BOXVIA: Bayesian optimization executable and visualizable application

Ishii, Akimitsu; Kamijyo, Ryunosuke; Yamanaka, Akinori; Yamamoto, Akiyasu

doi:10.1016/j.softx.2022.101019

Cited by 6 publications

(5 citation statements)

References 17 publications

(18 reference statements)

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“…A large value of this hyperparameter encourages exploration, whereas a smaller value encourages local optimization. [ 44 ] A total of four suggestions were generated for each iteration, two of which were generated with xi = 0.05, and the other two with xi = 0.9. Meanwhile, a control experiment was performed in every run, with the same composition, annealing conditions and environment to confirm the reproducibility of the automated operation (Figure S46, Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

Closed‐Loop Multi‐Objective Optimization for Cu–Sb–S Photo‐Electrocatalytic Materials’ Discovery

Bai,

Khoo,

I Made

et al. 2023

Advanced Materials

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Copper antimony sulphides are regarded as promising catalysts for photoelectrochemical water splitting because of their earth abundance and broad light absorption. The unique photoactivity of copper antimony sulphides is dependent on their various crystalline structures and atomic compositions. Here, we built a closed‐loop workflow that explores Cu‐Sb‐S compositional space to optimise its photoelectrocatalytic hydrogen evolution from water, by integrating a high‐throughput robotic platform, characterization techniques, and machine learning (ML) optimization workflow. The multiple‐objective optimization model discovered optimum experimental conditions after only nine cycles of integrated experiments‐machine learning loop. Photocurrent testing at 0 V versus reversible hydrogen electrode (RHE) confirmed the expected correlation between the materials properties and photocurrent. An optimum photocurrent of ‐186 µA cm−2 was observed on Cu‐Sb‐S in the ratio of 9:45:46 in the form of single‐layer coating on F‐doped SnO2 (FTO) glass with a corresponding bandgap of 1.85 eV and 63.2% Cu1+/Cu species content. The targeted intelligent search revealed a non‐obvious CuSbS composition that exhibited 2.3 times greater activity than baseline results from random sampling.This article is protected by copyright. All rights reserved

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Section: Resultsmentioning

confidence: 99%

Closed‐Loop Multi‐Objective Optimization for Cu–Sb–S Photo‐Electrocatalytic Materials’ Discovery

Bai,

Khoo,

I Made

et al. 2023

Advanced Materials

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“…Researchers could synthesize samples according to the proposed conditions, perform measurements, and update the database. In machine learning, this "data-driven loop" was repeated to perform a balanced global search and local fine-tuning using the customized software BOX-VIA 54 . Simultaneously, the researchers provided a general framework and experimental data for the machine learning and design processes ("researcher-driven loop").…”

Section: Process Designmentioning

confidence: 99%

“…These initial findings supported the data-driven process design, which was an aspect described in the Methods-Bayesian Optimization and Software section. Notably, these results led to the development of BOXVIA, a dedicated Bayesian optimization software 54 . The sample synthesis was guided by Bayesian recommendations for the process parameters.…”

Section: Process Designmentioning

confidence: 99%

“…1 Conceptual schematic of the complementary data-and researcher-driven process designs. The "data-driven" loop, which provides the synthesis conditions for Bulk1, is driven by machine learning using the tailored software BOXVIA [54]. The "researcher-driven" loop, which provides the synthesis conditions for Bulk2, is based on human experience and theory-based strategies.…”

Section: Critical Current Propertiesmentioning

confidence: 99%

“…The data-driven process design code for this study was integrated by Ishii et al as the BOXVIA software and is detailed previously 54 .…”

Section: Author Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

Yamamoto,

Tokuta,

Ishii

et al. 2024

NPG Asia Mater

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Iron-based high-temperature (high-Tc) superconductors have good potential to serve as materials in next-generation superstrength quasipermanent magnets owing to their distinctive topological and superconducting properties. However, their unconventional high-Tc superconductivity paradoxically associates with anisotropic pairing and short coherence lengths, causing challenges by inhibiting supercurrent transport at grain boundaries in polycrystalline materials. In this study, we employ machine learning to manipulate intricate polycrystalline microstructures through a process design that integrates researcher- and data-driven approaches via tailored software. Our approach results in a bulk Ba0.6K0.4Fe2As2 permanent magnet with a magnetic field that is 2.7 times stronger than that previously reported. Additionally, we demonstrate magnetic field stability exceeding 0.1 ppm/h for a practical 1.5 T permanent magnet, which is a vital aspect of medical magnetic resonance imaging. Nanostructural analysis reveals contrasting outcomes from data- and researcher-driven processes, showing that high-density defects and bipolarized grain boundary spacing distributions are primary contributors to the magnet’s exceptional strength and stability.

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Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2 polycrystalline bulk superconducting materials

Ishii

Kikuchi

Yamanaka

et al. 2023

Journal of Alloys and Compounds

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BOXVIA: Bayesian optimization executable and visualizable application

Cited by 6 publications

References 17 publications

Closed‐Loop Multi‐Objective Optimization for Cu–Sb–S Photo‐Electrocatalytic Materials’ Discovery

Closed‐Loop Multi‐Objective Optimization for Cu–Sb–S Photo‐Electrocatalytic Materials’ Discovery

Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design

Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2 polycrystalline bulk superconducting materials

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