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Sample Preparation for Analysis: Advanced Techniques

Sample Preparation for Analysis: Advanced Techniques

Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
Acid digestion with strong acids is commonly used to dissolve inorganic materials that are insoluble (do not dissolve) in water. This method can be useful for...

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Updated: Jun 14, 2025

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Author Spotlight: Accelerating Discovery in Microporous Material Chemistry

Published on: October 6, 2023

积极学习以快速定位合成复杂化合物合金.

Nathan S Johnson¹, Aashwin Ananda Mishra¹, Dylan J Kirsch²

¹SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA.

Materials (Basel, Switzerland)

|August 29, 2024

概括

本研究引入了一种主动学习 (AL) 方法,以优化复杂薄膜合金的合成. 这种方法通过减少优化时间,显著加快了新先进材料的发现.

科学领域:

材料科学材料科学材料科学
化学工程是化学工程的重要组成部分.
计算材料科学科学计算材料科学

背景情况:

先进的材料合成越来越专注于复杂的组成,在实现精确的元素比率方面提出了重大挑战.
优化这些材料的合成参数是耗时的,并且随着构成复杂度的提高而变得更加困难.
当前的方法,即使是经验丰富的操作员,在合成参数相结合时也难以保持一致性,阻碍了快速的材料勘探.

研究的目的:

展示一种主动学习 (AL) 方法,以优化含有多达五个主要元素的薄膜合金的物理蒸汽沉积 (PVD) 合成.
为了比较高斯过程 (GP) 和基于随机森林 (RF) 的AL模型对材料合成优化的有效性.
探索使用预训练模型的转移学习的应用,以加快新材料组合的发现.

主要方法:

实施了积极学习 (AL) 框架,以指导物理蒸汽沉积 (PVD) 合成参数的优化.
在AL循环中使用高斯过程 (GP) 和随机森林 (RF) 机器学习模型来预测最佳合成条件.
研究了转移学习策略,利用在更简单 (三元,四元) 或相关组合上训练的模型,对复杂 (五元) 合金进行预训练模型.

主要成果:

性能最好的AL模型成功地在14次代内确定了目标金合金的合成参数.

关键词:

积极学习是积极学习.机器学习是机器学习.蒸汽沉积蒸汽沉积的情况

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随机森林和高斯过程模型在低维 (三元,四元) 系统上预训练时,与仅在五元数据上训练的模型相比,显示出更好的预测准确性.

样本与目标组合共享共同元素的预训练也产生了性能改进,展示了AL方法的适应性.

结论:

积极学习 (AL) 提供了一种强大而可适应的策略,以加速对复杂的薄膜合金进行物理蒸汽沉积 (PVD) 合成的优化.
转移学习的使用通过利用相关材料系统的知识,显著提高了AL模型的效率.
这种方法具有很大的潜力,可以加速对各种先进,复杂的材料的探索和发现.