论文标题
用机器学习的电子密度破解量子缩放极限
Cracking the Quantum Scaling Limit with Machine Learned Electron Densities
论文作者
论文摘要
科学的一个长期目标是准确解决大分子系统的schrödinger方程。经典计算机上电流量子化学算法的尺度较差，有效限制了大约几十个原子，我们可以为其计算分子电子结构。我们提出了一种机器学习（ML）方法，可以突破这种缩放限制并使可能的大型系统计算量子化学计算。我们表明，可以训练欧几里得神经网络，以预测有限数据的高保真度。学习电子密度使我们能够在小型系统上训练机器学习模型，并对大型系统进行准确的预测。我们表明，这种ML电子密度模型可以突破量子缩放极限，并以量子精度计算数千原子系统的电子密度。
A long-standing goal of science is to accurately solve the Schrödinger equation for large molecular systems. The poor scaling of current quantum chemistry algorithms on classical computers imposes an effective limit of about a few dozen atoms for which we can calculate molecular electronic structure. We present a machine learning (ML) method to break through this scaling limit and make quantum chemistry calculations of very large systems possible. We show that Euclidean Neural Networks can be trained to predict the electron density with high fidelity from limited data. Learning the electron density allows us to train a machine learning model on small systems and make accurate predictions on large ones. We show that this ML electron density model can break through the quantum scaling limit and calculate the electron density of systems of thousands of atoms with quantum accuracy.
