Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations
Energy storage technologies have experienced significant advancements in recent decades, driven by the growing demand for efficient and sustainable energy solutions. The limitations associated with lithium’s supply chain, cost, and safety concerns have prompted the exploration of alternative battery...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/10/12/431 |
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| author | Claudio Ronchetti Sara Marchio Francesco Buonocore Simone Giusepponi Sergio Ferlito Massimo Celino |
| author_facet | Claudio Ronchetti Sara Marchio Francesco Buonocore Simone Giusepponi Sergio Ferlito Massimo Celino |
| author_sort | Claudio Ronchetti |
| collection | DOAJ |
| description | Energy storage technologies have experienced significant advancements in recent decades, driven by the growing demand for efficient and sustainable energy solutions. The limitations associated with lithium’s supply chain, cost, and safety concerns have prompted the exploration of alternative battery chemistries. For this reason, research to replace widespread lithium batteries with sodium-ion batteries has received more and more attention. In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. This materials discovery approach is disruptive and significantly faster than traditional physics-based computational methods. |
| format | Article |
| id | doaj-art-14d2a0e8ee9743aaa4b427a66e03818d |
| institution | DOAJ |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-14d2a0e8ee9743aaa4b427a66e03818d2025-08-20T02:57:12ZengMDPI AGBatteries2313-01052024-12-01101243110.3390/batteries10120431Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory CalculationsClaudio Ronchetti0Sara Marchio1Francesco Buonocore2Simone Giusepponi3Sergio Ferlito4Massimo Celino5Telespazio S.p.A., Via Tiburtina 965, 00156 Rome, ItalyItalian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA)—C. R. Casaccia, Via Anguillarese 301, 00123 Rome, ItalyItalian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA)—C. R. Casaccia, Via Anguillarese 301, 00123 Rome, ItalyItalian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA)—C. R. Casaccia, Via Anguillarese 301, 00123 Rome, ItalyItalian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA)—C. R. Portici, Piazzale Enrico Fermi 1, 80055 Portici, ItalyItalian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA)—C. R. Casaccia, Via Anguillarese 301, 00123 Rome, ItalyEnergy storage technologies have experienced significant advancements in recent decades, driven by the growing demand for efficient and sustainable energy solutions. The limitations associated with lithium’s supply chain, cost, and safety concerns have prompted the exploration of alternative battery chemistries. For this reason, research to replace widespread lithium batteries with sodium-ion batteries has received more and more attention. In the present work, we report cutting-edge research, where we explored a wide range of compositions of cathode materials for Na-ion batteries by first-principles calculations using workflow chains developed within the AiiDA framework. We trained crystal graph convolutional neural networks and geometric crystal graph neural networks, and we demonstrate the ability of the machine learning algorithms to predict the formation energy of the candidate materials as calculated by the density functional theory. This materials discovery approach is disruptive and significantly faster than traditional physics-based computational methods.https://www.mdpi.com/2313-0105/10/12/431DFT calculationsneural networksmachine learningelectrochemical energy storageNa-ionhigh-throughput calculations |
| spellingShingle | Claudio Ronchetti Sara Marchio Francesco Buonocore Simone Giusepponi Sergio Ferlito Massimo Celino Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations Batteries DFT calculations neural networks machine learning electrochemical energy storage Na-ion high-throughput calculations |
| title | Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations |
| title_full | Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations |
| title_fullStr | Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations |
| title_full_unstemmed | Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations |
| title_short | Study of Cathode Materials for Na-Ion Batteries: Comparison Between Machine Learning Predictions and Density Functional Theory Calculations |
| title_sort | study of cathode materials for na ion batteries comparison between machine learning predictions and density functional theory calculations |
| topic | DFT calculations neural networks machine learning electrochemical energy storage Na-ion high-throughput calculations |
| url | https://www.mdpi.com/2313-0105/10/12/431 |
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