发表机构
Rutgers University; Quantum Informatics, LLC（罗格斯大学; 量子信息有限责任公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对 ReaxFF 参数优化中的高维、非凸和非连续难题，提出随机优化结合坐标搜索策略，在 Mo-S 体系实现超 80% 误差改进，并成功泛化至 W-S 体系。
AI 中文摘要
ReaxFF 是一种分子动力学方法，可视为量子方法的一个良好近似，用于研究由一万到十万个原子组成的反应性分子体系。虽然 ReaxFF 通常是比量子方法快得多的替代方案，但其力场每个元素包含近 100 个参数，这使得力场开发成为一个高维优化问题。除了高维性，非凸性和非连续性也使其成为一个难以优化的问题。我们采用随机优化结合坐标搜索策略进行高效优化，并采样新的参数点，这些参数点能产生接近从量子力学方法获得的预定义“参考值”的良好分子性质，针对 $\mathrm{Mo-S}$ 体系。我们还提供了从任意随机输入样本出发的经验误差保证。我们在调整误差水平为 $13{,}000$ 时发现了 $\mathrm{Mo-S}$ 体系的新参数点，而 Sengul 等人（2022）在相同损失函数下的水平为 $70{,}000$，实现了超过 80% 的改进。我们还将算法扩展到无训练数据的样本外体系 $\mathrm{W-S}$，相比 Sengul 等人（2021）记录了超过 70% 的改进。
英文摘要
ReaxFF is a molecular dynamics method that can be considered a good approximation to quantum methods for investigating reactive molecular systems consisting of ten thousand to one hundred thousand atoms. While ReaxFF is usually a much faster alternative to quantum methods, the force field consists of nearly 100 parameters per element, which makes the force field development a high dimensional optimization problem. In addition to the high-dimensionality, non-convexity and non-continuity make it a hard problem to optimize. We use random optimization along with coordinate search strategies to optimize efficiently and sample new parameter points that yield good molecular properties close to predefined `reference values' obtained from quantum mechanical methods for the $\mathrm{Mo-S}$ system. We also provide empirical error guaranties starting from any random sample of inputs. We discover new points for the $\mathrm{Mo-S}$ system at adjusted error levels of $13{,}000$ as compared to Sengul et al. (2022) at $70{,}000$ levels under the same loss function, registering over $80\%$ improvement. We also extend our algorithm to an out-of-sample system, $\mathrm{W-S}$, with no training data to record over $70\%$ improvement over Sengul et al. (2021).
Comments35 pages, 15 figures, 8 tables
