为聚合物开发可扩展和通用化的机器学习力场
Shaswat Mohanty1, James Stevenson2, Andrea R Browning1
1Schrödinger, Inc., Portland, OR, 97204, USA.
Scientific reports
|October 11, 2023
概括
一个新的充电递归神经网络 (QRNN) 模型准确地预测了聚合物的特性. 这种先进的模型使得比以前更大的系统能够进行稳定的分子动力学模拟.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 聚合物科学 聚合物科学
背景情况:
- 预测聚合物特性对于设计新材料至关重要.
- 经典力场经常与特定的聚合物系统 (如聚乙烯糖醇) 发生冲突.
- 对于先进的聚合物应用,需要精确的建模.
研究的目的:
- 为了开发一个更准确的潜在能量表面,用于聚合物模拟.
- 创建一个充电递归神经网络 (QRNN) 模型用于聚合物属性预测.
- 为了实现聚合物系统的大规模分子动力学模拟.
主要方法:
- 在较小的原子集群的DFT计算上训练一个QRNN模型.
- 利用主动学习来代地改进模型.
- 实施一种新的培训方法,采用半经验性的部分收费.
- 通过模拟乙烯糖醇寡合体来验证模型.
主要成果:
- 该QRNN模型很好地概括到更大的原子集群和更长的聚合物链.
- 该模型产生稳定的分子动力学模拟轨迹.
- 模拟的聚合物链动态与实验数据有很好的一致性.
- 该QRNN方法允许模拟比DFT大得多的系统.
结论:
- 开发的QRNN模型提供了比经典方法更准确的力场.
- 这项工作为聚合物的大规模分子模拟提供了一个有前途的方法.
- QRNN模型增强了针对特定应用量身定制聚合物分子的能力.
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