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arXiv 2209.02072cond-mat.soft
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粗粒化分辨率下含自由基聚合物电子性质的预测

Prediction of Electronic Properties of Radical-Containing Polymers at Coarse-Grained Resolutions

Riccardo Alessandri, Juan J. de Pablo

AI总结:

针对软电子材料多尺度电子性质预测计算成本高的问题,提出结合机器学习与粗粒化技术的方法,在保证精度的前提下替代传统回映射方法,加速多尺度工作流程并为相关粗粒化模型开发提供框架。

摘要:

软电子材料的性质取决于电子自由度与构象自由度在宽时空尺度范围内的耦合。要描述这类性质,需要多尺度方法,使其既能获取电子性质,又能对软材料的构象空间进行采样。理论上,这可以通过将充分构象采样所需的粗粒化(CG)方法,经由回映射到原子分辨率水平模型并重复进行量子化学计算,与构象平均的电子性质分布关联起来实现。然而,这类方法的计算需求阻碍了其在高通量计算机辅助软材料发现中的应用。在此,我们提出一种结合机器学习与CG技术的方法,可在不牺牲精度的前提下替代传统基于回映射的方法。我们针对一类新兴的软电子材料——即非共轭含自由基聚合物,一种有望用于全有机储能的材料——对该方法进行了说明。我们训练监督机器学习模型,以学习CG分辨率下电子性质对聚合物构象的依赖关系。随后,我们参数化保留电子结构信息的CG模型,模拟CG凝聚相,并仅通过CG自由度预测此类相的电子性质。我们通过将该方法与完整的基于回映射的方法进行比较来验证我们的方法,发现两种方法之间具有良好的一致性。这项工作证明了所提出的方法在加速多尺度工作流程方面的潜力,并为开发保留电子结构信息的CG模型提供了框架。

英文摘要:

The properties of soft electronic materials depend on the coupling of electronic and conformational degrees of freedom over a wide range of spatiotemporal scales. Description of such properties requires multiscale approaches capable of, at the same time, accessing electronic properties and sampling the conformational space of soft materials. This could in principle be realized by connecting the coarse-grained (CG) methodologies required for adequate conformational sampling to conformationally-averaged electronic property distributions via backmapping to atomistic-resolution level models and repeated quantum-chemical calculations. Computational demands of such approaches, however, have hindered their application in high-throughput computer-aided soft materials discovery. Here, we present a method that, combining machine learning and CG techniques, can replace traditional backmapping-based approaches without sacrificing accuracy. We illustrate the method for an emerging class of soft electronic materials, namely non-conjugated, radical-containing polymers, promising materials for all-organic energy storage. Supervised machine learning models are trained to learn the dependence of electronic properties on polymer conformation at CG resolutions. We then parametrize CG models that retain electronic structure information, simulate CG condensed phases, and predict the electronic properties of such phases solely from the CG degrees of freedom. We validate our method by comparing it against a full backmapping-based approach, and find good agreement between both methods. This work demonstrates the potential of the proposed method to accelerate multiscale workflows, and provides a framework for the development of CG models that retain electronic structure information.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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