A wide array of models, spanning from computationally expensive ab initio methods to a spectrum of force-field approaches, have been developed and employed to probe silica polymorphs and understand growth processes and atomic-level dynamical transitions in silica. However, the quest for a model capable of making accurate predictions with high computational efficiency for various silica polymorphs is still ongoing. Recent developments in short-range machine-learned models, such as GAP and NNPScan, have shown promise in providing reasonable descriptions of silica, but their computational cost remains high compared to force fields such as BKS which are based on simple interpretable functional forms. Here, we build on the recent success of our reinforcement learning (RL) workflow to derive a new set of optimal parameters for a promising short-range BKS-based model proposed by Soules. We use RL to navigate the eight-dimensional parameter space of the Soules potential using an experimental training data set that includes both local and global structural features from approximately 21 experimentally realized silica polymorphs, including high density phases and porous zeolites. We compare the performance of our machine-learned ML-Soules model with other high quality models including our recent machine-learned parametrization of BKS (ML-BKS), a machine-learned potential (GAP), as well as predictions of ab initio calculations with the highly fidelity SCAN functional. The ML-Soules accurately captures the relative energetic ordering of various polymorphs as well as their structural features at a significantly reduced computational expense. The ML-Soules model also reasonably captures the structure, density, and elastic constants of quartz, as well as metastable silica polymorphs. We further discuss the limitations of the Soules functional form and propose potential enhancements, including the incorporation of additional three-body terms and/or the utilization of different short-ranged functional forms to achieve greater accuracy for both global and local features in the modeling of silica while retaining low computational cost.

中文翻译：

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机器学习：二氧化硅的简单可解释短程电位


已经开发并采用了各种各样的模型，从计算成本高昂的从头到外法，以探测二氧化硅多晶型物并了解二氧化硅中的生长过程和原子级动力学转变。然而，对能够对各种二氧化硅多晶型物进行准确预测且计算效率高的模型的追求仍在进行中。短程机器学习模型（如 GAP 和 NNPScan）的最新发展在提供二氧化硅的合理描述方面显示出前景，但与 BKS 等基于简单可解释函数形式的力场相比，它们的计算成本仍然很高。在这里，我们基于强化学习 （RL） 工作流程最近的成功，为 Soules 提出的有前途的基于 BKS 的短程模型推导出一组新的最佳参数。我们使用 RL 来导航 Soules 势的八维参数空间，使用实验训练数据集，该数据集包括来自大约 21 个实验实现的二氧化硅多晶型物的局部和全局结构特征，包括高密度相和多孔沸石。我们将机器学习的 ML-Soules 模型的性能与其他高质量模型进行了比较，包括我们最近的机器学习 BKS 参数 （ML-BKS）、机器学习电位 （GAP），以及使用高保真度 SCAN 函数对 ab initio 计算的预测。ML-Soules 以显著降低的计算成本准确捕获了各种多晶型物的相对能量排序及其结构特征。ML-Soules 模型还合理地捕获了石英的结构、密度和弹性常数，以及亚稳态二氧化硅多晶型物。 我们进一步讨论了 Soules 泛函形式的局限性，并提出了潜在的增强功能，包括加入额外的三体项和/或使用不同的短程泛函形式，以便在二氧化硅建模中实现更高的全局和局部特征的准确性，同时保持较低的计算成本。