Paper Citation Record · LEDGER

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

As of 5 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 2 inbound Pith citation observations for arXiv:2601.06820.

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

pith.paper-citation-record.v1
2601.06820 v2

Coverage vector

measured 56 of 56 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:21:47.658173Z

measured 58 of 58 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T10:56:35.079982Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:49:42.334844Z

Reference resolution

56 of 56 outbound references displayed

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External citation measurements

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Outbound references

Observation da8ca321-565c-4d61-a161-5e812202c2e8 · outbound

This paper cites Springer, 2016.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Springer, 2016

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.100709Z

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Observation d7d56381-1d08-4e05-9248-1c91efe913ed · outbound

This paper cites Active learn- ing in materials science with emphasis on adaptive sampling using uncertainties for targeted design.npj Computational Materials, 5(1):21, 2019.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Active learn- ing in materials science with emphasis on adaptive sampling using uncertainties for targeted design.npj Computational Materials, 5(1):21, 2019

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.107664Z

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Observation f65f09e2-2dea-4338-93ee-3f4219964135 · outbound

This paper cites an unresolved cited work.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.118162Z

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Observation a9194ed6-ba50-4c51-b0c1-9eef243badf0 · outbound

This paper cites Bayesian optimization algorithms for accelerator physics.Physical review accelerators and beams, 27(8):084801, 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Bayesian optimization algorithms for accelerator physics.Physical review accelerators and beams, 27(8):084801, 2024

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.132584Z

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Observation 3965c1da-23b2-4343-a075-ac245dc74b48 · outbound

This paper cites Sequential closed-loop bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery.Nature Chem- istry, 16(8):1286–1294, 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Sequential closed-loop bayesian optimization as a guide for organic molecular metallophotocatalyst formulation discovery.Nature Chem- istry, 16(8):1286–1294, 2024

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.141218Z

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Observation 517a439b-7652-4bca-a820-c62ce2af058d · outbound

This paper cites Best practices for multi- fidelity bayesian optimization in materials and molecular research.Nature Computational Science, pages 1–10, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Best practices for multi- fidelity bayesian optimization in materials and molecular research.Nature Computational Science, pages 1–10, 2025

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.146949Z

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Observation 5330b632-a51a-4959-bc15-1a53bd178bc2 · outbound

This paper cites Benchmarking the perfor- mance of bayesian optimization across multiple experimental materials science domains.npj Computational Materials, 7(1):188, 2021.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Benchmarking the perfor- mance of bayesian optimization across multiple experimental materials science domains.npj Computational Materials, 7(1):188, 2021

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.155663Z

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Observation 2fff765b-7d58-430e-9f17-0dde1e84f161 · outbound

This paper cites Active learning-guided accelerated discovery of ultra-efficient high-entropy thermoelectrics.Advanced Materials, page e15054, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Active learning-guided accelerated discovery of ultra-efficient high-entropy thermoelectrics.Advanced Materials, page e15054, 2025

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.169846Z

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Observation ecddac90-f142-436c-8e5f-66af3a285e23 · outbound

This paper cites Materials design with target-oriented bayesian optimization.npj Computational Materials, 11(1):209, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Materials design with target-oriented bayesian optimization.npj Computational Materials, 11(1):209, 2025

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.181567Z

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Observation 3c66c805-3988-496a-8ea6-3fcfcf5c77b5 · outbound

This paper cites A survey and benchmark of high-dimensional bayesian optimization of discrete sequences.Advances in Neural Information Processing Systems, 37:140478–140508, 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery A survey and benchmark of high-dimensional bayesian optimization of discrete sequences.Advances in Neural Information Processing Systems, 37:140478–140508, 2024

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.190501Z

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Observation 9cf8cf0c-c6ae-4db7-a1bb-4873a6bc1e15 · outbound

This paper cites Bias free multiobjective active learning for materials design and discovery.Nature communications, 12(1):2312, 2021.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Bias free multiobjective active learning for materials design and discovery.Nature communications, 12(1):2312, 2021

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.200673Z

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Observation 9a09f501-5868-476d-a972-3301e3a00792 · outbound

This paper cites Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via bayesian machine learning.Nature Communications, 16(1):6528, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Artificial-intelligence-guided design of ordered gas diffusion layers for high-performing fuel cells via bayesian machine learning.Nature Communications, 16(1):6528, 2025

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.208034Z

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Observation 7d572737-81a7-483c-ac2b-798f9069ffc6 · outbound

This paper cites Bayesian reaction optimization as a tool for chemical synthesis.Nature, 590(7844):89–96, 2021.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Bayesian reaction optimization as a tool for chemical synthesis.Nature, 590(7844):89–96, 2021

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.213366Z

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Observation 2dfa466c-54d9-4f34-b1e1-4cf13afbd6fb · outbound

This paper cites Increasing certainty in systems biology models using bayesian multimodel inference.Nature Communi- cations, 16(1):7416, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Increasing certainty in systems biology models using bayesian multimodel inference.Nature Communi- cations, 16(1):7416, 2025

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.223260Z

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Observation 6f934e15-0773-471c-a4e9-2c1144972203 · outbound

This paper cites Mlmd: a programming-free ai platform to predict and design materials.npj Computational Materials, 10(1):59, 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Mlmd: a programming-free ai platform to predict and design materials.npj Computational Materials, 10(1):59, 2024

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.234110Z

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Observation ef082ffd-c8f9-4ff5-9895-ad457d4d91b9 · outbound

This paper cites Efficient hyperparameter tuning for predicting student performance with bayesian optimization.Multimedia tools and applications, 83(17):52711–52735, 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Efficient hyperparameter tuning for predicting student performance with bayesian optimization.Multimedia tools and applications, 83(17):52711–52735, 2024

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.246664Z

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Observation 17c16570-1f29-4778-84c2-3a60c564207f · outbound

This paper cites A bayesian active learning platform for scalable combination drug screens.Nature Communications, 16(1):156, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery A bayesian active learning platform for scalable combination drug screens.Nature Communications, 16(1):156, 2025

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.255515Z

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Observation b57ce355-1626-4bb8-a127-f44aa0c3bf5e · outbound

This paper cites Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys.Materials & Design, 241:112921, 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Active learning accelerates the discovery of high strength and high ductility lead-free solder alloys.Materials & Design, 241:112921, 2024

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.261290Z

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Observation 491089b2-2ed4-40e6-91b1-b230b003db9f · outbound

This paper cites Botorch: A framework for efficient monte-carlo bayesian opti- mization.Advances in neural information processing systems, 33:21524–21538, 2020.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Botorch: A framework for efficient monte-carlo bayesian opti- mization.Advances in neural information processing systems, 33:21524–21538, 2020

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.266807Z

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Observation 2534e96e-3029-462a-9d2e-910d491877a2 · outbound

This paper cites Ae: A domain-agnostic platform for adaptive experimentation.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Ae: A domain-agnostic platform for adaptive experimentation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.275229Z

Source-reported events for the cited work

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Observation 7e511e20-2ad6-4a8d-b77b-a53b713793e3 · outbound

This paper cites GPyOpt: A bayesian optimization framework in python.http: //github.com/SheffieldML/GPyOpt, 2016.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery GPyOpt: A bayesian optimization framework in python.http: //github.com/SheffieldML/GPyOpt, 2016

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.285823Z

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Observation 48638ca4-e29c-44f0-9675-50862ead9490 · outbound

This paper cites A tutorial on multiobjective optimization: fun- damentals and evolutionary methods.Natural computing, 17(3):585–609, 2018.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery A tutorial on multiobjective optimization: fun- damentals and evolutionary methods.Natural computing, 17(3):585–609, 2018

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.291874Z

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Observation a3d1ef62-3e39-480f-872d-7a4181e4eb85 · outbound

This paper cites Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement.Advances in neural information processing systems, 34:2187–2200, 2021.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Parallel bayesian optimization of multiple noisy objectives with expected hypervolume improvement.Advances in neural information processing systems, 34:2187–2200, 2021

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.299123Z

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Observation 07d205ad-5dcb-476b-8c0f-6b3873bfe02d · outbound

This paper cites Accessed: 2025-10-30.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Accessed: 2025-10-30

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.304674Z

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Observation d67e02d5-3fa1-4c0e-a3e0-c2061fcc5f86 · outbound

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Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.314245Z

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Observation e7eb780e-a041-45e9-b090-94fcbc3778e3 · outbound

This paper cites Machine learning-engineered nanozyme system for synergistic anti-tumor ferroptosis/apoptosis therapy.Small, 21(5):2408750, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Machine learning-engineered nanozyme system for synergistic anti-tumor ferroptosis/apoptosis therapy.Small, 21(5):2408750, 2025

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.324423Z

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Observation 2d0e6a53-03ff-46e7-b186-34f1b6ea0773 · outbound

This paper cites Accelerated design of age-hardened mg-ca-zn alloys with enhanced mechanical properties via machine learning.Computational Materials Science, 249:113665, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Accelerated design of age-hardened mg-ca-zn alloys with enhanced mechanical properties via machine learning.Computational Materials Science, 249:113665, 2025

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.331075Z

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Observation b5ef3244-d264-4de8-8ba2-b8a68a2e9df1 · outbound

This paper cites Bayesian active learning for accelerated design of broadband polarization- insensitive metasurfaces.Intelligent Computing, 4:0135, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Bayesian active learning for accelerated design of broadband polarization- insensitive metasurfaces.Intelligent Computing, 4:0135, 2025

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.340988Z

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Unavailable: canonical work link unavailable.

Observation 2cc1da0b-02fc-48bd-817b-a95ecca59acf · outbound

This paper cites Active learning-based research of foaming agent for epb shield soil conditioning in gravel stratum.Measurement, 239:115509, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Active learning-based research of foaming agent for epb shield soil conditioning in gravel stratum.Measurement, 239:115509, 2025

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.352658Z

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Observation 6b846b1f-2712-461d-aa7b-b986d6e64a43 · outbound

This paper cites Spatial-adaptive active learning identifies ultra-durable and highly active catalysts for acidic oxygen evolution reaction.Science Bulletin, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Spatial-adaptive active learning identifies ultra-durable and highly active catalysts for acidic oxygen evolution reaction.Science Bulletin, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.364244Z

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Observation 83eb3b7c-d39b-4b74-8a1e-ddaf837e7e2f · outbound

This paper cites Self-driving laboratory for accelerated on-surface synthesis under ultrahigh vacuum.Nano Letters, 25(30):11609–11617, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Self-driving laboratory for accelerated on-surface synthesis under ultrahigh vacuum.Nano Letters, 25(30):11609–11617, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.369161Z

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Observation 330787df-fe3b-4853-9115-69a6f41d3a07 · outbound

This paper cites Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830, 2011.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830, 2011

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.380795Z

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Observation 857e76ac-fe1f-466b-960c-24467de8c35f · outbound

This paper cites The global optimization problem: an introduction.Towards Global Optimiation 2, pages 1–15, 1978.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery The global optimization problem: an introduction.Towards Global Optimiation 2, pages 1–15, 1978

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.389686Z

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Observation 079a18b7-8106-4e76-8aa6-ce656168fbe3 · outbound

This paper cites Springer science & business media, 2012.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Springer science & business media, 2012

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.394034Z

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Observation e18ab4df-4747-42a7-8a16-3af7274b5222 · outbound

This paper cites Comparison of multiobjective evolution- ary algorithms: Empirical results.Evolutionary computation, 8(2):173–195, 2000.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Comparison of multiobjective evolution- ary algorithms: Empirical results.Evolutionary computation, 8(2):173–195, 2000

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.400491Z

Source-reported events for the cited work

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Observation fb176c11-a4bc-484c-b7df-68af0cf7849b · outbound

This paper cites Scalable test problems for evolutionary multiobjective optimization.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Scalable test problems for evolutionary multiobjective optimization

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.405481Z

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Observation 5bb92f9e-d57d-4671-b858-c7b3fab4ffd9 · outbound

This paper cites A fast elitist non- dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery A fast elitist non- dominated sorting genetic algorithm for multi-objective optimization: Nsga-ii

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.420349Z

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Observation 6289a79d-47b7-4569-8269-279496a98588 · outbound

This paper cites an unresolved cited work.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.429065Z

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Observation ea3ef2e5-bdeb-4651-ab89-e3138df6ec80 · outbound

This paper cites How atomic bonding plays the hardness behavior in the al–co–cr–cu–fe–ni high entropy family.Small Science, 4(2):2300225, 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery How atomic bonding plays the hardness behavior in the al–co–cr–cu–fe–ni high entropy family.Small Science, 4(2):2300225, 2024

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Observation db8a2d73-9672-4ab9-b1c7-df70fc0f2dd5 · outbound

This paper cites Exploring high entropy alloys: a review on thermodynamic design and computational modeling strategies for advanced materials ap- plications.Heliyon, 10(22), 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Exploring high entropy alloys: a review on thermodynamic design and computational modeling strategies for advanced materials ap- plications.Heliyon, 10(22), 2024

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.440898Z

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Observation d2879f2b-7e84-4a10-a6e2-aa5fe9ace5eb · outbound

This paper cites Review on wear resistance of laser cladding high- entropy alloy coatings.Journal of Materials Research and Technology, 28:911–934, 2024.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Review on wear resistance of laser cladding high- entropy alloy coatings.Journal of Materials Research and Technology, 28:911–934, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.447144Z

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Observation 8bb9f3d6-69ef-442f-9434-0ae24c229d6e · outbound

This paper cites Hot-rolled al-added medium mn steel (fe-8mn-2.85 al-1si-0.2 c): Microstructural evolution and tensile behavior.Materialia, 29:101790, 2023.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Hot-rolled al-added medium mn steel (fe-8mn-2.85 al-1si-0.2 c): Microstructural evolution and tensile behavior.Materialia, 29:101790, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.452749Z

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Observation f71329f9-9272-4d56-a139-26ddbb5487b3 · outbound

This paper cites Stabilizing austenite via intercritical mn partitioning in a medium mn steel.Scripta Materialia, 225:115162, 2023.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Stabilizing austenite via intercritical mn partitioning in a medium mn steel.Scripta Materialia, 225:115162, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.460469Z

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Observation 5e653eb7-88e6-463d-a72a-67b61c5b7547 · outbound

This paper cites High dislocation density–induced large ductility in deformed and partitioned steels.Science, 357(6355):1029– 1032, 2017.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery High dislocation density–induced large ductility in deformed and partitioned steels.Science, 357(6355):1029– 1032, 2017

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.467683Z

Source-reported events for the cited work

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Observation c2d6e86f-bcda-41fa-9330-e7214128a93a · outbound

This paper cites Target- driven design of high strength yet corrosion resistant medium mn steel via interpretable machine-learning.Materials & Design, page 115217, 2025.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Target- driven design of high strength yet corrosion resistant medium mn steel via interpretable machine-learning.Materials & Design, page 115217, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.476423Z

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Observation 068d365d-859e-42ae-a5e7-5220440aa3c6 · outbound

This paper cites Materials informatics: Emergence to au- tonomous discovery in the age of ai.arXiv preprint arXiv:2601.00742, 2026.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Materials informatics: Emergence to au- tonomous discovery in the age of ai.arXiv preprint arXiv:2601.00742, 2026

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Observation 13fbc2ee-2787-43d6-8c9c-b1a7012d5f33 · outbound

This paper cites Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

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Observation 312923f5-abbd-4f35-9945-9f1c1b14044d · outbound

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Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.509704Z

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Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

Reference 49

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This paper cites Form >2, we use Monte Carlo approxima- tion: EHVI(x)≈ 1 S SX s=1 max(0,HV(F ∪ {f(s)(x)})−HV(F))(28) wheref (s)(x)are samples from the GP posterior.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Form >2, we use Monte Carlo approxima- tion: EHVI(x)≈ 1 S SX s=1 max(0,HV(F ∪ {f(s)(x)})−HV(F))(28) wheref (s)(x)are samples from the GP posterior

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.526885Z

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Unavailable: canonical work link unavailable.

Observation b8acef5e-10f1-44b1-87e2-814573ffe56f · outbound

This paper cites an unresolved cited work.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

Reference 51

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Observation c8a1d906-4b63-40c3-b35f-6ac8400b8ffb · outbound

This paper cites an unresolved cited work.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.564274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6631415-ce4b-4160-90c1-c04076c92396 · outbound

This paper cites fromM u l t i B g o l e a r nimportbgo # qNEHVI w i t h s i n g l e p o i n t s e l e c t i o n VS rec , improvements , i d x = bgo.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery fromM u l t i B g o l e a r nimportbgo # qNEHVI w i t h s i n g l e p o i n t s e l e c t i o n VS rec , improvements , i d x = bgo

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-03T11:21:47.588940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3c82a45d-23d6-4270-b387-14049f6a3058 · outbound

This paper cites an unresolved cited work.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

Reference 54

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Source-reported events for the cited work

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Observation 7d73a429-88f3-4171-ad69-38b5625b606e · outbound

This paper cites an unresolved cited work.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

Reference 55

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Observation 9a5a3f5a-bc9e-4162-a27d-72fb70019f0b · outbound

This paper cites an unresolved cited work.

Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery Unresolved cited work

Reference 56

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Pith citing papers

Observation db8f16ab-4910-484d-aaed-4071e229af09 · inbound

Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial cites this paper.

Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-10T01:18:57.261370Z

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Observation 0ebf69f1-7b22-4384-baf8-74febcf9c2dd · inbound

SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery cites this paper.

SVGym (SciVerseGym): An Environment for Reinforcement Learning and Bayesian Optimization in Crystal Discovery Bgolearn: a Unified Bayesian Optimization Framework for Accelerating Materials Discovery

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-07-10T01:18:57.261370Z

Source-reported events for the cited work

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