1
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Yamaguchi Y, Atsumi T, Kanamori K, Tanibata N, Takeda H, Nakayama M, Karasuyama M, Takeuchi I. Drawing a materials map with an autoencoder for lithium ionic conductors. Sci Rep 2023; 13:16799. [PMID: 37798325 PMCID: PMC10556005 DOI: 10.1038/s41598-023-43921-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 09/29/2023] [Indexed: 10/07/2023] Open
Abstract
Efforts to optimize known materials and enhance their performance are ongoing, driven by the advancements resulting from the discovery of novel functional materials. Traditionally, the search for and optimization of functional materials has relied on the experience and intuition of specialized researchers. However, materials informatics (MI), which integrates materials data and machine learning, has frequently been used to realize systematic and efficient materials exploration without depending on manual tasks. Nonetheless, the discovery of new materials using MI remains challenging. In this study, we propose a method for the discovery of materials outside the scope of existing databases by combining MI with the experience and intuition of researchers. Specifically, we designed a two-dimensional map that plots known materials data based on their composition and structure, facilitating researchers' intuitive search for new materials. The materials map was implemented using an autoencoder-based neural network. We focused on the conductivity of 708 lithium oxide materials and considered the correlation with migration energy (ME), an index of lithium-ion conductivity. The distribution of existing data reflected in the materials map can contribute to the development of new lithium-ion conductive materials by enhancing the experience and intuition of material researchers.
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Affiliation(s)
- Yudai Yamaguchi
- Department of Advanced Ceramics, Nagoya Institute of Technology, Gokiso, Showa, Nagoya, Aichi, 466-8555, Japan
| | - Taruto Atsumi
- Department of Advanced Ceramics, Nagoya Institute of Technology, Gokiso, Showa, Nagoya, Aichi, 466-8555, Japan
| | - Kenta Kanamori
- Department of Computer Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555, Japan
| | - Naoto Tanibata
- Department of Advanced Ceramics, Nagoya Institute of Technology, Gokiso, Showa, Nagoya, Aichi, 466-8555, Japan
| | - Hayami Takeda
- Department of Advanced Ceramics, Nagoya Institute of Technology, Gokiso, Showa, Nagoya, Aichi, 466-8555, Japan
| | - Masanobu Nakayama
- Department of Advanced Ceramics, Nagoya Institute of Technology, Gokiso, Showa, Nagoya, Aichi, 466-8555, Japan.
| | - Masayuki Karasuyama
- Department of Computer Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555, Japan
| | - Ichiro Takeuchi
- Department of Computer Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, Aichi, 466-8555, Japan
- RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihonbashi, Chuo-ku, Tokyo, 103-0027, Japan
- Faculty of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464-8601, Japan
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3
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Lombardo T, Duquesnoy M, El-Bouysidy H, Årén F, Gallo-Bueno A, Jørgensen PB, Bhowmik A, Demortière A, Ayerbe E, Alcaide F, Reynaud M, Carrasco J, Grimaud A, Zhang C, Vegge T, Johansson P, Franco AA. Artificial Intelligence Applied to Battery Research: Hype or Reality? Chem Rev 2021; 122:10899-10969. [PMID: 34529918 PMCID: PMC9227745 DOI: 10.1021/acs.chemrev.1c00108] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
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This is a critical
review of artificial intelligence/machine learning
(AI/ML) methods applied to battery research. It aims at providing
a comprehensive, authoritative, and critical, yet easily understandable,
review of general interest to the battery community. It addresses
the concepts, approaches, tools, outcomes, and challenges of using
AI/ML as an accelerator for the design and optimization of the next
generation of batteries—a current hot topic. It intends to
create both accessibility of these tools to the chemistry and electrochemical
energy sciences communities and completeness in terms of the different
battery R&D aspects covered.
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Affiliation(s)
- Teo Lombardo
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France
| | - Marc Duquesnoy
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France
| | - Hassna El-Bouysidy
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden
| | - Fabian Årén
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden
| | - Alfonso Gallo-Bueno
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Spain
| | - Peter Bjørn Jørgensen
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, Building 301, 2800 Kgs. Lyngby, Denmark
| | - Arghya Bhowmik
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, Building 301, 2800 Kgs. Lyngby, Denmark
| | - Arnaud Demortière
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France
| | - Elixabete Ayerbe
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain
| | - Francisco Alcaide
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain
| | - Marine Reynaud
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Spain
| | - Javier Carrasco
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Spain
| | - Alexis Grimaud
- Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,UMR CNRS 8260 "Chimie du Solide et Energie", Collège de France, 11 Place Marcelin Berthelot, 75231 Paris Cedex 05, France Sorbonne Universités - UPMC Univ Paris 06, 4 Place Jussieu, F-75005 Paris, France
| | - Chao Zhang
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Chemistry - Ångström Laboratory, Box 538, 75121 Uppsala, Sweden
| | - Tejs Vegge
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, Building 301, 2800 Kgs. Lyngby, Denmark
| | - Patrik Johansson
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden
| | - Alejandro A Franco
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Institut Universitaire de France, 103 Boulevard Saint Michel, 75005 Paris, France
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4
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He B, Ye A, Chi S, Mi P, Ran Y, Zhang L, Zou X, Pu B, Zhao Q, Zou Z, Wang D, Zhang W, Zhao J, Avdeev M, Shi S. CAVD, towards better characterization of void space for ionic transport analysis. Sci Data 2020; 7:153. [PMID: 32444597 PMCID: PMC7244509 DOI: 10.1038/s41597-020-0491-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 04/15/2020] [Indexed: 12/03/2022] Open
Abstract
Geometric crystal structure analysis using three-dimensional Voronoi tessellation provides intuitive insights into the ionic transport behavior of metal-ion electrode materials or solid electrolytes by mapping the void space in a framework onto a network. The existing tools typically consider only the local voids by mapping them with Voronoi polyhedra vertices and then define the mobile ions pathways using the Voronoi edges connecting these vertices. We show that in some structures mobile ions are located on Voronoi polyhedra faces and thus cannot be located by a standard approach. To address this deficiency, we extend the method to include Voronoi faces in the constructed network. This method has been implemented in the CAVD python package. Its effectiveness is demonstrated by 99% recovery rate for the lattice sites of mobile ions in 6,955 Li-, Na-, Mg- and Al-containing ionic compounds extracted from the Inorganic Crystal Structure Database. In addition, various quantitative descriptors of the network can be used to identify and rank the materials and further used in materials databases for machine learning.
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Affiliation(s)
- Bing He
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Anjiang Ye
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Shuting Chi
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Penghui Mi
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Yunbing Ran
- State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China
| | - Liwen Zhang
- State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China
| | - Xinxin Zou
- School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China
| | - Bowei Pu
- State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China
| | - Qian Zhao
- Materials Genome Institute, Shanghai University, Shanghai, 200444, China
| | - Zheyi Zou
- State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China
| | - Da Wang
- State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China
| | - Wenqing Zhang
- Department of Physics and Shenzhen Institute for Quantum Science & Technology, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Jingtai Zhao
- School of Materials Science and Engineering, Guilin University of Electronic Technology, Guilin, 541004, China
| | - Maxim Avdeev
- Australian Nuclear Science and Technology Organisation, Locked Bag 2001, Kirrawee DC, NSW, 2232, Australia
- School of Chemistry, The University of Sydney, Sydney, 2006, Australia
| | - Siqi Shi
- State Key Laboratory of Advanced Special Steel, School of Materials Science and Engineering, Shanghai University, Shanghai, 200444, China.
- Materials Genome Institute, Shanghai University, Shanghai, 200444, China.
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8
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Matsuda S, Nishioka K, Nakanishi S. High-throughput combinatorial screening of multi-component electrolyte additives to improve the performance of Li metal secondary batteries. Sci Rep 2019; 9:6211. [PMID: 30996343 PMCID: PMC6470175 DOI: 10.1038/s41598-019-42766-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 04/03/2019] [Indexed: 11/14/2022] Open
Abstract
Data-driven material discovery has recently become popular in the field of next-generation secondary batteries. However, it is important to obtain large, high quality data sets to apply data-driven methods such as evolutionary algorithms or Bayesian optimization. Combinatorial high-throughput techniques are an effective approach to obtaining large data sets together with reliable quality. In the present study, we developed a combinatorial high-throughput system (HTS) with a throughput of 400 samples/day. The aim was to identify suitable combinations of additives to improve the performance of lithium metal electrodes for use in lithium batteries. Based on the high-throughput screening of 2002 samples, a specific combination of five additives was selected that drastically improved the coulombic efficiency (CE) of a lithium metal electrode. Importantly, the CE was remarkably decreased merely by removing one of these components, highlighting the synergistic basis of this mixture. The results of this study show that the HTS presented herein is a viable means of accelerating the discovery of ideal yet complex electrolytes with multiple components that are very difficult to identify via conventional bottom-up approach.
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Affiliation(s)
- Shoichi Matsuda
- Global Research Center for Environment and Energy based on Nanomaterials Science, National Institute of Material Science, 1-1 Namiki, Tsukuba, Ibaraki, 305-0044, Japan.
| | - Kiho Nishioka
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka, 560-8531, Japan
| | - Shuji Nakanishi
- Graduate School of Engineering Science, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka, 560-8531, Japan.,Research Center for Solar Energy Chemistry, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka, 560-8531, Japan
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