Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be derived. This is complicated by the fact that wavefunctions transform non-trivially under molecular rotations, which makes them a challenging prediction target. To solve this issue, we introduce general SE(3)-equivariant operations and building blocks for constructing deep learning architectures for geometric point cloud data and apply them to reconstruct wavefunctions of atomistic systems with unprecedented accuracy. Our model achieves speedups of over three orders of magnitude compared to ab initio methods and reduces prediction errors by up to two orders of magnitude compared to the previous state-of-the-art. This accuracy makes it possible to derive properties such as energies and forces directly from the wavefunction in an end-to-end manner. We demonstrate the potential of our approach in a transfer learning application, where a model trained on low accuracy reference wavefunctions implicitly learns to correct for electronic many-body interactions from observables computed at a higher level of theory. Such machine-learned wavefunction surrogates pave the way towards novel semi-empirical methods, offering resolution at an electronic level while drastically decreasing computational cost. Additionally, the predicted wavefunctions can serve as initial guess in conventional ab initio methods, decreasing the number of iterations required to arrive at a converged solution, thus leading to significant speedups without any loss of accuracy or robustness.
翻译:机器学习使得能够以高精确度和效率预测量化学特性,从而绕过计算成本高昂的初始计算。 较新的方法不是就一套固定的特性进行培训,而是试图将电子波函数(或密度)作为原子系统的核心数量来学习,从中可以得出所有其他可观测的原子系统。由于波函数在分子旋转下改变非三重性,从而使它们成为具有挑战性的预测目标,这就使得情况更加复杂。 为了解决这个问题,我们引入了一般的 SE(3) QQQ 操作和构件,用于为几何点云值数据建造深层学习结构,并应用它们来以前所未有的精确度重建非omistic 系统的精确值。 我们的模型比Ab Intio方法加速了三个以上的数量级(或密度),并将预测误差减少至两个数量级。 这种精确度使能量和力能够直接从波值中产生一个具有挑战性的预测目标。 为了解决这个问题,我们的方法在转换应用中表现出潜力,在不精确度的云值水平上,在低精确度参考度上训练了不精确度的精确度的精确度值值,从而可以使机能的机能的机变的机变的机变的机的机变的机变的机变的机变的机变的机能方法。