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通过深度学习预测晶体结构
Kevin Ryan1, Jeff Lengyel1, Michael Shatruk1
1Department of Chemistry and Biochemistry , Florida State University , Tallahassee , Florida 32306 , United States.
Journal of the American Chemical Society
|June 7, 2018
概括
深度神经网络分析晶体数据以根据它们的原子环境识别元素. 这种机器学习方法有助于预测新的材料组成,指导合成努力.
科学领域:
- 材料科学
- 计算化学
- 晶体学
背景情况:
- 晶体结构存储库包含大量的晶体学数据.
- 由于数据的规模和复杂性, 手动分析这些数据具有挑战性.
- 机器学习为晶体信息的自动化分析提供了潜力.
研究的目的:
- 应用深度神经网络 (DNN) 来分析晶体数据.
- 训练DNN模型根据它们的晶体环境来区分化学元素.
- 使用已知的结构模板预测形成新化合物的可能性.
主要方法:
- 使用多视角原子指纹作为DNN模型的输入.
- 在大约5万个晶体结构的数据集上训练神经网络.
- 根据结构模板预测新的化合物形成.
主要成果:
- DNN模型通过其晶体环境拓学成功区分了化学元素.
- 确定了结构相似的原子位点,揭示了与周期表相关的趋势.
- 该模型对未见的数据进行了高精度的预测,在前10个预测中发现了~30%.
结论:
- 开发的DNN方法有效地分析了晶体数据.
- 这种方法可以指导合成努力发现新材料,特别是复杂的多元系统.
- 这些发现突显了机器学习在材料发现方面的力量.
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