Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials DOI Creative Commons
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi

et al.

Advanced Engineering Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 18, 2025

Disordered structures, characterized by their lack of periodicity, present significant challenges in fields such as materials science and biology. Conventional methods often fall short capturing the intricate properties behaviors these complex systems. For example, prediction material amorphous polymers high‐entropy alloys has historically been inaccurate due to inherent disorder, which arises from probabilistic nature structural defects nonuniform atomic arrangements. However, rise machine learning (ML) offers a revolutionary approach understanding predicting behavior disordered materials. This perspective article explores how ML techniques, including neural networks generative models, provide unprecedented insights into with driving advances industries energy storage, drug discovery, engineering. By leveraging powerful algorithms, researchers can now predict properties, identify hidden patterns, accelerate discovery novel Case studies illustrate ability overcome data scarcity, enhance model reliability, enable real‐time analysis structures. While quality computational costs remain, integration traditional marks transformative leap our navigate landscape, setting stage for ground‐breaking discoveries.

Language: Английский

Continuous Plant-Based and Remote Sensing for Determination of Fruit Tree Water Status DOI Creative Commons
Alessandro Carella, Pedro Tomás Bulacio Fischer, Roberto Massenti

et al.

Horticulturae, Journal Year: 2024, Volume and Issue: 10(5), P. 516 - 516

Published: May 16, 2024

Climate change poses significant challenges to agricultural productivity, making the efficient management of water resources essential for sustainable crop production. The assessment plant status is crucial understanding physiological responses stress and optimizing practices in agriculture. Proximal remote sensing techniques have emerged as powerful tools non-destructive, efficient, spatially extensive monitoring status. This review aims examine recent advancements proximal methodologies utilized assessing status, consumption, irrigation needs fruit tree crops. Several proved useful continuous estimation but strong limitations terms spatial variability. On contrary, technologies, although less precise estimates, can easily cover from medium large areas with drone or satellite images. integration would definitely improve assessment, resulting higher accuracy by integrating temporal scales. paper consists three parts: first part covers current plant-based tools, second techniques, third includes an update on combined use two methodologies.

Language: Английский

Citations

9
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials DOI Creative Commons
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi

et al.

Advanced Engineering Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 18, 2025

Disordered structures, characterized by their lack of periodicity, present significant challenges in fields such as materials science and biology. Conventional methods often fall short capturing the intricate properties behaviors these complex systems. For example, prediction material amorphous polymers high‐entropy alloys has historically been inaccurate due to inherent disorder, which arises from probabilistic nature structural defects nonuniform atomic arrangements. However, rise machine learning (ML) offers a revolutionary approach understanding predicting behavior disordered materials. This perspective article explores how ML techniques, including neural networks generative models, provide unprecedented insights into with driving advances industries energy storage, drug discovery, engineering. By leveraging powerful algorithms, researchers can now predict properties, identify hidden patterns, accelerate discovery novel Case studies illustrate ability overcome data scarcity, enhance model reliability, enable real‐time analysis structures. While quality computational costs remain, integration traditional marks transformative leap our navigate landscape, setting stage for ground‐breaking discoveries.

Language: Английский

Citations

1