在DFT精度下缓慢灭,高压玻璃状B2O3
Debendra Meher1, Nikhil V S Avula1, Sundaram Balasubramanian1
1Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore 560064, India.
The Journal of chemical physics
|January 24, 2025
概括
一个新的机器学习潜力 (MLP) 准确地模拟了B2O3玻璃,从而实现了现实的模拟. 这种方法克服了传统方法的局限性,在高火率下揭示了结构文物.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 无机玻璃的准确建模需要精确的原子间相互作用,结构秩序的大型系统,以及缓慢的火率.
- 传统的分子动力学 (MD) 模拟,包括第一原理和力场方法,在同时满足这些标准方面面临挑战.
研究的目的:
- 为B2O3玻璃开发一种机器学习潜力 (MLP),有效地解决现有模拟技术的局限性.
- 为了能够准确地模拟玻璃的特性和结构,特别是关于火率和压力效应.
主要方法:
- 在量子密度函数理论 (DFT) 数据上训练的机器学习潜力 (MLP) 的开发,用于B2O3.3.
- 深潜分子动力学 (MD) 模拟利用开发的MLP.
- 将模拟结果与实验数据进行比较,包括状态方程,结构因子和高压行为.
主要成果:
- 开发的MLP准确地预测了B2O3玻璃的状态方程和密度,特别是缓慢的火速度.
- 模拟显示,火速度超过10^11 K/s在环境条件下引入结构工件.
- 来自模拟的压力依赖性结构因子与实验性X射线和中子散射数据有很好的一致性.
- 高压模拟准确地捕捉了和氧的各种协调几何形状.
结论:
- 机器学习潜力为模拟B2O3玻璃提供了强大而准确的方法,克服了传统MD方法的局限性.
- 该研究强调了火速对玻璃结构的关键影响,以及MLP在捕捉这些影响方面的有效性.
- 这些发现为在各种条件下对玻璃材料进行更可靠的计算研究提供了途径.
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