通过使用机器学习的原子间潜力,使得被冲击的能量材料能够准确地进行化学建模
Cong Huy Pham1, Nir Goldman1,2, Laurence E Fried1
1Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, USA.
The Journal of chemical physics
|February 13, 2026
概括
我们开发了一种机器学习潜力,可以模拟像1,3,5-triamino-2,4,6-trinitrobenzene (TATB) 这样的受到冲击的能量材料. 这种高效的方法在极端条件下提供了对复杂化学的洞察力,准确地复制实验数据.
科学领域:
- 计算化学是一种计算化学.
- 材料科学是一种材料科学.
- 化学动力学 化学动力学
背景情况:
- 有机材料的动态压缩涉及复杂的,多个时间尺度的反应.
- 在各种应用中,精确的能量材料模型,如在爆炸过程中使用的1,3,5-triamino-2,4,6-trinitrobenzene (TATB),至关重要.
- 现有的方法在极端条件下捕获复杂的化学物质时面临挑战.
研究的目的:
- 开发一个高效的机器学习潜力来模拟爆炸下的TATB.
- 建立一个强大的框架来建模冲击有机能量材料.
- 为了获得有关TATB在冲击压缩过程中的化学转换的详细见解.
主要方法:
- 利用切比舍夫多项式来构建一个机器学习潜力.
- 开发了一种用于生成多种训练数据的策略,以捕获复杂的TATB化学.
- 进行了大规模的,多纳秒模拟震惊的TATB.
主要成果:
- 机器学习潜力在各种热力学条件和其他爆炸物中表现出强大的可转移性.
- 模拟准确地复制了TATB状态数据的实验Hugoniot方程.
- 在冲击压缩后观察到富含的碳集群的快速形成.
结论:
- 开发的机器学习方法可以在极端条件下准确可靠地对有机材料进行化学建模.
- 这项研究为未来对受到冲击的高能材料的研究提供了坚实的框架.
- 这些发现为TATB和相关化合物的爆炸化学提供了详细的见解.
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