Developing universal machine learning models for ab initio calculations is the frontier of materials cutting edge research in the new era of artificial intelligence. Here, we present the Deep Augment Way model (DeePAW) that is a universal machine learning (ML) model for orbital-free (OF) ab initio calculations, based on the density functional theory (DFT). DeePAW is currently the best OFDFT ML model according to the three criterions, 1) covering the largest number of elements, 2) having the widest application capability to diverse crystal structures, and 3) achieving the highest prediction accuracy without further fine-tuning. These scientific merits and innovations of DeePAW are stemmed from the novel SE(3)-equivariant double massage passing neuron networks. Besides predicting electron density distributions, DeePAW predicts formation energies of crystals as well and therefore paves an efficient avenue for multiscale materials modeling beyond conventional electronic structure calculation methods.

翻译：开发用于从头算的通用机器学习模型是人工智能新时代下材料前沿研究的关键领域。在此，我们提出深度增强路径模型（DeePAW），这是一种基于密度泛函理论（DFT）的通用无轨道（OF）从头算机器学习（ML）模型。根据以下三个准则，DeePAW是目前最优的OFDFT机器学习模型：1）覆盖最多元素种类，2）对多样晶体结构具有最广泛的应用能力，3）无需进一步微调即可达到最高预测精度。DeePAW的这些科学价值与创新源于新颖的SE(3)-等变双重消息传递神经网络。除预测电子密度分布外，DeePAW还能预测晶体的形成能，从而为超越传统电子结构计算方法的多尺度材料建模开辟了高效途径。