论文标题
具有可扩展$δ$ -QML的化学空间的DMC精度
Towards DMC accuracy across chemical space with scalable $Δ$-QML
论文作者
论文摘要
在过去的十年中，已证明量子扩散蒙特卡洛（DMC）通过数值求解电子多体schrödinger方程来成功预测广泛分子和固体的能量和特性。我们表明，当基于量子机器学习（QML）的替代方法结合使用时，可以减轻计算负担，以便QMC明显地潜在地构成了化学空间中高质量描述的形成。我们讨论实现这一目标所需的三个关键近似：固定节点近似，化学键解离能的通用和准确参考以及可扩展的最小AMONS集基于QML（AQML）模型。提供的数值证据包括超过一千个小的有机分子的融合DMC结果，其中最多5个用作AMON的重原子，以及50个具有9个重原子的中等大小的有机分子来验证AQML预测。以$δ$ -AQML的模型收集的数值证据表明，已经大小的QMC训练数据集足以预测整个化学空间中几乎化学精度的总能量。
In the past decade, quantum diffusion Monte Carlo (DMC) has been demonstrated to successfully predict the energetics and properties of a wide range of molecules and solids by numerically solving the electronic many-body Schrödinger equation. We show that when coupled with quantum machine learning (QML) based surrogate methods the computational burden can be alleviated such that QMC shows clear potential to undergird the formation of high quality descriptions across chemical space. We discuss three crucial approximations necessary to accomplish this: The fixed node approximation, universal and accurate references for chemical bond dissociation energies, and scalable minimal amons set based QML (AQML) models. Numerical evidence presented includes converged DMC results for over one thousand small organic molecules with up to 5 heavy atoms used as amons, and 50 medium sized organic molecules with 9 heavy atoms to validate the AQML predictions. Numerical evidence collected for $Δ$-AQML models suggests that already modestly sized QMC training data sets of amons suffice to predict total energies with near chemical accuracy throughout chemical space.
