Prof Ong gave a talk at the Joule & Energy and AI Joint Online Symposium on our recent work on “Multi-fidelity Graph Networks forMaterials Property Predictions”. Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science. In recent years, machine learning (ML) on ab initio calculations has emerged as a new paradigm to provide rapid predictions of materials properties across vast chemical spaces. However, the performances of ML models are determined by the quantity and quality of data, which tend to be inversely correlated with each other. In this talk, we show that multi-fidelity materials graph networks can transcend this trade-off to achieve accurate predictions of the experimental band gaps of ordered and disordered materials to within 0.3-0.5 eV. Further, such models can be readily extended to predict the band gaps of disordered crystals to excellent agreement with experiments, addressing a major gap in the computational prediction of materials properties. You can check out the two works discussed in the presentation at: (1) Chen, C.; Zuo, Y.; Ye, W.; Li, X.; Ong, S. P. Multi-Fidelity Graph Networks for Machine Learning the Experimental Properties of Ordered and Disordered Materials. arXiv:2005.04338 [cond-mat] 2020. […]
Prof Ong gave a webinar talk on the AI Revolution in Materials Science for the Singapore Agency of Science Technology and Research (A*STAR). In this talk, he discussed the big challenges in materials science where AI can make a huge impact towards addressing as well as outstanding challenges and opportunities to bringing forth the AI revolution to the materials domain.
Our joint work with Ping Liu’s group on “A Disordered Rock Salt Anode for Fast-charging Lithium-ion Batteries” has been published in Nature. In this work, we report that disordered rock salt (DRS) Li3+xV2O5 as a fast-charging anode that can reversibly cycle two lithium ions for thousands of cycles. Because it operates at an average voltage of about 0.6 volts versus a Li/Li+, Li3+xV2O5 is less likely compared to graphite to plate lithium metal, alleviating a major safety concern, while still being 71% more energy dense than lithium titanate. Zhuoying from the Materials Virtual Lab studied the new anode using DFT calculations. We propose a new redistributive lithium intercalation mechanism that suppresses the intercalation voltage and lowers energy barriers for diffusion. This low-potential, high-rate intercalation reaction can be used to identify other metal oxide anodes for fast-charging, long-life lithium-ion batteries. Check out our work at here. Press: UCSD News