机器学习是利用扰乱的神经网络潜力的凝聚相系统的电场响应
Kit Joll1, Philipp Schienbein2,3, Kevin M Rosso4
1Department of Physics and Astronomy and Thomas Young Centre, University College London, London, UK.
Nature communications
|September 18, 2024
概括
我们开发了扰乱神经网络潜在分子动力学 (PNNP MD) 来模拟凝聚物质如何与电场相互作用. 这种方法准确地模拟了水的介电性质,克服了传统技术的计算限制.
科学领域:
- 计算物理和化学 计算物理和化学
- 材料科学是一种材料科学.
- 凝聚物质物理学 凝聚物质物理学
背景情况:
- 凝结相与电场之间的相互作用对自然和技术过程至关重要.
- 这些相互作用的精确分子模拟在计算上昂贵,限制了当前的研究范围.
- 像ab-initio分子动力学 (AIMD) 这样的现有方法面临着重大的计算挑战.
研究的目的:
- 引入一种新的计算方法,扰乱神经网络潜在分子动力学 (PNNP MD),用于在电场下模拟系统.
- 扩展可访问的时间和长度尺度,用于涉及电场的分子动力学模拟.
- 为了能够对与外部电场相互作用的多种凝聚相系统进行准确的原子学的洞察.
主要方法:
- 扰乱神经网络潜在分子动力学 (PNNP MD) 的开发和应用.
- 通过在零场分子动力学数据上训练的两个神经网络,对介电性质的机器学习.
- 验证与ab-initio分子动力学 (AIMD) 进行准确性评估.
主要成果:
- PNNP MD准确地机器学习液态水的介电性质,包括放松动态,介电常数和取决于场的红外光谱.
- 该方法即使在强烈的电场强度 (约. 0.2 V Å−1) 的情况.
- 神经网络展示了可靠的外推能力,用于现场响应,仅在零场配置上进行训练.
结论:
- PNNP MD提供了一种计算效率高,准确的方法来模拟电场中的凝聚相系统.
- 该方法克服了传统AIMD的局限性,使得更大规模的模拟成为可能.
- PNNP MD是一种多功能,模块化和可改进的工具,用于获得对各种材料电场相互作用的原子学理解.
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