4.8 Article

LIBAC: An Annotated Corpus for Automated Reading of the Lithium-Ion Battery Research Literature

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Chemistry, Physical

Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations

Marc Duquesnoy et al.

Summary: The optimization of the electrodes manufacturing process is crucial for high-quality LIB cells, especially for automotive applications. A proposed deterministic ML-assisted pipeline is used for multi-objective optimization of electrode properties and inverse design of its manufacturing process. The pipeline generates a synthetic dataset from physics-based simulations and trains deterministic ML models to implement fast optimization. The successful fabrication of the electrode validates the physical relevance of the modeling pipeline.

ENERGY STORAGE MATERIALS (2023)

Review Chemistry, Physical

A review of the recent progress in battery informatics

Chen Ling

Summary: This in-depth review focuses on the interdisciplinary field of battery informatics, which combines machine learning and battery research engineering. It highlights the crucial issue of battery data availability and explains how recent achievements have addressed the challenge of data scarcity. The review concludes with a perspective on this exciting new field.

NPJ COMPUTATIONAL MATERIALS (2022)

Review Chemistry, Multidisciplinary

Artificial Intelligence Applied to Battery Research: Hype or Reality?

Teo Lombardo et al.

Summary: This article is a critical review of the application of artificial intelligence/machine learning methods in battery research. It aims to provide a comprehensive and authoritative review that is easily understandable to the battery community. The review discusses the concepts, approaches, tools, outcomes, and challenges of using AI/ML to accelerate the design and optimization of the next generation of batteries, and intends to make these tools accessible to the chemistry and electrochemical energy sciences communities while covering various aspects of battery R&D.

CHEMICAL REVIEWS (2022)

Article Biochemical Research Methods

A novel multiple kernel fuzzy topic modeling technique for biomedical data

Junaid Rashid et al.

Summary: This paper proposes a novel multiple kernel fuzzy topic modeling (MKFTM) technique for biomedical text mining, using fusion probabilistic inverse document frequency and multiple kernel fuzzy c-means clustering algorithm. The proposed approach efficiently handles the sparsity and redundancy problem in biomedical text documents and achieves high accuracy in discovering semantically relevant topics and in classification and clustering tasks.

BMC BIOINFORMATICS (2022)

Editorial Material Chemistry, Physical

Standardized Battery Reporting Guidelines

Alexandra K. Stephan

Review Multidisciplinary Sciences

Opportunities and challenges of text mining in materials research

Olga Kononova et al.

Summary: Research publications serve as the primary repository of scientific knowledge, but their unstructured format poses obstacles to large-scale analysis. Recent advances in natural language processing have provided tools for information extraction, but challenges arise when applied to scientific text with technical terminology. Text mining methodology in materials science is still in its early stages, with efforts focused on understanding the application of TM and NLP in this field.

ISCIENCE (2021)

Editorial Material Chemistry, Physical

An Experimental Checklist for Reporting Battery Performances

Yang-Kook Sun

ACS ENERGY LETTERS (2021)

Article Engineering, Industrial

Water-based manufacturing of lithium ion battery for life cycle impact mitigation

Chris Yuan et al.

Summary: Water-based manufacturing processes for lithium ion batteries show potential for reducing energy consumption and minimizing environmental impacts compared to conventional manufacturing methods.

CIRP ANNALS-MANUFACTURING TECHNOLOGY (2021)

Review Biochemical Research Methods

An extensive review of tools for manual annotation of documents

Mariana Neves et al.

Summary: The study evaluated 78 annotation tools and selected 15 for in-depth evaluation. The results showed that some tools are comprehensive and mature with high scores in functional and technical aspects, suitable for most annotation projects.

BRIEFINGS IN BIOINFORMATICS (2021)

Review Computer Science, Theory & Methods

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

Laith Alzubaidi et al.

Summary: Deep learning has become the gold standard in the machine learning community, widely used in various domains and capable of learning massive data. Through a comprehensive survey, a better understanding of the most important aspects of deep learning is provided.

JOURNAL OF BIG DATA (2021)

Article Electrochemistry

What Can Text Mining Tell Us About Lithium-Ion Battery Researchers' Habits?

Hassna El-Bousiydy et al.

Summary: Artificial Intelligence has the potential to revolutionize battery R&D by accelerating material discovery, but its success depends on access to high-quality data. Scientific publications may provide valuable data for developing reliable AI algorithms, but challenges still need to be addressed.

BATTERIES & SUPERCAPS (2021)

Article Chemistry, Physical

Good practice guide for papers on batteries for the Journal of Power Sources

Jie Li et al.

JOURNAL OF POWER SOURCES (2020)

Article Electrochemistry

A Framework for Optimal Safety Li-ion Batteries Design using Physics-Based Models and Machine Learning Approaches

Takumi Yamanaka et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2020)

Article Green & Sustainable Science & Technology

An efficient screening method for retired lithium -ion batteries based on support vector machine

Zhongkai Zhou et al.

JOURNAL OF CLEANER PRODUCTION (2020)

Article Electrochemistry

Text mining for processing conditions of solid-state battery electrolyte

Rubayyat Mahbub et al.

ELECTROCHEMISTRY COMMUNICATIONS (2020)

Article Electrochemistry

Artificial Intelligence Investigation of NMC Cathode Manufacturing Parameters Interdependencies

Ricardo Pinto Cunha et al.

BATTERIES & SUPERCAPS (2020)

Article Energy & Fuels

Data-driven prediction of battery cycle life before capacity degradation

Kristen A. Severson et al.

NATURE ENERGY (2019)

Article Computer Science, Information Systems

Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles

Yohwan Choi et al.

IEEE ACCESS (2019)

Article Computer Science, Information Systems

A Neural-Network-Based Method for RUL Prediction and SOH Monitoring of Lithium-Ion Battery

Jiantao Qu et al.

IEEE ACCESS (2019)

Article Automation & Control Systems

Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression

Jingwen Wei et al.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2018)

Article Computer Science, Information Systems

Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach

Lei Ren et al.

IEEE ACCESS (2018)

Article Materials Science, Multidisciplinary

Crack detection in lithium-ion cells using machine learning

Lukas Petrich et al.

COMPUTATIONAL MATERIALS SCIENCE (2017)

Article Chemistry, Multidisciplinary

The CHEMDNER corpus of chemicals and drugs and its annotation principles

Martin Krallinger et al.

JOURNAL OF CHEMINFORMATICS (2015)

Article Electrochemistry

A Computational Investigation of Li9M3(P2O7)3(PO4)2 (M = V, Mo) as Cathodes for Li Ion Batteries

Anubhav Jain et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2012)

Article Chemistry, Physical

Novel mixed polyanions lithium-ion battery cathode materials predicted by high-throughput ab initio computations

Geoffroy Hautier et al.

JOURNAL OF MATERIALS CHEMISTRY (2011)

Article Electrochemistry

Alloy Negative Electrodes for High Energy Density Metal-Ion Cells

Tuan T. Tran et al.

JOURNAL OF THE ELECTROCHEMICAL SOCIETY (2011)

Article Biochemical Research Methods

GENIA corpus-a semantically annotated corpus for bio-textmining

J-D Kim et al.

BIOINFORMATICS (2003)