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Observation ff43b275-9dff-46a4-bddb-51fdc314669c · outbound

This paper cites Demonstrating Linked Battery Data To Accelerate Knowledge Flow in Battery Science.

Accelerating Battery Material Optimization through iterative Machine Learning Demonstrating Linked Battery Data To Accelerate Knowledge Flow in Battery Science

Reference 20

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Observation 8266829f-3482-40dc-a98a-ebe7df7eba70 · outbound

This paper cites An autonomous laboratory for the accelerated synthesis of novel materials.

Accelerating Battery Material Optimization through iterative Machine Learning An autonomous laboratory for the accelerated synthesis of novel materials

Reference 21

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Observation eaa9a9fd-7adf-4093-863c-a15aa2efdde9 · outbound

This paper cites Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects.

Accelerating Battery Material Optimization through iterative Machine Learning Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects

Reference 22

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Accelerating Battery Material Optimization through iterative Machine Learning Inverse design of nanoporous crystalline reticular materials with deep generative models

Reference 23

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This paper cites Perspective: Materials informatics and big data: Realization of the “fourth paradigm.

Accelerating Battery Material Optimization through iterative Machine Learning Perspective: Materials informatics and big data: Realization of the “fourth paradigm

Reference 24

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Accelerating Battery Material Optimization through iterative Machine Learning Machine-learning-assisted materials discovery using failed experiments

Reference 25

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Accelerating Battery Material Optimization through iterative Machine Learning Machine learning for chemical reactivity: the importance of failed experiments

Reference 26

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Accelerating Battery Material Optimization through iterative Machine Learning Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design

Reference 27

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This paper cites Active learning platform for accelerating the search for high-voltage cathode materials in an extensive chemical space.

Accelerating Battery Material Optimization through iterative Machine Learning Active learning platform for accelerating the search for high-voltage cathode materials in an extensive chemical space

Reference 28

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Accelerating Battery Material Optimization through iterative Machine Learning A review of machine learning for the optimization of production processes

Reference 29

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This paper cites Machine learning -assisted multi -objective optimization of battery manufacturing from synthetic data generated by physics-based simulations.

Accelerating Battery Material Optimization through iterative Machine Learning Machine learning -assisted multi -objective optimization of battery manufacturing from synthetic data generated by physics-based simulations

Reference 30

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Accelerating Battery Material Optimization through iterative Machine Learning Battery production design using multi -output machine learning models

Reference 31

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Accelerating Battery Material Optimization through iterative Machine Learning Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis

Reference 32

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This paper cites Comparison of the structural and electrochemical properties of layered Li [NixCoyMnz] O2 (x= 1/3, 0.5, 0.6, 0.7, 0.8 and 0.85) cathode material for lithium-ion batteries.

Accelerating Battery Material Optimization through iterative Machine Learning Comparison of the structural and electrochemical properties of layered Li [NixCoyMnz] O2 (x= 1/3, 0.5, 0.6, 0.7, 0.8 and 0.85) cathode material for lithium-ion batteries

Reference 33

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Accelerating Battery Material Optimization through iterative Machine Learning Heuristic solution for achieving long -term cycle stability for Ni -rich layered cathodes at full depth of discharge

Reference 34

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This paper cites A review on cathode materials for advanced lithium ion batteries: microstructure designs and performance regulations.

Accelerating Battery Material Optimization through iterative Machine Learning A review on cathode materials for advanced lithium ion batteries: microstructure designs and performance regulations

Reference 35

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Observation ce65cac2-6072-4565-a6f2-292ecc1476b4 · outbound

This paper cites Impacts of variations in manufacturing parameters on performance of lithium-ion-batteries.

Accelerating Battery Material Optimization through iterative Machine Learning Impacts of variations in manufacturing parameters on performance of lithium-ion-batteries

Reference 36

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This paper cites Optimizing the Microstructure and Processing Parameters for Lithium‐Ion Battery Cathodes: A Use Case Scenario with a Digital Manufacturing Platform.

Accelerating Battery Material Optimization through iterative Machine Learning Optimizing the Microstructure and Processing Parameters for Lithium‐Ion Battery Cathodes: A Use Case Scenario with a Digital Manufacturing Platform

Reference 37

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This paper cites Unraveling and regulating superstructure domain dispersion in lithium - rich layered oxide cathodes for high stability and reversibility.

Accelerating Battery Material Optimization through iterative Machine Learning Unraveling and regulating superstructure domain dispersion in lithium - rich layered oxide cathodes for high stability and reversibility

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:18:34.278077Z

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Observation d5af597d-dca2-4feb-a924-ff6ecbe7ace0 · outbound

This paper cites Insight gained from using machine learning techniques to predict the discharge capacities of doped spinel cathode materials for lithium ‐ion batteries applications.

Accelerating Battery Material Optimization through iterative Machine Learning Insight gained from using machine learning techniques to predict the discharge capacities of doped spinel cathode materials for lithium ‐ion batteries applications

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:18:34.159613Z

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This paper cites Capacity prediction method of lithium -ion battery in production process based on eXtreme Gradient Boosting.

Accelerating Battery Material Optimization through iterative Machine Learning Capacity prediction method of lithium -ion battery in production process based on eXtreme Gradient Boosting

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:18:34.139375Z

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Observation d5aa0fed-6d9e-46a3-a952-22edd6184f75 · outbound

This paper cites State of health prediction for lithium-ion battery using a gradient boosting-based data-driven method.

Accelerating Battery Material Optimization through iterative Machine Learning State of health prediction for lithium-ion battery using a gradient boosting-based data-driven method

Reference 41

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No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.