Environmental Science Water Research & Technology, Год журнала: 2024, Номер 10(5), С. 1009 - 1018
Опубликована: Янв. 1, 2024
Managing drinking water-associated pathogens that can cause infections in immunocompromised individuals is a persistent challenge, particularly for healthcare facilities where occupant exposures carry substantial health risk.
Язык: Английский
Advanced Materials, Год журнала: 2024, Номер 36(18)
Опубликована: Янв. 19, 2024
Abstract Machine learning holds significant research potential in the field of nanotechnology, enabling nanomaterial structure and property predictions, facilitating materials design discovery, reducing need for time‐consuming labor‐intensive experiments simulations. In contrast to their achiral counterparts, application machine chiral nanomaterials is still its infancy, with a limited number publications date. This despite great advance development new sustainable high values optical activity, circularly polarized luminescence, enantioselectivity, as well analysis structural chirality by electron microscopy. this review, an methods used studying provided, subsequently offering guidance on adapting extending work nanomaterials. An overview within framework synthesis–structure–property–application relationships presented insights how leverage study these highly complex are provided. Some key recent reviewed discussed Finally, review captures achievements, ongoing challenges, prospective outlook very important field.
Язык: Английский
Процитировано
25
Advanced Materials, Год журнала: 2025, Номер unknown
Опубликована: Фев. 9, 2025
Machine learning is increasingly being applied in polymer chemistry to link chemical structures macroscopic properties of polymers and identify patterns the that help improve specific properties. To facilitate this, a dataset needs be translated into machine readable descriptors. However, limited inadequately curated datasets, broad molecular weight distributions, irregular configurations pose significant challenges. Most off shelf mathematical models often need refinement for applications. Addressing these challenges demand close collaboration between chemists mathematicians as must formulate research questions terms while are required refine This review unites both disciplines address curation hurdles highlight advances synthesis modeling enhance data availability. It then surveys ML approaches used predict solid-state properties, solution behavior, composite performance, emerging applications such drug delivery polymer-biology interface. A perspective field concluded importance FAIR (findability, accessibility, interoperability, reusability) integration theory discussed, thoughts on machine-human interface shared.
Язык: Английский
Процитировано
5
Critical Reviews in Oncology/Hematology, Год журнала: 2025, Номер unknown, С. 104701 - 104701
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
4
Macromolecules, Год журнала: 2024, Номер unknown
Опубликована: Авг. 9, 2024
Язык: Английский
Процитировано
7
MRS Communications, Год журнала: 2024, Номер 14(5), С. 752 - 770
Опубликована: Июль 1, 2024
Abstract Artificial intelligence and machine learning (ML) continue to see increasing interest in science engineering every year. Polymer is no different, though implementation of data-driven algorithms this subfield has unique challenges barring widespread application these techniques the study polymer systems. In Prospective, we discuss several critical ML science, including structure representation, high-throughput limitations, limited data availability. Promising studies targeting resolution issues are explored, contemporary research demonstrating potential despite existing obstacles discussed. Finally, present an outlook for moving forward. Graphical
Язык: Английский
Процитировано
4
Environmental Science Nano, Год журнала: 2024, Номер 11(7), С. 2885 - 2893
Опубликована: Янв. 1, 2024
There are numerous opportunities for nanomaterials and nanotechnology to support circular economy adoption. In this perspective, we present the important role engineered can play in advancing circularity of bulk composite materials.
Язык: Английский
Процитировано
4
Computational Materials Science, Год журнала: 2024, Номер 246, С. 113399 - 113399
Опубликована: Окт. 17, 2024
Язык: Английский
Процитировано
3
Advanced Materials, Год журнала: 2025, Номер unknown
Опубликована: Март 10, 2025
The traditional trial-and-error approach, although effective, is inefficient for optimizing rubber composites. latest developments in machine learning (ML)-assisted methodologies are also not suitable predicting and composite properties. This due to the dependency of properties on processing conditions, which prevents alignment data collected from different sources. In this work, a novel workflow called ML-enhanced approach proposed. integrates orthogonal experimental design with symbolic regression (SR) effectively extract empirical principles. combination enables optimization process retain characteristics while significantly improving efficiency capability. Using composites as model system, extracts principles encapsulated by high-frequency terms SR-derived mathematical formulas, offering clear guidance material property optimization. An online platform has been developed that allows no-code usage proposed methodology, designed seamlessly integrate into existing process.
Язык: Английский
Процитировано
0
Advanced Nanocomposites, Год журнала: 2025, Номер unknown
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0
Polymer Chemistry, Год журнала: 2025, Номер unknown
Опубликована: Янв. 1, 2025
AI-driven polymer science: a structured perspective on integrating machine learning for data analysis, property prediction, and automated research workflows.
Язык: Английский
Процитировано
0