Solution-Phase Design of Emerging Nanomaterials

Emerging materials such as liquid metals, intermetallics, phase-change materials, and glassy chalcogenides provide unconventional properties to drive innovations in catalysis, biomedicine, photonics, and data processing. However, the same unique characteristics that enable these functionalities, such as dynamic surfaces, complex bonding, or amorphous structures, simultaneously present significant challenges in controlling their synthesis and tailoring them for specific applications. Solution-phase assembly of these materials from nanoscale building blocks offers a powerful avenue to overcome these barriers. This approach provides unprecedented nanoscale control and high flexibility to adjust composition, geometry, and surface, while also enabling advanced patterning technologies and providing compatibility with various substrates. This perspective highlights the transformative potential of solution-phase synthesis for the development of next-generation functional materials. More broadly, we showcase the avenue toward precise design of materials as well as accelerated materials discovery and optimization, particularly when integrated with modern machine learning-based algorithms. This has far-reaching implications, complementing and even replacing conventional fabrication methods as well as high-throughput materials screening and optimization.

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Publication Details

Journal
Chemistry of Materials
Published
2025-11-21
DOI
https://doi.org/10.1021/acs.chemmater.5c01948
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.52
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article

Solution-Phase Design of Emerging Nanomaterials

Maksym Yarema, Florian M. Schenk
1 citations
Chemistry of Materials
Machine Learning in Materials Science
0.52
article

Solution-Phase Design of Emerging Nanomaterials

Maksym Yarema, Florian M. Schenk
article en
1 citations

Abstract

Emerging materials such as liquid metals, intermetallics, phase-change materials, and glassy chalcogenides provide unconventional properties to drive innovations in catalysis, biomedicine, photonics, and data processing. However, the same unique characteristics that enable these functionalities, such as dynamic surfaces, complex bonding, or amorphous structures, simultaneously present significant challenges in controlling their synthesis and tailoring them for specific applications. Solution-phase assembly of these materials from nanoscale building blocks offers a powerful avenue to overcome these barriers. This approach provides unprecedented nanoscale control and high flexibility to adjust composition, geometry, and surface, while also enabling advanced patterning technologies and providing compatibility with various substrates. This perspective highlights the transformative potential of solution-phase synthesis for the development of next-generation functional materials. More broadly, we showcase the avenue toward precise design of materials as well as accelerated materials discovery and optimization, particularly when integrated with modern machine learning-based algorithms. This has far-reaching implications, complementing and even replacing conventional fabrication methods as well as high-throughput materials screening and optimization.

Chemistry of MaterialsVol. 37(23)
ETH Zurich (CH), Electronics Design (Estonia) (EE)
Openalex Percentile: Top 30%
Machine Learning in Materials Science
0.52
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Solution-Phase Design of Emerging Nanomaterials — Maksym Yarema, Florian M. Schenk · Chemistry of Materials (2025) | TGRS Research Map | TGRS