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This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani +7 more
wiley +1 more source
Although research on the preparation of metal oxides and other materials for various applications increases exponentially, it is more important to understand the need for eco-friendly methods of preparation to preserve the environment.
S. Pavithra +2 more
doaj +1 more source
Supporting AI Readiness Through Digital Workflows in Materials Science
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source
A robust extended Kalman filter (EKF) is proposed as a method for estimation of the state of charge (SOC) of lithium-ion batteries used in hybrid electric vehicles (HEVs). An equivalent circuit model of the battery, including its electromotive force (EMF)
jiang, J. +7 more
core +1 more source
Operando characterisations of lithium/sodium metal batteries [PDF]
This thesis explores the degradation mechanisms of lithium/sodium metal battery anodes by several operando characterization techniques, such as real-time liquid-cell scanning transmission electron microscopy, online mass spectrometry, and in-situ atomic ...
Gong, Chen
core +1 more source
Detecting Anomalous Cell Behavior in Electrochemical Battery Testing Using Machine Learning
Machine‐learning‐based screening enables automated identification of anomalous battery cells from complementary electrochemical tests. A curated battery database supports configuration‐aware comparison of rate‐capability and impedance data. Supervised classification of rate‐test data achieves 90% accuracy, while CNN‐VAE‐based impedance analysis reaches
Minu Rose +7 more
wiley +1 more source
Lithium metal battery is considered to be the most promising energy storage technologies due to its ultra-high theoretical capacity and extremely low standard potential. However, the infinite volume change during uneven deposition/dissolution process and
Xinshuang Chang +7 more
doaj +1 more source
Enhancing Low‐Temperature Performance of Sodium‐Ion Batteries via Anion‐Solvent Interactions
DOL is introduced into electrolytes as a co‐solvent, increasing slat solubility, ion conductivity, and the de‐solvent process, and forming an anion‐rich solvent shell due to its high interaction with anion. With the above virtues, the batteries using this electrolyte exhibit excellent cycling stability at low temperatures. Abstract Sodium‐ion batteries
Cheng Zheng +7 more
wiley +1 more source
Lithium (Li) dendrite growth significantly deteriorates the performance and shortens the operation life of lithium metal batteries. Capturing the intricate dynamics of surface localized and rapid mass transport at the electrolyte–electrode interface of ...
Xile Han +10 more
doaj +1 more source
Progress in phase field research of dead lithium in lithium batteries
In recent years, the increase in people’s demand for energy has led to the development of secondary batteries. Because of its high theoretical capacity and low electrochemical potential, lithium metal has gradually become the preferred negative electrode
Jing LIU, Huiyi CHEN, Huiyu LIU, Shu LI
doaj +1 more source

