机器学习引导的自适应参数化,用于混合单原子/粗粒模型中的合术语,用于水性离子液体中的二氨酸自组装
Yang Ge1, Xueping Wang1, Qiang Zhu1
1Key Laboratory of Mesoscopic Chemistry of Ministry of Education, Institute of Theoretical and Computational Chemistry, School of Chemistry and Chemical Engineering, Nanjing University, Nanjing 210023, China.
Journal of chemical theory and computation
|September 19, 2023
概括
一种机器学习方法创建了一个混合原子和粗粒模型来预测的自我组装. 这种方法显著降低了计算成本,并准确地模拟了离子液体度如何影响甲纳米结构的形成.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 生物物理学的生物物理.
背景情况:
- 预测的自我组装成纳米结构至关重要,但计算成本昂贵.
- 现有的方法在的灵活性和高计算需求方面扎.
- 准确的建模需要复杂系统的高效仿真技术.
研究的目的:
- 为混合原子和粗粒度 (CG) 模型开发一种机器学习引导的自适应参数化方法.
- 为了研究二氨 (P) 在水性离子液体 (IL) 混合物中的自我组装.
- 为了降低与预测自组合相关的计算成本.
主要方法:
- 开发了一种混合模型,将二氨的联合原子 (UA) 与离子液 ([BMIM]+[BF4]-) 的可极化静电变量粗粒 (VaCG) 结合起来.
- 引入了虚拟网站 (VS) 来模拟UA和CG模型之间的范德瓦尔斯 (vdW) 相互作用.
- 采用机器学习引导的自适应参数化来自动优化模型参数.
主要成果:
- 与全原子 (AA) 模拟相比,混合分辨率模型显著降低了计算成本.
- 模拟准确地预测了微结构性质,如键和辐射分布函数.
- 离子液度的增加改变了氨从有序纤维素到分支结构和无形聚合物的自我组装.
结论:
- 在ML引导的自适应参数化策略是有效的开发准确和计算效率高的分子模型.
- 这种方法使得在像IL-水混合物这样的系统中研究复杂的自我组装过程成为可能.
- 这些发现表明,它可以广泛应用于建模聚合物,脂质双层和多糖体.
相关概念视频
Molecular Models
Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Protein Complex Assembly
Proteins can form homomeric complexes with another unit of the same protein or heteromeric complexes with different types. Most protein complexes self-assemble spontaneously via ordered pathways, while some proteins need assembly factors that guide their proper assembly. Despite the crowded intracellular environment, proteins usually interact with their correct partners and form functional complexes.
Many viruses self-assemble into a fully functional unit using the infected host cell to...
Many viruses self-assemble into a fully functional unit using the infected host cell to...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...


