Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Molecular Models02:00

Molecular Models

37.7K
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
37.7K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

34.0K
VSEPR Theory for Determination of Electron Pair Geometries
34.0K
The Representativeness Heuristic02:13

The Representativeness Heuristic

15.8K
The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
15.8K
Molecular Orbital Theory II03:51

Molecular Orbital Theory II

18.9K
Molecular Orbital Energy Diagrams
18.9K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.1K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.1K
Molecular Orbital Theory I02:35

Molecular Orbital Theory I

31.6K
Overview of Molecular Orbital Theory
31.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Modular Self-Driving Laboratory for Automated Synthesis of CsPb(Cl/Br/I)<sub>3</sub> Perovskite Nanocrystals.

Nano letters·2025
Same author

MOF-ChemUnity: Literature-Informed Large Language Models for Metal-Organic Framework Research.

Journal of the American Chemical Society·2025
Same author

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery.

Machine learning: science and technology·2025
Same author

Connecting metal-organic framework synthesis to applications using multimodal machine learning.

Nature communications·2025
Same author

Periodic GFN1-xTB Tight Binding: A Generalized Ewald Partitioning Scheme for the Klopman-Ohno Function.

Journal of chemical theory and computation·2025
Same author

Assessment of fine-tuned large language models for real-world chemistry and material science applications.

Chemical science·2024

相关实验视频

Updated: May 25, 2025

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

309

在贝叶斯优化中对分子和材料的自适应表示.

Mahyar Rajabi-Kochi1, Negareh Mahboubi2, Aseem Partap Singh Gill1

  • 1Chemical Engineering & Applied Chemistry, University of Toronto Toronto Ontario M5S 3E5 Canada mohamad.moosavi@utoronto.ca.

Chemical science
|February 27, 2025
PubMed
概括

特性 适应贝叶斯优化 (FABO) 在优化过程中动态调整分子表示,优于固定方法和随机搜索材料发现. 这种方法在复杂的搜索空间中增强了自动发现.

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
  • 人工智能的人工智能

背景情况:

  • 贝叶斯优化 (BO) 对自动化材料发现至关重要,但严重依赖于有效的分子表示.
  • 专家或数据驱动方法选择的固定表示可能是不理想的或有偏见的,特别是对于具有有限数据的新任务.
  • 特征向量的完整性和紧性显著影响BO效率.

研究的目的:

  • 引入一个特征适应贝叶斯优化 (FABO) 框架,将动态特征选择集成到BO过程中.
  • 为了使BO能够在整个优化周期中调整材料表示,提高效率和准确性.
  • 在新型分子优化任务中解决固定表示的局限性.

主要方法:

  • 开发了FABO,一个框架,将特征选择与贝叶斯优化循环中的高斯过程集成在一起.
  • 在多个优化周期中动态调整的材料表示 (特征向量).
  • 评估了FABO的分子优化任务,包括发现金属有机框架 (MOF).

主要成果:

  • 与使用预定义特征空间的随机搜索和方法相比,FABO表现出更高的性能.
  • 适应性表示方法成功地确定了不同MOF发现任务的有效特征,具有不同的属性分布.
  • 对于已知的任务,FABO与人类的化学直觉保持一致,并证明对新的优化挑战具有强大作用.

更多相关视频

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

相关实验视频

Last Updated: May 25, 2025

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function
05:57

Author Spotlight: In Silico Creation and Impact of Carbonylated Amino Acids on Protein Structure and Function

Published on: April 26, 2024

309
Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids
08:21

Curation of Computational Chemical Libraries Demonstrated with Alpha-Amino Acids

Published on: April 13, 2022

2.6K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.0K

结论:

  • 在自动化发现中,FABO提供了一种强大的方法来导航复杂的材料搜索空间.
  • 动态特征适应对于优化贝叶斯优化性能至关重要,特别是当先前知识有限时.
  • 该框架提高了自动化材料发现活动的效率和可靠性.