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Imaging Biological Samples with Optical Microscopy

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Updated: Apr 25, 2026

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Published on: July 29, 2013

使用联合数据库和机器学习的光学材料发现和设计.

Victor Trinquet¹, Matthew L Evans^1,2, Cameron J Hargreaves¹

¹UCLouvain, Institute of Condensed Matter and Nanosciences (IMCN), Chemin des Étoiles 8, Louvain-la-Neuve 1348, Belgium. victor.trinquet@uclouvain.be.

Faraday discussions

|September 19, 2024

概括

这项研究使用机器学习和联合数据库来发现用于高折射率光学应用的新型无机材料. 该方法有效地选数百万个假设结构进行实验和理论研究.

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Design and Fabrication of an Optical Fiber Made of Water

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Published on: June 4, 2019

相关实验视频

Last Updated: Apr 25, 2026

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Fabrication and Characterization of Disordered Polymer Optical Fibers for Transverse Anderson Localization of Light

Published on: July 29, 2013

Design and Fabrication of an Optical Fiber Made of Water

Design and Fabrication of an Optical Fiber Made of Water

Published on: November 8, 2018

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

High-Throughput Analysis of Optical Mapping Data Using ElectroMap

Published on: June 4, 2019

科学领域:

材料科学材料科学材料科学
计算化学的计算化学
固态物理固态物理

背景情况:

使用密度函数理论等计算方法生成大量的假设无机材料.
这些材料越来越容易通过开放的数据库和标准化的API,如OPTIMADE API.
OPTIMADE联盟提供了超过3000万个晶体结构的访问权限,其中许多是新的,并通过机器学习识别.

研究的目的:

开发一种高效的工作流程,用于选假设无机材料的大型数据集.
确定下一代光学材料,特别是具有高折射率的光学材料.
为了利用自动计算,联合数据策划和机器学习来发现材料.

主要方法:

利用OPTIMADE API访问一个超过3000万个晶体结构的联合数据库.
应用了MODNet,一种神经网络模型,用于在主动学习框架内进行属性预测.
实施了高通量计算策略,结合了针对性选的积极学习.

主要成果:

成功分离了特定的结构和化学物质,这些物质有可能用于高折射率光学应用.
展示了一种有效的方法,用于非详尽地选动态和大型材料空间.
确定了进一步理论计算和实验验证的有前途的候选者.

结论:

提出的工作流有效地利用自动计算,联合数据集和机器学习来加速材料发现.
该方法具有适应性,可以随着新数据和数据库的可用性而定期重新评估.
这项工作通过将计算能力与数据联合相结合,促进了新型光学材料的发现.