Modeling the energy and forces of atomic systems is a fundamental problem in computational chemistry with the potential to help address many of the world's most pressing problems, including those related to energy scarcity and climate change. These calculations are traditionally performed using Density Functional Theory, which is computationally very expensive. Machine learning has the potential to dramatically improve the efficiency of these calculations from days or hours to seconds. We propose the Spherical Channel Network (SCN) to model atomic energies and forces. The SCN is a graph neural network where nodes represent atoms and edges their neighboring atoms. The atom embeddings are a set of spherical functions, called spherical channels, represented using spherical harmonics. We demonstrate, that by rotating the embeddings based on the 3D edge orientation, more information may be utilized while maintaining the rotational equivariance of the messages. While equivariance is a desirable property, we find that by relaxing this constraint in both message passing and aggregation, improved accuracy may be achieved. We demonstrate state-of-the-art results on the large-scale Open Catalyst dataset in both energy and force prediction for numerous tasks and metrics.
翻译:模拟原子系统的能量和力量是计算化学的一个根本问题,有可能帮助解决世界上许多最紧迫的问题,包括能源稀缺和气候变化。这些计算传统上使用计算成本极高的密度功能理论进行。机器学习有可能大大提高这些计算的效率,从数日或数小时到数秒。我们提议利用球道频道网络模拟原子能量和力量。SCN是一个图形神经网络,节点代表原子和边缘其相邻原子。原子嵌入是一组球形功能,称为球形信道,使用球形协调器进行。我们证明,通过根据3D边缘方向旋转嵌入,在保持电文的旋转平衡时,可以使用更多的信息。虽然电子变异性是一种可取的特性,但我们发现,通过在信息传递和汇总中放松这种制约,可以实现更高的准确性。我们展示了大规模开放卡塔利卫星和多度能量预测的状态。