Prof Ong is the Feature Editor in Mar 2019’s MRS Energy Quarterly article on “Artificial intelligence is aiding the search for energy materials”. In this article written by Prachi Patel, we interview various leaders in the field on their perspectives on how AI is being applied in energy materials design, from discovering entirely novel materials to enabling large-scale complex simulations to providing insights into how to synthesize materials. Check it out at https://doi.org/10.1557/mrs.2019.51.
Paul’s collaborative paper on “Rational Synthesis and Electrochemical Performance of LiVOPO4 Polymorphs” as part of the NorthEast Center for Chemical Energy Storage has just been published in Journal of Materials Chemistry A. Here, we have carried out a comprehensive experimental and DFT study of the α-I, β and ε polymorphs of LiVOPO4 and demonstrated how selectivity for each polymorph can be tuned through manipulating the precursor and oxidation environment. Further, we discuss how synthesis conditions may be used to improve the rate performance of β-LiVOPO4.
Prof Ong’s review article as a finalist for the Rising Stars in Computational Materials Science is now available. This review outlines the efforts in the Materials Virtual Lab to integrate software automation, data generation and curation and machine learning to (i) design and optimize technological materials for energy storage, energy efficiency and high-temperature alloys; (ii) develop scalable quantum-accurate models, and (iii) enhance the speed and accuracy in interpreting characterization spectra.
Our collaborative work with the Hu group @ Florida State University on functional defects in the Cl-doped Na3PS4 solid electrolyte has just been published in Advanced Functional Materials! Through tuning of Cl and Na vacancies in Cl-doped Na3PS4, we show that conductivities as high as 1.96 mS/cm can be achieved, with excellent full-cell performance in Na/Na3.0PS3.8Cl0.2/Na3V2(PO4)3 with a reversible capacity of 100 mAh/g at room temperature.
Chi has recently developed MatErials Graph Networks (MEGNet) based on DeepMind’s graph networks approach.We show that MEGNet models are a universal approach to machine learning for both crystals and molecules, outperforming prior ML models on a broad array of properties. We also demonstrate the incorporation of state (e.g., temperature, pressure) into MEGNet models, and how transfer learning can be used to accelerate and improve the accuracy of models trained on smaller data sets.
A preprint of our paper is published on arXiv and the models are available at https://github.com/materialsvirtuallab/megnet. We have also posted a useful data set of 69,000 crystals from the Materials Project on figshare for ML purposes.
Our work (Wang, Z.; Ha, J.; Kim, Y. H.; Im, W. Bin; McKittrick, J.; Ong, S. P. Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes. Joule 2018, 2 (5), 914–926 DOI: 10.1016/j.joule.2018.01.015) on discovery of phosphors using DFT calculations has been featured in ACS Chemical & Engineering News! Follow this link to read about it.
Our former group member, Zhenbin Wang, has just been awarded the Gareth Thomas Materials Excellence Award 2018! This award honors Prof Gareth Thomas, professor emeritus of UC Berkeley and Associate Director of the Institute of Mechanics and Materials at UC San Diego, and a leading Materials SCience of the 20th century. Congratulations to Zhenbin on this great honor and we wish him all the best in his postdoctoral stint at the group of Prof Jens Norskov in DTU!
Weike’s paper on “Deep Neural Networks for Accurate Predictions of Crystal Stability” is now out in Nature Communications. Predicting the stability of crystals is one of the central problems in materials science. Here, we show that deep neural networks, i.e., algorithms that mimic the animal brain, utilizing just the electronegativity and ionic radii as inputs can predict formation energies of crystals with extremely high accuracy. We also demonstrate how these models can be generalized for mixed crystals using a binary encoding scheme, and use it to identify thousands of potentially stable new compositions. We have published a web app (http://crystals.ai) that enables anyone to use these models.
News: UCSD News: Scientists use artificial neural networks to predict new stable materials
Paper: W. Ye, C. Chen, Z. Wang, I.-H. Chu, S.P. Ong, Deep neural networks for accurate predictions of crystal stability, Nat. Commun. 9 (2018) 3800. doi:10.1038/s41467-018-06322-x.
Xiangguo’s article on “Quantum-accurate spectral neighbor analysis potential models for Ni-Mo binary alloys and fcc metals” has just been published in Physical Review B! In this work, we extend the spectral neighbor analysis potential, or SNAP, approach to fcc Ni-bcc Mo binary alloy systems. These new potentials are a substantial improvement over previous potentials based on the embedded atom method in terms of both energy and property (elastic constants, phonons, surface energies, etc.) predictions. In particular, we show that we can reproduce the Ni-Mo finite temperature phase diagram with high accuracy using the Ni-Mo SNAP model. Such high-accuracy, low-computational-cost SNAP models are an exciting enabler to studies of microstructural properties of alloys.
Article: Li, X.-G.; Hu, C.; Chen, C.; Deng, Z.; Luo, J.; Ong, S. P. Quantum-Accurate Spectral Neighbor Analysis Potential Models for Ni-Mo Binary Alloys and Fcc Metals. Phys. Rev. B 2018, 98 (9), 094104, doi:10.1103/PhysRevB.98.094104.
We have recently published two articles in Nature Scientific Data and MRS Bulletin! The first article is a follow-up from our npj Computational Materials article that specifically deals with the scope of data present in the X-ray absorption spectroscopy database (XASDb). The second article is a review on harnessing Materials Project data for machine learning and accelerated discovery. Check out both articles in our publications page!