| From the paper’s introduction: > Machine learning (ML) models for materials science have the potential to vastly expedite the computational discovery process. State-of-the-art ML models can predict the results of physics-based quantum mechanical calculations but are several orders of magnitude faster, making them ideal for predicting general material properties [5–7]. In addition to direct property prediction, combining universal ML potentials such as M3GNet [8], CHGNet [9], and GNoME [10] has made it possible to perform geometric optimization, and hence evaluate thermodynamic stability, for arbitrary combinations of elements and structures. The significant speed advantage of ML-based techniques over direct simulation has made it possible to explore materials across a vast chemical space that greatly exceeds the number of known materials. Models mentioned: M3GNet: M3GNet is a new materials graph neural network architecture that incorporates 3-body interactions. A key difference with prior materials graph implementations such as MEGNet is the addition of the coordinates for atoms and the 3×3 lattice matrix in crystals, which are necessary for obtaining tensorial quantities such as forces and stresses via auto-differentiation. Source: https://materialsvirtuallab.github.io/m3gnet/ CHGNet: A pretrained universal neural network potential for charge-informed atomistic modeling (see publication). Crystal Hamiltonian Graph neural Network is pretrained on the GGA/GGA+U static and relaxation trajectories from Materials Project, a comprehensive dataset consisting of more than 1.5 Million structures from 146k compounds spanning the whole periodic table. Source: https://github.com/CederGroupHub/chgnet GNoME: GNoME Is A State-Of-The-Art Graph Neural Network (GNN) Model. The Input Data For GNNs Take The Form Of A Graph That Can Be Likened To Connections Between Atoms, Which Makes GNNs Particularly Suited To Discovering New Crystalline Materials. Source: https://gmnomeai.cloud/ |