We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using
Machine Learning (MatSci ML) methods focused on solid-state materials with
periodic crystal structures. Applying machine learning methods to solid-state
materials is a nascent field with substantial fragmentation largely driven by
the great variety of datasets used to develop machine learning models. This
fragmentation makes comparing the performance and generalizability of different
methods difficult, thereby hindering overall research progress in the field.
Building on top of open-source datasets, including large-scale datasets like
the OpenCatalyst, OQMD, NOMAD, the Carolina Materials Database, and Materials
Project, the MatSci ML benchmark provides a diverse set of materials systems
and properties data for model training and evaluation, including simulated
energies, atomic forces, material bandgaps, as well as classification data for
crystal symmetries via space groups. The diversity of properties in MatSci ML
makes the implementation and evaluation of multi-task learning algorithms for
solid-state materials possible, while the diversity of datasets facilitates the
development of new, more generalized algorithms and methods across multiple
datasets. In the multi-dataset learning setting, MatSci ML enables researchers
to combine observations from multiple datasets to perform joint prediction of
common properties, such as energy and forces. Using MatSci ML, we evaluate the
performance of different graph neural networks and equivariant point cloud
networks on several benchmark tasks spanning single task, multitask, and
multi-data learning scenarios. Our open-source code is available at
this https URL

提出了 MatSci ML，这是一个用于建模具有周期晶体结构的固态材料的机器学习（MatSci ML）方法的新型基准。使用机器学习方法研究固态材料是一个新兴领域，由于使用不同种类的数据集来开发机器学习模型，导致了碎片化的情况，使得比较不同方法的性能和泛化能力变得困难，从而阻碍了该领域的研究进展。MatSci ML 基准建立在开源数据集的基础上，包括 OpenCatalyst、OQMD、NOMAD、Carolina 材料数据库和 Materials Project 等大规模数据集，为模型训练和评估提供了多样化的材料系统和属性数据，包括模拟能量、原子力、材料能隙，以及通过空间群对结晶对称性进行分类的数据。MatSci ML 中的属性多样性使得实施和评估固态材料的多任务学习算法成为可能，而数据集的多样性则促进了跨多个数据集开发新的更广义的算法和方法。在多数据集学习环境中，MatSci ML 使研究人员能够结合来自多个数据集的观测结果，进行共同预测共同属性，如能量和力。使用 MatSci ML，我们评估了不同的图神经网络和等变点云网络在涵盖单一任务、多任务和多数据学习场景的几个基准任务上的性能。我们的开源代码可在指定的 https URL 上找到。