神经网络动力学用于探索复杂成分材料中的扩散多重性和化学排序.
Bin Xing1,2, Timothy J Rupert1,2, Xiaoqing Pan1,2
1Center for Complex and Active Materials, University of California, Irvine, CA, USA.
我们开发了一个神经网络动力学 (NNK) 方案来模拟复杂材料中的原子扩散. 这种方法准确地预测了化学排序和结构形成,揭示了NbMoTa合金中最大B2排序的临界温度.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 化学物理 化学物理
背景情况:
- 原子扩散对于沉和相核化等物质过程至关重要.
- 在复杂的缩合金中模拟扩散是由于化学复杂性的挑战.
- 预测化学上有序的结构需要精确的模拟原子运输.
研究的目的:
- 引入一种新的计算框架来模拟复杂材料中的扩散.
- 准确预测扩散诱导的化学和结构演变.
- 探索耐火合金中的温度依赖的排序现象.
主要方法:
- 开发了一个神经网络动力学 (NNK) 方案来模拟原子扩散.
- 采用高效的网格结构和化学表现.
- 利用人工神经网络来预测迁移障碍和原子跳跃.
主要成果:
- 在NbMoTa合金中成功模拟了温度依赖的局部化学排序.
- 确定了NbMoTa合金中的B2顺序达到最大的临界温度.
- 在临界温度附近观察到最高的扩散异质性,与订单相关.
结论:
- 该NNK框架允许精确预测复杂合金中的扩散和排序.
- 扩散异质性在化学排序和B2结构形成中起着关键作用.
- 可扩展的NNK方法为发现新材料特性开辟了新的途径.
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