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Machine learning accelerates high-voltage electrolyte discovery for lithium metal batteries

Energy Storage Materials 2025年 79卷

作者： Yan, Yizhe Hai, Feng Wang, Bin Cao, Wenrui Li, Mingtao Wang, Chaohui Li, Naipeng Zhao, Dan State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an Shaanxi710049 China Shaanxi Key Laboratory of Energy Chemical Process Intensification School of Chemical Engineering and Technology Xi'an Jiaotong University No. 28 Xianning West Road Xi'an Shannxi710049 China Department of Mechanical Engineering Faculty of Engineering University of Canterbury Private Bag 4800 Christchurch8140 New Zealand

Appropriate electrolytes are essential for ensuring the performance stability of high-voltage lithium metal batteries. However, the complexity arising from multiple solvents and their relative ratios leads to significant challenges for the design of electrolytes. Herein, we propose a machine learning (ML) approach that links the microscopic properties of electrolytes with their macroscopic battery performance, thus enabling the discovery of high-performance electrolyte formulations to be accelerated. By designing chemical groups of electrolyte solvents as features and establishing a cycling stability evaluation metric for batteries, an ML model is created to predict the capacity retention of high-voltage lithium metal batteries. Through integrating this ML model with a heuristic optimization algorithm, a series of superior electrolytes are identified within a ternary solvent design space (over 29,000 possible electrolytes). Our model reveals that a specific proportion of the fluorinated ether diluent is critical for achieving superior capacity retention in fluorinated electrolytes. To validate the cycling stability of these electrolytes, we experimentally tested them in a Li||LiNi0.5Mn1.5O4 coin cell configuration. All the cells containing the discovered electrolytes demonstrate outstanding capacity retention, consistent with our model's prediction trend. This work highlights the potential of ML approaches for the design and optimization of stable, high-performance battery electrolytes. © 2025 Elsevier B.V.

关键词： Lithium batteries

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Prediction of LWST values using ML and ANN techniques from frcition and texture measuring devices based on long term field data

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International Journal of Transportation Science and Technology 2025年

作者： Alsheyab, Mohammad Ahmad Khasawneh, Mohammad Ali Alotaibi, Danah Hussain Mohammed, Nayeemuddin Khasawneh, Ahmad Ali Graduate Research Assistant of Iowa State University Department of Civil Construction and Environmental Engineering AmesIA United States Civil Engineering Department Prince Mohammad Bin Fahd University Al Khobar31952 Saudi Arabia Management Engineering and Process Improvement Department/Integrated Systems Engineering Department Ohio State University Wexner Medical Center & Ohio State University ColumbusOH43202 United States

Given the cost, time requirements, and limited accessibility of Locked Wheel Skid Trailer (LWST) testing, this research explores alternative approaches to predicting skid number (SN) using more affordable and readily available devices. These alternatives offer quicker results, which is particularly relevant given the standardized nature of skid number measurements worldwide. The study focuses on predicting the (SN) generated by the LWST test through analyzing data from Dynamic Friction Tester (DFT), British Pendulum Tester (BPT), and Circular Texture Meter (CTM) using statistical and machine learning techniques. The research includes a thorough assessment of various prediction methods and factors influencing skid number accuracy, including strategies for handling missing data. Prediction models employ Multiple Linear Regression (MLR), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Ensemble Bagged Tree (EBT) techniques, utilizing predictors such as Dynamic Friction Number at 20 km/h (DFT20), Dynamic Friction Number at 64 km/h (DFT64), British Pendulum Number (BPN), and Mean Profile Depth (MPD). The development of forty-eight models, including Full, Reduced, and individual predictor models, involved employing data imputation methods and stepwise regression. Significant correlations were observed among friction parameters, with DFT20 showing the highest correlation with skid numbers, while MPD exhibited the lowest. Following data imputation, notable enhancements in BPN's correlation were noted. The study emphasizes the superiority of Full models in skid number prediction accuracy, with EBT models, especially those enhanced by data imputation, demonstrating outstanding performance. Statistical simulations confirmed the reliability of these models, indicating minimal outliers, overfitting, and high accuracy in skid number estimation. The prioritization of DFT over BPT and CTM is highlighted, with the potential for further exploration of devices to imp

关键词： Pendulums

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Stability,acidity and interaction properties of [Bmim][SbF_6] coupled with concentrated sulfuric acid

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Science China Chemistry 2017年第9期60卷 1243-1249页

作者： Honghua Zhang Ruixia Liu Ruirui Zhang Feng Huo Zhiqiang Yang Bin He Suojiang Zhang Yanji Wang School of Chemical Engineering Hebei University of Technology Key Laboratory of Green Process and Engineering Beijing Key Laboratory of Ionic Liquids Clean ProcessState Key Laboratory of Multiphase Complex SystemsInstitute of Process EngineeringChinese Academy of Sciences

Ionic liquid coupled with strong acid systems presents considerable promise in some catalytic fields. In the present work, the multiple complex systems composed by 98 wt% concentrated sulfuric acid and [Bmim][SbF6] were investigated in the terms of stability, acidity and interaction properties. It was found that acidolysis of [Bmim][SbF6] occurred in the 98 wt% concentrated sulfuric acid accompanied by HF releasing and SbF6−degrading to [SbF6−y(HSO4)y]−. The species after acidolysis in the multiple complex systems were checked and confirmed by electrospray ionization mass spectrometry (ESI-MS), Fourier transform infrared spectroscopy (FT-IR),1H NMR and19F NMR. Acidity increased slightly with less than 1 wt% [Bmim][SbF6] addition, while decreased with more proportion, which was determined based on the Hammett acidity functions H0, using13C NMR. The strong hydrogen bond S–O–HF of interaction among the multiple complex systems was confirmed by molecular dynamic simulation. © 2017, Science China Press and Springer-Verlag Berlin Heidelberg.

关键词： Electrodeposition Electrospray ionization Fourier transform infrared spectroscopy Hydrogen bonds Ionic liquids Mass spectrometry Molecular dynamics Sulfuric acid

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Pt-Containing Ag_2S-Noble Metal Nanocomposites as Highly Active Electrocatalysts for the Oxidation of Formic Acid

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Nano-Micro Letters 2014年第3期6卷 252-257页

作者： Hui Liu Yan Feng Hongbin Cao Jun Yang State Key Laboratory of Multiphase Complex Systems Institute of Process Engineering Chinese Academy of Sciences University of Chinese Academy of Sciences Research Centre for Process Pollution Control Institute of Process Engineering Chinese Academy of Sciences

Nanocomposites with synergistic effect are of great interest for their enhanced properties in a given application. Herein, we reported the high catalytic activity of Pt-containing Ag2S-noble metal nanocomposites in formic acid oxidation, which is a key reaction in direct formic acid fuel cell. The electrochemical measurements including voltammograms and chronoamperograms are used to characterize the catalytic property of Pt-containing nanocomposites for the oxidation of formic acid. In view of the limited literatures on using nanocomposites consisting of semiconductor and noble metals for catalyzing the reactions of polymer electrolyte membrane-based fuel cells, this study provides a helpful exploration for expanding the application of semiconductor-noble metal nanocomposites.

关键词： Nanocomposites Synergistic effect Formic acid oxidation Direct formic acid fuel cell

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A transient thermal model within the laser shadow during laser-assisted automated fiber placement: Prediction of temperature at the nip point using a Lagrangian description

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Composites Part B: engineering 2025年 303卷

作者： Dong, Ningguo Niu, Chengcheng Yao, Xinhua Ding, Zequan Ji, Yuyang Fu, Jianzhong Luan, Congcong The State Key Laboratory of Fluid Power and Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province College of Mechanical Engineering Zhejiang University Hangzhou310027 China

Automated fiber placement, from a material point of view, involves a transient thermal phenomenon, which introduces additional complexities due to the formation of a laser shadow zone in the optical path. In this study, a transient thermal model was developed within the laser shadow during laser-assisted automated fiber placement, incorporating velocity dependence using a Lagrangian description. This model predicted the temperature history within the laser shadow, thereby enabling control over temperature at the nip point. Experiments were conducted to validate the model by measuring temperatures using a Long Wave Infrared sensor and K-type thermocouples. A good agreement with the experimental results was achieved under various process conditions. The effects of placement speed, laser power, tooling temperature, and roller diameter were analyzed by evaluating both the model predictions and the measured data. Moreover, several composite components were fabricated, and the interlaminar shear strength was tested to characterize the effect of temperature at the nip point. Finally, a temperature at the nip point of 350.5 °C, obtained at a placement speed of 100 mm/s and a laser power of 550 W, yields a maximum value of 59.9 MPa. © 2025 Elsevier Ltd

关键词： Thermocouples

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Edge device deployment for intelligent machine tools: A lightweight and interpretable tool wear monitoring method considering wear behavior

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Robotics and Computer-Integrated Manufacturing 2025年 95卷

作者： Peng, Yezhen Kang, Weimin Yu, Fengwen Ding, Zequan Zhou, Wenhong Fu, Jianzhong Hu, Songyu State Key Laboratory of Fluid Power & Mechatronic Systems College of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province College of Mechanical Engineering Zhejiang University Hangzhou310027 China

Tool wear condition monitoring is essential for reducing production costs and improving machining precision, serving as a key strategy for achieving machine tool intelligence. However, existing methods often depend on empirically designed complex networks to achieve high recognition accuracy, which results in high computational costs, poor performance during later wear stages, and limited interpretability. To address these challenges, a lightweight and interpretable tool wear recognition method is proposed. The feature self-adaptive reconstruction strategy based on wear behavior improves feature quality, while a nonlinear cumulative wear model provides physics guidance, ensuring the model remains lightweight and interpretable. To improve recognition accuracy and robustness during later wear stages, an adaptive loss adjustment mechanism driven by error uncertainty is proposed. Additionally, the influence of reconstructed features on model output is analyzed using shapley additive explanations (SHAP) values, while dependency graphs explore interactions between features across signals and domains, reinforcing physical interpretability. Results show reconstructed features in the y-direction have the greatest influence on model output during side milling. Time-domain and time-frequency domain features dominate, with frequency-domain features providing complementary information. Experiments show the proposed method reduces RMSE by 4.77 and 6.63, MAE by 2.89 and 5.17, and improves R² by 0.06 and 0.12 compared to models with different loss weights and feature processing methods. Recognition accuracy was further improved during later wear stages, achieving RMSE, MAE, and R² values of 3.58, 2.73, and 0.92, respectively. Moreover, the model uses only four fully connected layers, reducing parameters by over 9.32 times, demonstrating the feasibility of edge deployment. © 2025 Elsevier Ltd

关键词： Milling machines

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Developing deep learning-based large-scale organic reaction classification model via sigma-profiles

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Green Chemical engineering 2025年第2期 181-192页

作者： Wenlong Wang Chenyang Xu Jian Du Lei Zhang State Key Laboratory of Fine Chemical Frontiers Science Center for Smart Materials Oriented Chemical EngineeringInstitute of Chemical Process Systems EngineeringSchool of Chemical EngineeringDalian University of Technology Department of Chemical and Biomolecular Engineering National University of Singapore

Advanced technologies like deep learning have accelerated the discovery of novel chemical reactions, especially in the field of organic synthesis. With hundreds of thousands of reactions available for reference, one way to effectively leverage them is by classifying chemical reactions into different clusters based on their specific characteristics, which makes target-guided navigation in the vast chemical space possible. Although previous attempts that apply deep learning to reaction classification tasks have made substantial progress, developing a model with good interpretability as well as high accuracy for large-scale reaction classification tasks remains an open question. In this work, a deep learning-based model for a large-scale reaction classification task is first constructed by utilizing pre-trained BERT and autoencoder. Then, the model is trained under the open-source dataset USPTO＿TPL which contains recorded reactions of up to 1000 different types. The multi-classification accuracy of the model on the testing dataset is 99.382%, showing its great potential for practical use. Besides,a reaction similarity map is presented to correlate the reactions in the USPTO＿TPL dataset based on their sigmaprofile-based statistical features. Finally, representative reactions from the testing dataset are provided to illustrate the model's effectiveness on the reaction classification task.

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Architectures for integrating functional safety into modular process plants

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IFAC-PapersOnLine 2015年第21期48卷 1321-1326页

作者： Pfeffer, Annett Urbas, Leon Chair of Process Control Systems Engineering Technische Universität Dresden Germany

As an approach to cope with rising volatility of sales markets and increasingly shorter development cycles, process industries are expecting much from the concept of modularization. However, a topic that is still rarely focused in this context is safety. Therefore, this paper is focused on the expected challenges of engineering functional safety including the architectures of safety instrumented systems, and the requirements of modularization. Possible architectures are presented and discussed in relation to the requirements of functional safety in modular process plants. © 2015

关键词： Architecture

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Hierarchical RL-MPC for Demand Response Scheduling

arXiv

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arXiv 2025年

作者： Bloor, Maximilian Del Rio Chanona, Ehecatl Antonio Tsay, Calvin Imperial College London Sargent Centre for Process Systems Engineering South Kensington Campus LondonSW7 2AZ United Kingdom Imperial College London Department of Computing South Kensington Campus LondonSW7 2AZ United Kingdom

This paper presents a hierarchical framework for demand response optimization in air separation units (ASUs) that combines reinforcement learning (RL) with linear model predictive control (LMPC). We investigate two control architectures: a direct RL approach and a control-informed methodology where an RL agent provides setpoints to a lower-level LMPC. The proposed RL-LMPC framework demonstrates improved sample efficiency during training and better constraint satisfaction compared to direct RL control. Using an industrial ASU case study, we show that our approach successfully manages operational constraints while optimizing electricity costs under time-varying pricing. Results indicate that the RL-LMPC architecture achieves comparable economic performance to direct RL while providing better robustness and requiring fewer training samples to converge. The framework offers a practical solution for implementing flexible operation strategies in process industries, bridging the gap between data-driven methods and traditional control approaches. © 2025, CC BY-SA.

关键词： Reinforcement learning

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Model-based optimal design of phase change ionic liquids for efficient thermal energy storage

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Green Energy & Environment 2021年第3期6卷 392-404页

作者： Huaiwei Shi Xiang Zhang Kai Sundmacher Teng Zhou Process Systems Engineering Otto-von-Guericke University MagdeburgUniversitätsplatz 2MagdeburgD-39106Germany Process Systems Engineering Max Planck Institute for Dynamics of Complex Technical SystemsSandtorstr.1MagdeburgD-39106Germany

The selection of phase change material(PCM)plays an important role in developing high-efficient thermal energy storage(TES)*** liquids(ILs)or organic salts are thermally stable,non-volatile,and ***,researchers have proved that some ILs possess higher latent heat of fusion than conventional *** these attractive characteristics,yet surprisingly,little research has been performed to the systematic selection or structural design of ILs for ***,most of the existing work is only focused on the latent heat when selecting ***,one should note that other properties such as heat capacity and thermal conductivity could affect the TES performance as *** this work,we propose a computer-aided molecular design(CAMD)based method to systematically design IL PCMs for a practical TES *** effects of different IL properties are simultaneously captured in the IL property models and TES process *** ILs holding a best compromise of all the properties are identified through the solution of a formulated CAMD problem where the TES performance of the process is maximized.[MPyEtOH][TfO]is found to be the best material and excitingly,the identified top nine ILs all show a higher TES performance than the traditional PCM paraffin wax at 10 h thermal charging time.

关键词： Ionic liquid Phase change material Thermal energy storage Computer-aided molecular design process modelling and evaluation

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