Высокопроизводительный вычислительный поиск новых материалов для твердотельных аккумуляторов (High-throughput computational search for new solid-state battery materials) тема диссертации и автореферата по ВАК РФ 00.00.00, кандидат наук Дембицкий Артем Дмитриевич

• Дембицкий Артем Дмитриевич
• кандидат наук
• 2025

Оглавление диссертации кандидат наук Дембицкий Артем Дмитриевич

Table of contents

Introduction

Chapter 1. Literature review

1.1 Challenges in solid-state batteries development

1.2 Solid electrolytes and protective layers

1.3 Mechanisms and properties governing ion conduction

1.4 Atomistic modeling of solid electrolytes

1.4.1 Density functional theory

1.4.2 Machine learning interatomic potentials

1.5 Accelerated discovery of solid electrolytes and protective coatings

1.6 Machine learning models for predicting ionic mobility

1.7 Concluding remarks

Chapter 2. Methods

2.1 Density functional theory calculations

2.1.1 Electrochemical stability calculations

2.1.2 Ab initio molecular dynamics

2.1.3 Dopability calculations

2.2 Bond valence site energy method

2.3 Nudged elastic band calculations

2.4 Machine learning-assisted molecular dynamics

2.5 Ionic diffusivity and conductivity

2.6 Concerted migration analysis

2.7 Ionic conductivity measurements

2.8 Powder X-ray diffraction

2.9 Percolating graph detection

2.10 Sampling Li-ion migration hops

2.11 Data split

2.12 Feature design and selection

2.13 Models training and evaluation

2.14 MLIPs description and evaluation

2.15 uMLIPs fine-tuning

2.16 Crystal structures

Chapter 3. Screening of NH+-based precursors for designing solid

electrolytes for Na-ion batteries

3.1 Screening algorithm

3.2 BVEL filtered candidate overview

3.3 Electrochemical stability and Na-ion mobility of selected candidates

3.3.1 Langbeinite-type NaZr2(PO4)3

3.3.2 KTiOPO4-structured NaGaPO4F

3.4 Outlook

Chapter 4. Na-ion diffusion mechanisms in KTiOPO4-structured

NaGaPO4F

4.1 AIMD study of Na-ion diffusion

4.2 Analysis of Na-ion concerted migration

4.3 Large-scale MD study of Na-ion diffusion

4.4 Atomistic description of Na frenkel defect dynamics

4.5 Aliovalent substitutions

4.6 Practical application

Chapter 5. Datasets and machine learning models for accelerated

search of fast Li-ion conductors

5.1 Percolation barrier prediction with the BVEL13k dataset

5.2 Migration barrier prediction with nebBVSE122k and nebDFT2k datasets

5.3 Benchmarking universal interatomic potentials with the nebDFT2k

and MPLiTrj datasets

5.4 Practical application of uMLIPs

5.5 Future work and limitations

Conclusions

List of Symbols, Abbreviations

References

List of figures

List of tables

Appendix A. Screening of NH+-based precursors for designing

solid electrolytes for Na-ion batteries

Appendix B. Na-ion diffusion in NaGaPO4F

B.1 MLMD

B.2 Concerted migration

Appendix C. Datasets and machine learning models for

accelerated search of fast Li-ion conductors

Введение диссертации (часть автореферата) на тему «Высокопроизводительный вычислительный поиск новых материалов для твердотельных аккумуляторов (High-throughput computational search for new solid-state battery materials)»

Introduction

Work relevance. Li-ion secondary batteries (LIBs), originally developed for portable electronics, play a critical role in advancing the widespread development of electric vehicles (EVs) [1], contributing to reduced CO2 emissions on a lifecycle basis. However, the development of alternative energy storage technologies has become essential due to the geochemical constraints of elements used for LIBs production (e.g. Li, Co, Ni, Cu) and supply chain vulnerabilities [2; 3], the energy density of modern LIBs approaching its practical limit (265 Wh kg-1 for a battery cell utilizing LiNi0.gCo015Al0.05O2 (NCA) versus graphite) [4; 5], and risk of liquid leakage and ignition due to use of organic liquid electrolytes [6].

To mitigate the abovementioned concerns, current research priorities focus on developing Li/Na-ion solid-state batteries (SSBs) with non-flammable solid electrolyte materials [7]. This architecture enables more compact device designs allowing bipolar stacking with higher practical energy density of the battery cell (393 Wh kg-1 for a battery cell utilizing NCA versus lithium metal) [4; 5] and enhanced fire safety by eliminating risks associated with liquid electrolyte leakage upon battery casing damage. Despite the theoretical advantages of the SSBs technology, key challenges remain unresolved for its practical implementation, including stabilizing the solid electrolyte interface (SEI), improving mechanical stability, and enhancing long-term cycling performance.

Improving the energy density, charge rate and stability of a solid-state battery can be achieved by innovations in the architecture of devices and/or material components [8]. Therefore, the accelerated search for solid-electrolyte materials for developing high-performance long-life SSBs is required.

The goal of the current work is to develop and validate accelerated high-throughput data-driven approaches for the discovery, optimization, and selection of solid-electrolytes for Li/Na-ion all-solid state batteries. The following problems were addressed to achieve the goal:

1. To develop screening algorithms for discovery and selection of solid electrolytes using density functional theory (DFT) and surrogate models.

2. To identify new solid electrolyte candidates using the developed methodology.

3. To calculate ionic conductivity and determine ionic conductivity mechanisms of selected candidate(s) and strategies for enhancing their transport properties.

4. To build a dataset and methodology for benchmarking machine learning (ML) models for accelerated search of fast ionic conductors.

5. To demonstrate the applicability of the developed ML-based methodology for screening ionic conductors for SSBs.

Scientific novelty:

1. A novel data-driven algorithm for screening NH4-based inorganic precursors suitable for synthesizing new electrode materials and solid electrolytes with high Na+ mobility is developed.

2. A new class of solid electrolytes, KTiOPO4-structured NaGaPO4F, is discovered. For the first time, Na+ diffusion mechanisms in the KTP-structured material are determined through combined ab initio and machine learning-assisted molecular dynamics simulations (AIMD and MLMD, respectively).

3. A new dataset for benchmarking ML models for predicting Li+ migration, calculated using DFT and bond valence site energy (BVSE) methods, is developed.

4. The accuracy of universal machine learning interatomic potentials (uMLIPs) in predicting DFT-derived Li+ migration pathways and corresponding energetics in crystals is evaluated for the first time.

5. A new algorithm for screening protective coatings for Li-ion SSBs using uMLIPs for identifying candidates with high Li-ion mobility is developed and validated.

Main results submitted for the defense:

1. The developed algorithm for screening NH4-based inorganic precursors enables the identification of suitable compounds for synthesizing new solid electrolyte materials with high Na-ion mobility.

2. NaGaPO4F represents a new class of KTiOPO4-structured solid electrolytes with concerted Na-ion diffusion mediated by Na vacancies, exhibiting an unprecedented ultra-low Na-ion migration barrier of 0.11-0.18 eV according to DFT, AIMD and MLMD studies. Extrinsic Na vacancies of 6.25 % in the material yield a room-temperature conductivity of ~0.01 S cm-1.

According to DFT calculations, Na vacancies of sufficient concentration can be introduced via Ga-to-Sn/Zr and Na-to-Ba/Sr aliovalent substitution.

3. State-of-the-art uMLIPs reach near-DFT accuracy in predicting optimal Li+ migration trajectories and corresponding migration barriers, enabling efficient high-throughput identification of inorganic crystalline materials with high Li+ mobility.

Theoretical and practical significance. The theoretical significance of this work consists in developing and validating new computational algorithms for screening fast ionic conductors for SSBs. The practical significance is demonstrated by the discovery of candidate Na-ion electrolytes through the developed computational screening. The experimentally measured properties of these electrolytes show reasonable agreement with theoretical predictions. This research has yielded a suite of specialized Python libraries: BVlain for computing mobile ion percolation barriers via the BVSE method, ions for finding symmetrically unique ionic jumps forming macroscopic percolation network, mlyzed for processing and analyzing cooperative migration events in MD trajectories, and LiTraj for handling the custom datasets developed in this work. These datasets represent a valuable community resource, providing standardized benchmarks for evaluating new models targeting fast ion conductor screening.

Methodology and research methods. The international experimental and theoretical databases containing structural, electronic, and thermodynamic data were used for selecting objects of the study. The modern quantum chemistry, empirical, and machine learning methods were used for predicting physical chemistry properties of the model systems, including density functional theory (DFT), ab initio molecular dynamics simulations, large-scale molecular dynamics simulations employing machine learning interatomic potentials, classical machine learning models and graph neural networks for regression problems.

The validity and reliability of the results of the thesis is confirmed as follows. All developed and used methods are described in detail in the dissertation. The developed methods are implemented in publicly available Python libraries to reproduce the results of this study. The results of the DFT calculated formation energies and migration barriers for benchmark systems are in agreement with published experimental observations and computational results obtained by other authors using similar computational scheme.

Approbation. The main results on the topic of the dissertation resulted in 3 publications in periodical scientific journals or publications indexed by Web of Science or Scopus [9—11]. The results of the dissertation were presented at the following international conferences:

- The 8-th International Conference on Sodium Batteries (ICNaB 2023), China, Liyang, September 21-24, 2023

- The 2nd Sino-Russian Symposium on Chemistry and Materials, Russia, Moscow, May 29-June 1, 2024

- Matter and Materials, Russia, Moscow, March 3-4, 2025

The author's personal contributions include the formulation of research goals and tasks, design and execution of computational studies, development of computational algorithms, and comprehensive analysis of the results, all conducted under the supervision of his scientific advisor and consultant. The experimental results presented in this work were obtained by Sergey Marshenya (Skolkovo Institute of Science and Technology).

Dissertation structure. The dissertation consists of an abstract, introduction, 4 chapters, conclusions, lists of symbols, abbreviations, figures, tables, and appendices. The dissertation is 155 pages long, including 32 figures, and 28 tables. The list of references contains 228 titles.

Organization of the Dissertation. The dissertation is organized as follows. Chapter 1 overviews current challenges in developing SSBs and approaches to atomistic modeling of battery materials. Chapter 2 details the computational methods employed in this study. Chapter 3 presents the developed algorithm for screening NH+-based precursors suitable for synthesizing materials with high Na+ mobility, identifying KTP-structured NH4GaPO4F and langbeinite NH4Zr2(PO4)3 as promising precursors for Na-based solid electrolytes. Chapter 4 elucidates Na-ion diffusion mechanisms in NaGaPO4F using AIMD and MLMD simulations, while proposing strategies to enhance its ionic conductivity. Chapter 5 introduces benchmark datasets for evaluating ML models' accuracy in predicting Li-ion migration and percolation barriers in ionic crystals, demonstrating their practical application for screening protective coatings for Li-ion SSBs. The final chapter summarizes the main results of the study.

Заключение диссертации по теме «Другие cпециальности», Дембицкий Артем Дмитриевич

Conclusions

This work focuses on developing computational approaches for high-throughput discovery, selection, and optimization of fast-ion conductors for solid-state batteries - a safer, higher-energy-density alternative to conventional liquid-electrolyte metal-ion batteries. For this, we employ a multiscale modeling framework combining quantum-chemistry calculations, empirical methods, and machine learning approaches to predict ionic transport properties. The main results of the work are as follows.

We present a computational approach for screening NH4-based precursors suitable for synthesizing novel solid electrolytes with high Na+ mobility via ion-exchange. By adapting DFT and BVSE methods to model isomorphous ion exchange for the first time, we validate our approach through the discovery of NaGaPO4F. This material constitutes a novel class of KTiOPO4-structured solid electrolytes, exhibiting high Na+ mobility, negligible electronic conductivity, an electrochemical stability window of 1.78-3.84 V vs. Na/Na+, and kinetic oxidation stability up to 4.88 V vs. Na/Na+.

Our AIMD simulations reveal that the single and concerted Na-ion migration mediated by Na vacancies is responsible for the unprecedented ultra-low Na-ion activation barrier of 0.11-0.16 eV in NaGaPO4F. Extrinsic Na vacancies of 6.25% in the material yield a room-temperature conductivity of ~0.01 S cm-1. Through the large-scale MD simulations using ML interatomic potentials specifically trained for NaGaPO4F, we demonstrate that Na+ diffusion in the stoichiometric material occurs via Frenkel pair formation followed by vacancy migration. We determine the defect formation energy of ^0.9 eV, which accounts for the poor ionic conductivity observed in the stoichiometric phase and explains its low experimental conductivity values. According to DFT predictions, Na vacancies of sufficient concentration can be introduced via through aliovalent substitution (e.g., Ba2+ for Na+ or Zr4+/Sn4+ for Ga3+).

We introduce novel datasets computed at the BVSE and DFT levels of theory, enabling a systematic comparative analysis of various ML models for predicting Li-ion percolation barriers, migration barriers, and migration trajectories. GNN models developed for structure-to-property prediction of the BVSE-calculated percolation and migration barriers are effective for high-throughput screenings

of Li-ion conductors. Classical ML and GNN structure-to-property models benchmarked on the DFT-derived dataset can be utilized for pre-screening "good" and "bad" ionic conductors.

With the DFT-derived dataset, for the first time, we evaluate the accuracy of state-of-the-art uMLIPs for finding optimal Li-ion migration trajectories and migration barriers in ionic crystals, and show that SevenNet and MACE-MP-0 uMLIPs demonstrate near-DFT accuracy. This performance enables their integration into high-throughput schemes for the accelerated search for fast Li-ion conductors.

The developed datasets can serve as a valuable resource for further development, evaluation and selection of data-driven approaches for designing Li-ion conductors, thereby accelerating the discovery of materials with outstanding transport properties. At the same time, the developed methodology can be adopted for other alkali-ions.

We foresee two main directions for future developments. First, although new methods for generating crystal structures are emerging, the discovery rate of novel superionic conductors remains low and appears largely decoupled from advances in structural generation techniques. This highlights the need for dedicated methods to predict and design superionic conductors systematically. Second, and complementary to the first, there is a critical need for AIMD-derived datasets that capture ion diffusion across diverse crystal structures. Such datasets would enable the development of structure-to-property models for direct prediction of ionic conductivity and the validation of uMLIPs.

Список литературы диссертационного исследования кандидат наук Дембицкий Артем Дмитриевич, 2025 год

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