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Updated: Jun 6, 2025

Research and Development of High-performance Explosives

Research and Development of High-performance Explosives

Published on: February 20, 2016

机器学习模型用于高爆炸性晶体密度和性能

Jack V Davis¹, Frank W Marrs², Marc J Cawkwell³

¹High Explosives Science and Technology, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States.

Chemistry of materials : a publication of the American Chemical Society

|December 2, 2024

概括

机器学习模型现在可以高准确地预测爆炸性质,如密度和爆炸性能. 这加速了新能源材料的发现,通过计算选数百万种化合物.

科学领域:

材料科学材料科学材料科学
计算化学计算化学
机器学习机器学习

背景情况:

发现具有增强能量密度和性能的新爆炸物的发现已经停滞不前.
机器学习提供了一条途径,通过快速属性预测来加速新能源分子的识别.
合成的能量分子的大数据库对于训练准确的预测模型至关重要.

研究的目的:

开发精确的机器学习模型来预测能量材料的密度,爆炸速度和爆炸压力.
为了加速发现具有卓越性能特性的新爆炸物的发现.
为人工智能驱动的大量潜在能量化合物的选奠定基础.

主要方法:

组建了一个数据库,包含21,000个经过实验合成的能量分子.
使用电子结构和原子学模拟计算了爆炸速度和压力.
使用实验密度和计算性能指标训练的机器学习模型.
介绍了一个新的分子描述符,MolDensity,并分析了描述符的重要性.

主要成果:

开发了能够预测晶体密度,爆炸速度和爆炸压力的机器学习模型.
与以前的最先进模型相比,实现了对晶体密度预测的平方根平均误差20%的降低.
证明了在提高模型准确性方面,奇拉指定的 SMILES 字符串和 MolDensity 描述符的有效性.
确定了影响材料密度和性能的关键描述因素.

结论:

开发的机器学习模型为关键爆炸性质提供了廉价和高度准确的预测.
这些模型可以显著加快对大型化学空间 (>10^6化合物) 的选,以寻找高性能能量材料.
这项工作为未来人工智能驱动的先进能量材料的发现奠定了基础.

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