Barium-induced effects on structure and properties of β-Ca3(PO4)2-type Ca9Bi(VO4)7

Dorbakov, Nikolai G.; Titkov, V. V.; Stefanovich, S. Yu.; Baryshnikova, Oksana V.; Морозов, В.А.; Belik, Alexei А.; Lazoryak, Bogdan I.

doi:10.1016/j.jallcom.2019.03.365

Cited by 8 publications

(3 citation statements)

References 38 publications

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“…Furthermore, the success rate of the recommendation strategy may be underestimated in some cases. For example, BaO is predicted as the Ba source for synthesizing Ca 7.5 Ba 1.5 Bi(VO 4 ) 7 , while BaCO 3 is used in the reported synthesis ( 57 ). Given the slight difference between BaO and BaCO 3 , BaO may actually be suitable.…”

Section: Discussionmentioning

confidence: 99%

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Huo

Bartel

et al. 2023

Sci. Adv.

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Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories.

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Section: Discussionmentioning

confidence: 99%

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Huo

Bartel

et al. 2023

Sci. Adv.

View full text Add to dashboard Cite

show abstract

“…Furthermore, the success rate of the recommendation strategy may be underestimated in some cases. For example, BaO is predicted as the Ba source for synthesizing Ca 7.5 Ba 1.5 Bi(VO 4 ) 7 , while BaCO 3 is used in the reported synthesis (50). Given the slight difference between BaO and BaCO 3 , BaO may actually be suitable.…”

Section: Discussionmentioning

confidence: 99%

Inorganic synthesis recommendation by machine learning materials similarity from scientific literature

He¹,

Huo²,

Bartel³

et al. 2023

Preprint

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Synthesis prediction is a key accelerator for the rapid design of advanced materials. However, determining synthesis variables such as the choice of precursor materials, operations, and conditions is challenging for inorganic materials because the sequence of reactions during heating is not well understood. In this work, we use a knowledge base of 29,900 solid-state synthesis recipes, text-mined from the scientific literature, to automatically learn which precursors to recommend for the synthesis of a novel target material. The data-driven approach learns chemical similarity of materials and refers the synthesis of a new target to precedent synthesis procedures of similar materials, mimicking human synthesis design. When proposing five precursor sets for each of 2,654 unseen test target materials, the recommendation strategy achieves a success rate of at least 82%. Our approach captures decades of heuristic synthesis data in a mathematical form, making it accessible for use in recommendation engines and autonomous laboratories. Short title AI learning inorganic synthesis from literature TeaserDecades of heuristic data are automatically captured for guiding successful synthesis of novel inorganic materials.

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“…Borates, [1][2][3] fluoroborates, [4][5][6] niobates, 7,8 phosphates, 9 sulfates, 10 selenates, 11 vanadates, and other crystals are currently popular for research, and among them vanadates are applied in catalysts, 12,13 dielectrics, 14,15 magnetism, 16,17 batteries, 18,19 optoelectronics, laser technology, 20,21 luminescent materials, and many other fields, such as fluorescent materials M 5 (VO 4 ) 3 Cl (M = Ca, Sr, Ba) 22 and Pb 5 (VO 4 ) 3 Cl, 23 birefringent materials YVO 4 24 and Rb 2 VO(O 2 ) 2 F, 25 and nonlinear optical (NLO) materials BiVO 4 26 and Ca 9 Bi(VO 4 ) 7 . 27 It follows that the development of new vanadate crystal structures is necessary for many frontier research areas.…”

Section: Introductionmentioning

confidence: 99%

Synthesis and characterization of Cs₂Zn₄V₄O₁₅—a new vanadate in the Cs–Zn–V–O quaternary system

Feng

Zuo

2023

New J. Chem.

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Barium-induced effects on structure and properties of β-Ca3(PO4)2-type Ca9Bi(VO4)7

Cited by 8 publications

References 38 publications

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Inorganic synthesis recommendation by machine learning materials similarity from scientific literature

Synthesis and characterization of Cs₂Zn₄V₄O₁₅—a new vanadate in the Cs–Zn–V–O quaternary system

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Barium-induced effects on structure and properties of β-Ca3(PO4)2-type Ca9Bi(VO4)7

Cited by 8 publications

References 38 publications

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

Inorganic synthesis recommendation by machine learning materials similarity from scientific literature

Synthesis and characterization of Cs2Zn4V4O15—a new vanadate in the Cs–Zn–V–O quaternary system

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Synthesis and characterization of Cs₂Zn₄V₄O₁₅—a new vanadate in the Cs–Zn–V–O quaternary system