A Systematic Review of Deep Learning Approaches for Natural Language Processing in Battery Materials Domain

Natural Language Processing (NLP) acts towards the processing of linguistics between human and computer. The application of NLP in the chemical industry has proven to be a boon in the past decade. ...

[1]  Eduard H. Hovy,et al.  End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF , 2016, ACL.

[2]  Gobinda G. Chowdhury,et al.  Natural language processing , 2005, Annu. Rev. Inf. Sci. Technol..

[3]  Myle Ott,et al.  Understanding Back-Translation at Scale , 2018, EMNLP.

[4]  Quoc V. Le,et al.  Semi-Supervised Sequence Modeling with Cross-View Training , 2018, EMNLP.

[5]  Ashish Vaswani,et al.  Self-Attention with Relative Position Representations , 2018, NAACL.

[6]  R. Ritchie,et al.  Nanometer-scale gradient atomic packing structure surrounding soft spots in metallic glasses , 2018, npj Computational Materials.

[7]  Andreas H. Göller.  Dataset overlap density analysis , 2013, Journal of Cheminformatics.

[8]  Yue Zhang,et al.  In-Order Transition-based Constituent Parsing , 2017, TACL.

[9]  Bo Sundman,et al.  Calculations of thermophysical properties of cubic carbides and nitrides using the Debye–Grüneisen model , 2007 .

[10]  Stefano Ermon,et al.  Closed-loop optimization of fast-charging protocols for batteries with machine learning , 2020, Nature.

[11]  Rui Yan,et al.  Learning to Respond with Deep Neural Networks for Retrieval-Based Human-Computer Conversation System , 2016, SIGIR.

[12]  Bogdan Babych,et al.  Improving Machine Translation Quality with Automatic Named Entity Recognition , 2003, Proceedings of the 7th International EAMT workshop on MT and other Language Technology Tools, Improving MT through other Language Technology Tools Resources and Tools for Building MT - EAMT '03.

[13]  J. Seminario,et al.  Dendrite formation in silicon anodes of lithium-ion batteries , 2018, RSC advances.

[14]  Jungo Kasai,et al.  Robust Multilingual Part-of-Speech Tagging via Adversarial Training , 2017, NAACL.

[15]  Denis Pasero,et al.  High throughput methodology for synthesis, screening, and optimization of solid state lithium ion electrolytes. , 2011, ACS combinatorial science.

[16]  Hui Li,et al.  An Overview of Deep Generative Models , 2015 .

[17]  Dan Klein,et al.  Improving Neural Parsing by Disentangling Model Combination and Reranking Effects , 2017, ACL.

[18]  Baisakhi Chakraborty,et al.  Automated Knowledge Provider System with Natural Language Query Processing , 2016 .

[19]  João Aires-de-Sousa,et al.  Exploration of quantitative structure–property relationships (QSPR) for the design of new guanidinium ionic liquids , 2008 .

[20]  T. Yamaue,et al.  Machine Learning Approaches for Designing Mesoscale Structure of Li-Ion Battery Electrodes , 2019, Batteries.

[21]  Jeffrey Pennington,et al.  Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions , 2011, EMNLP.

[22]  C. Bauschlicher,et al.  Theoretical study of the spectroscopy of the alkali oxides LiO, NaO, and KO , 1991 .

[23]  V. Subramanian,et al.  Data Science Approaches for Electrochemical Engineers: An Introduction through Surrogate Model Development for Lithium-Ion Batteries , 2018 .

[24]  J. Grossman,et al.  Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes , 2018, ACS central science.

[25]  Joeri Van Mierlo,et al.  Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review , 2019, Renewable and Sustainable Energy Reviews.

[26]  Kristen A. Severson,et al.  Data-driven prediction of battery cycle life before capacity degradation , 2019, Nature Energy.

[27]  Noah A. Smith,et al.  What Do Recurrent Neural Network Grammars Learn About Syntax? , 2016, EACL.

[28]  O. Winther,et al.  A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning , 2019, Energy Storage Materials.

[29]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[30]  Venkatasubramanian Viswanathan,et al.  An accurate machine-learning calculator for optimization of Li-ion battery cathodes. , 2020, The Journal of chemical physics.

[31]  Dan Klein,et al.  Constituency Parsing with a Self-Attentive Encoder , 2018, ACL.

[32]  Reza Eslamloueyan,et al.  Using a Multilayer Perceptron Network for Thermal Conductivity Prediction of Aqueous Electrolyte Solutions , 2011 .

[33]  Olatomiwa Badmos,et al.  Image-based defect detection in lithium-ion battery electrode using convolutional neural networks , 2020, J. Intell. Manuf..

[34]  Tong Zhang,et al.  Deep Pyramid Convolutional Neural Networks for Text Categorization , 2017, ACL.

[35]  Mirella Lapata,et al.  Long Short-Term Memory-Networks for Machine Reading , 2016, EMNLP.

[36]  Patrick Hop,et al.  Geometric Deep Learning Autonomously Learns Chemical Features That Outperform Those Engineered by Domain Experts. , 2018, Molecular pharmaceutics.

[37]  Nikhil Ketkar,et al.  Convolutional Neural Networks , 2021, Deep Learning with Python.

[38]  Christopher Kanan,et al.  Visual question answering: Datasets, algorithms, and future challenges , 2016, Comput. Vis. Image Underst..

[39]  Sunghak Lee,et al.  Understanding the physical metallurgy of the CoCrFeMnNi high-entropy alloy: an atomistic simulation study , 2018, npj Computational Materials.

[40]  Sebastian Ruder,et al.  Universal Language Model Fine-tuning for Text Classification , 2018, ACL.

[41]  P. V. van Haastert,et al.  A homologue of the Parkinson’s disease-associated protein LRRK2 undergoes a monomer-dimer transition during GTP turnover , 2017, Nature Communications.

[42]  Arzucan Özgür,et al.  Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery , 2020, Drug discovery today.

[43]  Gowoon Cheon,et al.  Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials , 2018, Chemistry of Materials.

[44]  Evan Bolton,et al.  PubChem3D: conformer ensemble accuracy , 2013, Journal of Cheminformatics.

[45]  A. Bhardwaj,et al.  In situ click chemistry generation of cyclooxygenase-2 inhibitors , 2017, Nature Communications.