Publications

Materials graph networks as a universal machine learning framework for molecules and crystals

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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.

Awards

Gareth Thomas Materials Excellence Award 2018

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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!

News

Deep neural networks can predict crystal stability

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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.

Publications

Accurate Potentials for Ni-Mo alloys and fcc metals

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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.

Publications

New articles on XAS Database and Harnessing the Materials Project

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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!

Publications

Chancellor’s Dissertation Medal 2018

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Our first PhD graduate, Zhenbin Wang, has just been awarded the Chancellor’s Dissertation Medal! The Chancellor’s Dissertation Medal recognizes outstanding Ph.D. dissertations with impact and originality. Zhenbin’s dissertation on the “Design and Optimization of Phosphors for Solid-State Lighting using First-Principles Calculations” has provide immense insights into the structure-composition-property relationships in phosphors (materials which emit light), culminating in the discovery of the novel Sr2LiAlO4 phosphor – the first known crystal in the Sr-Li-Al-O chemistry. Congratulations to Zhenbin on the well-deserved honor!

Publications

Promise and Challenges of Quantum Computing for Energy Storage

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Shyue Ping recently co-authored a Future Energy article on “The Promise and Challenges of Quantum Computing for Energy Storage” with Alan Ho and Jarrod McClean of Google. This article frames and explores the opportunity of applying quantum computing to energy storage, with a focus on computational materials design of batteries.

Publications

KVOPO4: A New High Capacity Multi-electron Na‐Ion Battery Cathode

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Yuh-chieh Lin and Iek-Heng Chu are proud co-authors of a recent article published in Advanced Energy Materials on KVOPO4, a novel, high capacity multi-electron cathode for Na-ion batteries. This highly collaborative work, which is part of the NorthEast Center for Chemical Energy Storage (NECCESS) demonstrates fully activated Na+ intercalation over the V3+/4+/5+ couple in a vanadyl phosphate phase for the first time, with a high practical energy density of over 600 Wh/kg, the highest yet reported for any sodium cathode material. DFT calculations (contribution from MAVRL) shows that KVOPO4 is a 3D ionic conductor with low Na+ migration energy barrier of

Publications

Electrochemical Properties of Naphthalene Diimide

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Hanmei’s co-author paper with the Chen and Lipomi groups on “Understanding the Electrochemical Properties of Naphthalene Diimide: Implication for Stable and High-Rate Lithium-Ion Battery Electrodes” has just been published in Chemistry of Materials. In this work, we investigate the redox-active organic molecule, 1,4,5,8-naphthalenediimide (NDI), as a low-cost, high-abundance alternative to transition metal-based electrodes for lithium-ion batteries. Hanmei’s contribution is in using the latest SCAN functional combined with the HSE functional to identify the stable Li intercalation sites and compute the voltage profile of NDI, which are in excellent agreement with the experiments from the Chen group.