ACGNet: An Interpretable Attention Crystal Graph Neural Network for Accurate Oxidation Potential Prediction

Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries

(2023) Zhuqing Wang et al.   INFORMATION SCIENCES

A deep attention-assisted and memory-augmented temporal convolutional network based model for rapid lithium-ion battery remaining useful life predictions with limited data

(2023) Zicheng Fei et al.   Journal of Energy Storage

Online remaining useful life prediction of lithium-ion batteries using bidirectional long short-term memory with attention mechanism

(2022) Fu-Kwun Wang et al.   ENERGY

A Multichannel MN-GCN for Wheelset-Bearing System Fault Diagnosis

(2022) Zonghao Yuan et al.   IEEE SENSORS JOURNAL

Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning

(2021) Ziteng Liu et al.   Journal of Chemical Information and Modeling

Beyond Doping and Coating: Prospective Strategies for Stable High-Capacity Layered Ni-rich Cathodes

(2020) Ho-Hyun Sun et al.   ACS Energy Letters

Interfaces and Interphases in All-Solid-State Batteries with Inorganic Solid Electrolytes

(2020) Abhik Banerjee et al.   CHEMICAL REVIEWS

A Comprehensive Survey on Graph Neural Networks

(2020) Zonghan Wu et al.   IEEE Transactions on Neural Networks and Learning Systems

Structural and Electrochemical Aspects of LiNi0.8Co0.1Mn0.1O2 Cathode Materials Doped by Various Cations

(2019) Tina Weigel et al.   ACS Energy Letters

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals

(2019) Chi Chen et al.   CHEMISTRY OF MATERIALS

Dissolution, migration, and deposition of transition metal ions in Li-ion batteries exemplified by Mn-based cathodes – a critical review

(2018) Chun Zhan et al.   Energy & Environmental Science

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

(2018) Tian Xie et al.   PHYSICAL REVIEW LETTERS

Identifying an efficient, thermally robust inorganic phosphor host via machine learning

(2018) Ya Zhuo et al.   Nature Communications

Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials

(2017) Austin D. Sendek et al.   Energy & Environmental Science

Universal fragment descriptors for predicting properties of inorganic crystals

(2017) Olexandr Isayev et al.   Nature Communications

Nickel-Rich Layered Cathode Materials for Automotive Lithium-Ion Batteries: Achievements and Perspectives

(2016) Seung-Taek Myung et al.   ACS Energy Letters

Li-ion battery materials: present and future

(2015) Naoki Nitta et al.   Materials Today

The quest for new functionality

(2015) Aron Walsh   Nature Chemistry

Comparison of the structural and electrochemical properties of layered Li[NixCoyMnz]O2 (x = 1/3, 0.5, 0.6, 0.7, 0.8 and 0.85) cathode material for lithium-ion batteries

(2013) Hyung-Joo Noh et al.   JOURNAL OF POWER SOURCES

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

(2013) Anubhav Jain et al.   APL Materials

Phase stability, electrochemical stability and ionic conductivity of the Li10±1MP2X12(M = Ge, Si, Sn, Al or P, and X = O, S or Se) family of superionic conductors

(2012) Shyue Ping Ong et al.   Energy & Environmental Science

Recent advances in the research of polyanion-type cathode materials for Li-ion batteries

(2011) Zhengliang Gong et al.   Energy & Environmental Science