51维势能面的高斯过程模型用于质子化咪唑二聚体
Gaussian process model of 51-dimensional potential energy surface for protonated imidazole dimer
AI总结：
本文提出基于高斯过程的高维分子系统势能面建模方法，通过5000次ab initio计算构建了51维势能面模型，展示了其在质子化咪唑二聚体中的应用及高能区外推能力。
摘要：
本文旨在通过少量的ab initio计算获得高维分子系统的准确势能面（PES）。我们使用基于高斯过程（GPs）的概率建模方法。我们证明了可以基于5000个随机分布的ab initio计算构建一个准确的GP模型，其全局精度小于0.2 kcal/mol。我们的方法使用复合核的GP模型以增强贝叶斯信息内容，并将全局PES表示为完整维度GP和多个低维分子片段GP模型之和。我们通过构建质子化咪唑二聚体的全局PES来展示这些算法的潜力，这是一个包含19个原子的分子系统。我们证明了由此构建的GP模型能够从低能区（<10,000 cm⁻¹）外推PES，得到高能区（>20,000 cm⁻¹）的PES。这为高斯过程在高维物理和化学问题中的新应用开辟了前景，如通过外推映射相变或加速贝叶斯优化。
英文摘要：
The goal of the present work is to obtain accurate potential energy surfaces (PES) for high-dimensional molecular systems with a small number of ${\it ab}$ ${\it initio}$ calculations in a system-agnostic way. We use probabilistic modeling based on Gaussian processes (GPs). We illustrate that it is possible to build an accurate GP model of a 51-dimensional PES based on $5000$ randomly distributed ${\it ab}$ ${\it initio}$ calculations with a global accuracy of $< 0.2$ kcal/mol. Our approach uses GP models with composite kernels designed to enhance the Bayesian information content and represents the global PES as a sum of a full-dimensional GP and several GP models for molecular fragments of lower dimensionality. We demonstrate the potency of these algorithms by constructing the global PES for the protonated imidazole dimer, a molecular system with $19$ atoms. We illustrate that GP models thus constructed can extrapolate the PES from low energies ($< 10,000$ cm$^{-1}$), yielding a PES at high energies ($> 20,000$ cm$^{-1}$). This opens the prospect for new applications of GPs, such as mapping out phase transitions by extrapolation or accelerating Bayesian optimization, for high-dimensional physics and chemistry problems with a restricted number of inputs, i.e. for high-dimensional problems where obtaining training data is very difficult.
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。
