卷积神经网络机器学习架构的可能性和局限性,用于表征Achiral直角的状液晶
Rebecca Betts1, Ingo Dierking1
1Department of Physics and Astronomy, University of Manchester, Oxford Road, Manchester M139PL, UK. ingo.dierking@manchester.ac.uk.
Soft matter
|May 15, 2024
概括
机器学习准确地描述液晶相变,区分强度过渡,但在区分类似的流体相 (如SmA和SmB) 方面存在局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 物理化学 物理化学
- 数据科学数据科学数据科学
背景情况:
- 机器学习 (ML) 提供了强大的工具来表征材料和预测性能.
- 了解ML算法的能力和局限性对于其有效应用至关重要.
- 液晶表现出复杂的相位行为,难以分析.
研究的目的:
- 评估ML算法在区分各种液晶相中的有效性.
- 为了确定ML的局限性,特别是卷积神经网络 (CNN),在分析微妙的相位过渡.
主要方法:
- 研究了一种相序:同位素 - 流体质 A (SmA) - 六度质 B (SmB) - 软晶体 CrE - 晶体.
- 应用了ML算法,包括CNN,来分析这些相位过渡的数据.
- 专注于与最小的顺序变化之间的直角相之间的过渡.
主要成果:
- ML准确地区分了强的第一阶段过渡,包括液体到液晶和结晶.
- 从六边形SmB过渡到软晶体CRE,标志着从短距离到长距离的顺序的转变.
- 由于它们的相似短距离排序,CNN在区分流体SmA和六极式SmB相方面表现出局限性.
结论:
- ML算法有效地识别液晶中的不同相位过渡.
- 具有相似排序特征的相之间的微妙转换,如SmA和SmB,对当前的ML模型构成挑战.
- 需要进一步开发ML技术,以改善复杂的液晶相行为分析.
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