High-throughput investigation of stability and Li diffusion of doped solid electrolytes via neural network potential without configurational knowledge.

Solid electrolytes hold substantial promise as vital components of all-solid-state batteries. Enhancing their performance necessitates simultaneous improvements in their stability and lithium conductivity. These properties can be calculated using first-principles simulations, provided that the crystal structure of the material and the diffusion pathway through the material are known. However, solid electrolytes typically incorporate dopants to enhance their properties, necessitating the optimization of the dopant configuration for the simulations. Yet, performing such calculations via the first-principles approach is so costly that existing approaches usually rely on predetermined dopant configurations informed by existing knowledge or are limited to systems doped with only a few atoms. The proposed method enables the optimization of the dopant configuration with the support of neural network potential (NNP). Our approach entails the use of molecular dynamics to analyze the diffusion after the optimization of the dopant configuration. The application of our approach to Li 10 MP 2 S 12 - x O x (M = Ge, Si, or Sn) reproduce the experimental results well. Furthermore, analysis of the lithium diffusion pathways suggests that the activation energy of diffusion undergoes a percolation transition. This study demonstrates the effectiveness of NNPs in the systematic exploration of solid electrolytes.

Universally Exhaustive Generation of Molecular Structures and Prediction of Their Electronic States Using Machine Learning for N-type Organic Transistor Materials.

We have proposed a new method for the exploration of organic functional molecules, using an exhaustive molecular generator combined without combinatorial explosion and electronic state predicted by machine learning and adapted for developing n-type organic semiconductor molecules for field-effect transistors. Our method first enumerates skeletal structures as much as possible and next generates fused ring structures using substitution operations for atomic nodes and bond edges. We have succeeded in generating more than 4.8 million molecules. We calculated the electron affinity (EA) of about 51 thousand molecules with DFT calculation and trained the graph neural networks to estimate EA values of generated molecules. Finally, we obtained the 727 thousand molecules as candidates that satisfy EA values over 3 eV. The number of these possible candidate molecules is far beyond what we have been able to propose based on our knowledge and experience in synthetic chemistry, indicating a wide diversity of organic molecules.

Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements.

Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. Here we report a development of universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO4F, molecular adsorption in metal-organic frameworks, an order-disorder transition of Cu-Au alloys, and material discovery for a Fischer-Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.