Data-Driven Design of Lithium-Ion Batteries (D3BATT) — Remaking Battery Science

By Brian Storey

Eight years ago, TRI launched the Accelerated Materials Design and Discovery (AMDD) program, partnering with top universities to develop new tools, models, and methods that push the boundaries of what’s possible at the intersection of AI and materials science.

One of our largest programs, D3BATT, has transformed our fundamental understanding of the inner workings of the modern lithium-ion battery.

Over the past 50 years, our understanding of the lithium-ion battery has matured at two widely disparate length scales. At the large end of the length-scale spectrum is the final battery that you see and hold in your hand. At this length scale, we can consider the battery as a black-box energy storage device where only the overall relationship between the battery voltage and current matters. These easy-to-measure (but hard-to-understand) relationships are the concern of engineers designing electric vehicles. At the tiniest end of the length scale spectrum are atoms, the concern of chemists and material scientists. The atomic scale is fundamental to how a battery works, but we cannot directly observe it because things are incomprehensibly small.

You may have heard specific batteries referred to by a family of acronyms such as LFP, NMC811, and NCA. Each of these acronyms represents the underlying elemental composition of the positive electrode material. For example, LFP stands for lithium iron phosphate (where F stands for iron — Fe in the periodic table). That batteries are named for the elements selected from the periodic table to create the underlying materials is a testament to the criticality of the atomic scale.

However, factors such as energy density, power density, charging times, and lifetime of rechargeable batteries are controlled by processes at neither the hand-held scale nor the atomistic scale, but at the mesoscale (i.e., ~1/1,000 of a millimeter — the size of single biological cells for reference). A grand challenge is to develop a predictive understanding of mesoscale phenomena such as chemical reactions at material interfaces, phase transformations, mechanical deformation, unwanted chemical reactions, and failure due to cracking — to name a few. Such phenomena are microscopic and occur inside the battery during use, making direct observation extremely challenging.

At TRI, one of our largest university investments inside our Accelerated Materials Design and Discovery (AMDD) program was titled “Data-Driven Design of lithium-ion BATTeries” or D3BATT. This initiative sought to transform our understanding of the mesoscale. D3BATT was a multi-institution research center concentrated at Stanford and MIT, with additional projects at Purdue and Washington University at St. Louis. The primary principal investigators were Martin Bazant, Will Chueh, and Richard Braatz. Additional project support came from Edwin Garcia, Evan Reed, Stefano Ermon, and Peng Bai.

Figure 1. Concept image from the D3BATT project team. The diagram shows the length and time scale of different physical phenomena and our tools to understand them. The bottom left is the domain of atoms, whereas we see batteries and cars in the upper right.

Over the past eight years, the D3BATT project developed a novel multiscale framework, guided by machine learning, for understanding these inner “moving parts” of Li-ion batteries. This understanding allowed us to create novel experimental imaging methods, large open databases, and revolutionary new theories. The D3BATT program was built on true collaboration, with different universities joining forces with one another and working together with Toyota to push the boundaries of battery science.

Remaking battery science

The scientific output of the research program was incredible. Results included 65 published papers (with a handful more to come) and 13 Ph.D. theses (with five more to complete this year). But beyond the individual papers and results, the collective portfolio of work has helped to remake our foundational understanding of how batteries work.

Below are three of the comprehensive technical advancements from D3BATT. What makes the advancements remarkable is that each required the full eight-year span of the program to bring to life — true long-term commitments to scientific innovation.

1. Established the foundation of battery informatics.

A completed working battery only has two electrical terminals to access any information. It is easy to measure the voltage and current, but these signals don’t (seemingly) contain much information about what’s really happening on the inside. D3BATT pioneered the field of battery informatics, which is the analysis of subtle voltage and current signals when the battery is in use to infer the internal physical phenomena that are impossible to directly see. Data analysis algorithms developed elsewhere in the field of machine learning were an enabling technology that had not previously been used for battery science.

The first demonstration of this approach was in 2019, where we used machine learning to predict battery lifetime — using only data collected early in the battery’s lifecycle. The technical paper on this topic is one of the most highly recognized in our consortium and was the first to take a rigorous data-centric approach to battery life prediction. Afterwards, we used the early prediction models to optimize fast charging protocols (see technical paper). The combination of early lifetime prediction via machine learning with AI to decide the next best experiment dramatically accelerated the development time. We continued with this approach to rapidly optimize formation processes — formation is one of the most costly manufacturing steps (see technical paper).

We also built a new physically interpretable machine learning framework on top of a very large open-source dataset. The value of the ML framework is that the model outputs have physical meaning and are therefore actionable and improve our understanding. All the work was enabled by the open-source software package BEEP (Battery Evaluation and Early Prediction), which provides automation of experiments and analysis. BEEP was largely developed by software engineering experience at TRI. Through these demonstrations, we comprehensively developed the underlying tools and demonstrations on how to use battery informatics to accelerate battery development. Research continues to add new data streams, such as our recent work on acoustics, that show promise in improving our understanding and prediction capability.

2. Replaced the Butler-Volmer equation — “the” equation for electrochemistry.

When a battery charges and discharges, lithium atoms move from the positive to the negative electrode. At each electrode, the lithium must physically move from the liquid electrolyte into the electrode material. The rate of this transfer process determines the battery’s overall charging rate and performance. For over a century, this reaction rate has been described by the textbook Butler-Volmer equation.

But it turns out that this equation isn’t quite right. There are other important quantum mechanical processes that were first described by Rudolph Marcus, work which won a Nobel Prize in 1992. Over the course of D3BATT, Martin Bazant (Professor of Chemical Engineering and Mathematics, MIT) and his team refined his theory, which integrates Marcus theory into battery science. This new theory, coupled ion-electron transfer (CIET) theory, accurately describes the reaction process in battery materials. While the classic Butler-Volmer theory can sometimes seemingly explain the physics, it must do so with empirically fit parameters that depend on each new battery. CIET theory predicts trends that are different from classic Butler-Volmer and are more experimentally relevant.

An advantage of CIET is that while the underlying theory is quite complex, the final result is a quite simple equation that can be easily implemented in any modern battery model or simulation. Importantly, the theory is directly testable, and one of the important developments in D3BATT was the rigorous testing of mesoscale models. The culminating CIET paper, written in collaboration with another group at MIT, clearly demonstrates the accuracy of the theory for batteries, where classic theory falls short, and how CIET is changing how we understand batteries for the better (see technical paper).

3. Demonstrated (and quantified) the importance of lithium phase separation.

A classic view of the movement of lithium inside the electrode during charge/discharge is that its motion is ruled by diffusion, meaning lithium slowly and evenly spreads through the electrode. In this view, when the battery is 50% charged, the lithium is uniformly distributed throughout the positive and negative electrode material. It is important to understand that a battery electrode is not a single continuous material but is a bunch of tiny particles smushed together. At half charge, the classic view is that each particle has evenly distributed lithium throughout and among all electrode particles.

However, that evenness is not always true. Some materials “phase separate,” as shown in the experimental image of a single LFP electrode particle in Figure 2. A classic diffusion picture would be a smooth transition from red to green. Such material heterogeneities are important to understand since the material swells as lithium moves in and out. Therefore, uneven concentration of lithium ions leads to uneven stress, which leads to fracture and battery degradation. That this kind of phase behavior would exist in batteries was unexpected until it was directly measured by this group of researchers prior to D3BATT.

Figure 2. Experimental observation of phase separation in individual LFP particles. As the battery charges following the increased voltage of the yellow curve, the particle starts full of lithium (red) and then all the lithium leaves (green). In an intermediate state, the particle separates into red and green regions. From https://www.nature.com/articles/s41563-021-01191-0

Within D3BATT, a major advance was inventing new methods of learning the physics of these phase-separating battery materials through inverse learning. Meaning we could take the images like those shown in Figure 2, constrain our understanding by some fundamental physical principles, and then use a data-driven approach to learn the underlying physical laws. In our landmark paper (see technical paper), we used our workflow to determine the thermodynamics of the material and the reaction kinetics of single battery particles of lithium iron phosphate (LFP). The remarkable agreement between the experiment and the learned model across all particles under all conditions leads us to strongly believe in our current understanding; see Figure 3.

Figure 3. Some sample images showing phase separation in a single LFP particle during charge. The particle is smaller than a red blood cell. The comparison between theory and simulation is perfect.

Beyond single particles, the work in D3BATT also formulated and demonstrated that phase separation can occur not only within single particles but also across particles within a group that comprises a battery electrode. Our work demonstrated how population dynamics between particle nearest neighbors take over in some situations (see technical paper). This “fictitious phase separation” can occur in materials that otherwise would not be susceptible to phase separation at the level of single particles. Recent ongoing work shows that both of these effects — intra- and interparticle phase separation — are dominant in graphite, which is the material used for the negative electrode material in all of today’s commercial batteries.

Impact.

While D3BATT focused on battery science fundamentals, the results have many practical impacts. Two of the most important battery materials for today’s electric vehicles (graphite and LFP) are phase-separating and cannot be consistently described by our prior understanding. The new models and understanding of electrochemical thermodynamics developed in D3BATT are required. The models that have been in use for over 30 years do not capture the true nature of the mesoscale. Physically realistic and quantitative models of the mesoscale are needed to build models of battery performance at the engineering scale.

Also, classic battery models are not truly predictive. They have many parameters that must be fitted to experimental data limiting their applicability to new and unseen conditions. By integrating the theory developed through D3BATT into a new modeling framework, we are now able to significantly reduce the number of empirically determined fit parameters in classic battery models, making our predictions closer to first principles.

Finally, while the scientific impact has been important, it is perhaps the impact on the next generation of scientific leaders that matters the most. The D3BATT program trained dozens of graduate students and postdocs who went on to become professors themselves (at least seven to date), founded startups related to battery materials and battery diagnostics, and led new R&D efforts within the battery industry. The impact those people are having on the battery industry is an amplifier of the D3BATT investment.

While much work remains to complete the full vision of D3BATT, we are getting closer to having truly predictive design tools for batteries. I believe that many of the theories and discoveries from this program will be in future textbooks on battery science.

Interested in learning more about our AMDD projects? Click here to see the rest of the series.

Publications from D3BATT

1. Attia, P. M., Das, S., Harris, S. J., Bazant, M. Z., & Chueh, W. C. (2019). Electrochemical Kinetics of SEI Growth on Carbon Black: Part I. Experiments. Journal of The Electrochemical Society, 166(4), E97–E106. https://doi.org/10.1149/2.0231904jes
2. Attia, P. M., Grover, A., Jin, N., Severson, K. A., Markov, T. M., Liao, Y.-H., Chen, M. H., Cheong, B., Perkins, N., Yang, Z., Herring, P. K., Aykol, M., Harris, S. J., Braatz, R. D., Ermon, S., & Chueh, W. C. (2020). Closed-loop optimization of fast-charging protocols for batteries with machine learning. Nature, 578(7795), 397–402. https://doi.org/10.1038/s41586-020-1994-5
3. Aykol, M., Gopal, C. B., Anapolsky, A., Herring, P. K., Van Vlijmen, B., Berliner, M. D., Bazant, M. Z., Braatz, R. D., Chueh, W. C., & Storey, B. D. (2021). Perspective — Combining Physics and Machine Learning to Predict Battery Lifetime. Journal of The Electrochemical Society, 168(3), 030525. https://doi.org/10.1149/1945-7111/abec55
4. Bazant, M. Z. (2023). Unified quantum theory of electrochemical kinetics by coupled ion–electron transfer. Faraday Discussions, 246(0), 60–124. https://doi.org/10.1039/D3FD00108C
5. Berliner, M. D., Jiang, B., Cogswell, D. A., Bazant, M. Z., & Braatz, R. D. (2022a). Fast Charging of Lithium-ion Batteries by Mathematical Reformulation as Mixed Continuous-Discrete Simulation. 2022 American Control Conference (ACC), 5265–5270. https://doi.org/10.23919/ACC53348.2022.9867170
6. Berliner, M. D., Jiang, B., Cogswell, D. A., Bazant, M. Z., & Braatz, R. D. (2022b). Novel Operating Modes for the Charging of Lithium-ion Batteries. Journal of The Electrochemical Society, 169(10), 100546. https://doi.org/10.1149/1945-7111/ac9a80
7. Berliner, M. D., Zhao, H., Das, S., Forsuelo, M., Jiang, B., Chueh, W. H., Bazant, M. Z., & Braatz, R. D. (2021). Nonlinear Identifiability Analysis of the Porous Electrode Theory Model of Lithium-Ion Batteries. Journal of The Electrochemical Society, 168(9), 090546. https://doi.org/10.1149/1945-7111/ac26b1
8. Berliner, M. D., Kim, M., Cui, X., Lam, V. N., Asinger, P. A., Bazant, M. Z., Chueh, W. C., & Braatz, R. D. (2025). Bayesian Analysis of Interpretable Aging across Thousands of Lithium-ion Battery Cycles (No. arXiv:2504.10439). arXiv. https://doi.org/10.48550/arXiv.2504.10439
9. Che, Y., Lam, V. N., Rhyu, J., Schaeffer, J., Kim, M., Bazant, M. Z., Chueh, W. C., & Braatz, R. D. (2025). Diagnostic-free onboard battery health assessment (No. arXiv:2503.07383). arXiv. https://doi.org/10.48550/arXiv.2503.07383
10. Cubuk, E. D., Sendek, A. D., & Reed, E. J. (2019). Screening billions of candidates for solid lithium-ion conductors: A transfer learning approach for small data. The Journal of Chemical Physics, 150(21), 214701. https://doi.org/10.1063/1.5093220
11. Cui, X., Kang, S. D., Wang, S., Rose, J. A., Lian, H., Geslin, A., Torrisi, S. B., Bazant, M. Z., Sun, S., & Chueh, W. C. (2024). Data-driven analysis of battery formation reveals the role of electrode utilization in extending cycle life. Joule, 8(11), 3072–3087. https://doi.org/10.1016/j.joule.2024.07.024
12. Das, S., Attia, P. M., Chueh, W. C., & Bazant, M. Z. (2019). Electrochemical Kinetics of SEI Growth on Carbon Black: Part II. Modeling. Journal of The Electrochemical Society, 166(4), E107–E118. https://doi.org/10.1149/2.0241904jes
13. De Klerk, N. J. J., Vasileiadis, A., Smith, R. B., Bazant, M. Z., & Wagemaker, M. (2017). Explaining key properties of lithiation in TiO 2 -anatase Li-ion battery electrodes using phase-field modeling. Physical Review Materials, 1(2), 025404. https://doi.org/10.1103/PhysRevMaterials.1.025404
14. Deng, H. D., Jin, N., Attia, P. M., Lim, K., Kang, S. D., Kapate, N., Zhao, H., Li, Y., Bazant, M. Z., & Chueh, W. C. (2024). Beyond Constant Current: Origin of Pulse-Induced Activation in Phase-Transforming Battery Electrodes. ACS Nano, 18(3), 2210–2218. https://doi.org/10.1021/acsnano.3c09742
15. Deng, H. D., Zhao, H., Jin, N., Hughes, L., Savitzky, B. H., Ophus, C., Fraggedakis, D., Borbély, A., Yu, Y.-S., Lomeli, E. G., Yan, R., Liu, J., Shapiro, D. A., Cai, W., Bazant, M. Z., Minor, A. M., & Chueh, W. C. (2022). Correlative image learning of chemo-mechanics in phase-transforming solids. Nature Materials, 21(5), 547–554. https://doi.org/10.1038/s41563-021-01191-0
16. Deva, A., Krs, V., Robinson, L. D., Adorf, C. S., Benes, B., Glotzer, S. C., & García, R. E. (2021). Data driven analytics of porous battery microstructures. Energy & Environmental Science, 14(4), 2485–2493. https://doi.org/10.1039/D1EE00454A
17. Finegan, D. P., Zhu, J., Feng, X., Keyser, M., Ulmefors, M., Li, W., Bazant, M. Z., & Cooper, S. J. (2021). The Application of Data-Driven Methods and Physics-Based Learning for Improving Battery Safety. Joule, 5(2), 316–329. https://doi.org/10.1016/j.joule.2020.11.018
18. Fraggedakis, D., McEldrew, M., Smith, R. B., Krishnan, Y., Zhang, Y., Bai, P., Chueh, W. C., Shao-Horn, Y., & Bazant, M. Z. (2021). Theory of coupled ion-electron transfer kinetics. Electrochimica Acta, 367, 137432. https://doi.org/10.1016/j.electacta.2020.137432
19. Fraggedakis, D., Nadkarni, N., Gao, T., Zhou, T., Zhang, Y., Han, Y., Stephens, R. M., Shao-Horn, Y., & Bazant, M. Z. (2020). A scaling law to determine phase morphologies during ion intercalation. Energy & Environmental Science, 13(7), 2142–2152. https://doi.org/10.1039/D0EE00653J
20. Galuppini, G., Berliner, M. D., Cogswell, D. A., Zhuang, D., Bazant, M. Z., & Braatz, R. D. (2023). Nonlinear identifiability analysis of Multiphase Porous Electrode Theory-based battery models: A Lithium Iron Phosphate case study. Journal of Power Sources, 573, 233009. https://doi.org/10.1016/j.jpowsour.2023.233009
21. Galuppini, G., Berliner, M. D., Lian, H., Zhuang, D., Bazant, M. Z., & Braatz, R. D. (2023). Efficient computation of safe, fast charging protocols for multiphase lithium-ion batteries: A lithium iron phosphate case study. Journal of Power Sources, 580, 233272. https://doi.org/10.1016/j.jpowsour.2023.233272
22. Gao, T., Han, Y., Fraggedakis, D., Das, S., Zhou, T., Yeh, C.-N., Xu, S., Chueh, W. C., Li, J., & Bazant, M. Z. (2021). Interplay of Lithium Intercalation and Plating on a Single Graphite Particle. Joule, 5(2), 393–414. https://doi.org/10.1016/j.joule.2020.12.020
23. Geslin, A., Van Vlijmen, B., Cui, X., Bhargava, A., Asinger, P. A., Braatz, R. D., & Chueh, W. C. (2023). Selecting the appropriate features in battery lifetime predictions. Joule, 7(9), 1956–1965. https://doi.org/10.1016/j.joule.2023.07.021
24. Grover, A., Markov, T., Attia, P., Jin, N., Perkins, N., Cheong, B., Chen, M., Yang, Z., Harris, S., Chueh, W., & Ermon, S. (2018). Best arm identification in multi-armed bandits with delayed feedback. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics, 833–842. https://proceedings.mlr.press/v84/grover18b.html
25. Herring, P., Balaji Gopal, C., Aykol, M., Montoya, J. H., Anapolsky, A., Attia, P. M., Gent, W., Hummelshøj, J. S., Hung, L., Kwon, H.-K., Moore, P., Schweigert, D., Severson, K. A., Suram, S., Yang, Z., Braatz, R. D., & Storey, B. D. (2020). BEEP: A Python library for Battery Evaluation and Early Prediction. SoftwareX, 11, 100506. https://doi.org/10.1016/j.softx.2020.100506
26. Hughes, L. A., Savitzky, B. H., Deng, H. D., Jin, N. L., Lomeli, E. G., Yu, Y.-S., Shapiro, D. A., Herring, P., Anapolsky, A., Chueh, W. C., Ophus, C., & Minor, A. M. (2022). Correlative analysis of structure and chemistry of LixFePO4 platelets using 4D-STEM and X-ray ptychography. Materials Today, 52, 102–111. https://doi.org/10.1016/j.mattod.2021.10.031
27. Jana, A., Mitra, A. S., Das, S., Chueh, W. C., Bazant, M. Z., & García, R. E. (2022). Physics-based, reduced order degradation model of lithium-ion batteries. Journal of Power Sources, 545, 231900. https://doi.org/10.1016/j.jpowsour.2022.231900
28. Jana, A., Shaver, G. M., & García, R. E. (2019). Physical, on the fly, capacity degradation prediction of LiNiMnCoO2-graphite cells. Journal of Power Sources, 422, 185–195. https://doi.org/10.1016/j.jpowsour.2019.02.073
29. Jiang, B., Gent, W. E., Mohr, F., Das, S., Berliner, M. D., Forsuelo, M., Zhao, H., Attia, P. M., Grover, A., Herring, P. K., Bazant, M. Z., Harris, S. J., Ermon, S., Chueh, W. C., & Braatz, R. D. (2021). Bayesian learning for rapid prediction of lithium-ion battery-cycling protocols. Joule, 5(12), 3187–3203. https://doi.org/10.1016/j.joule.2021.10.010
30. Kang, S. D., & Chueh, W. C. (2021). Galvanostatic Intermittent Titration Technique Reinvented: Part I. A Critical Review. Journal of The Electrochemical Society, 168(12), 120504. https://doi.org/10.1149/1945-7111/ac3940
31. Kang, S. D., Kuo, J. J., Kapate, N., Hong, J., Park, J., & Chueh, W. C. (2021). Galvanostatic Intermittent Titration Technique Reinvented: Part II. Experiments. Journal of The Electrochemical Society, 168(12), 120503. https://doi.org/10.1149/1945-7111/ac3939
32. Khoo, E., Zhao, H., & Bazant, M. Z. (2019). Linear Stability Analysis of Transient Electrodeposition in Charged Porous Media: Suppression of Dendritic Growth by Surface Conduction. Journal of The Electrochemical Society, 166(10), A2280–A2299. https://doi.org/10.1149/2.1521910jes
33. Kim, M., Schaeffer, J., Berliner, M. D., Pedret Sagnier, B., Bazant, M. Z., Findeisen, R., & Braatz, R. D. (2024). Fast Charging of Lithium-Ion Batteries While Accounting for Degradation and Cell-to-Cell Variability. Journal of The Electrochemical Society, 171(9), 090517. https://doi.org/10.1149/1945-7111/ad76dd
34. Li, W., Bazant, M. Z., & Zhu, J. (2021). A physics-guided neural network framework for elastic plates: Comparison of governing equations-based and energy-based approaches. Computer Methods in Applied Mechanics and Engineering, 383, 113933. https://doi.org/10.1016/j.cma.2021.113933
35. Li, Y., Chen, H., Lim, K., Deng, H. D., Lim, J., Fraggedakis, D., Attia, P. M., Lee, S. C., Jin, N., Moškon, J., Guan, Z., Gent, W. E., Hong, J., Yu, Y.-S., Gaberšček, M., Islam, M. S., Bazant, M. Z., & Chueh, W. C. (2018). Fluid-enhanced surface diffusion controls intraparticle phase transformations. Nature Materials, 17(10), 915–922. https://doi.org/10.1038/s41563-018-0168-4
36. Lian, H., & Bazant, M. Z. (2024). Modeling Lithium Plating Onset on Porous Graphite Electrodes Under Fast Charging with Hierarchical Multiphase Porous Electrode Theory. Journal of The Electrochemical Society, 171(1), 010526. https://doi.org/10.1149/1945-7111/ad1e3d
37. Lund, J., Vikrant, K. S. N., Bishop, C. M., Rheinheimer, W., & García, R. E. (2021). Thermodynamically consistent variational principles for charged interfaces. Acta Materialia, 205, 116525. https://doi.org/10.1016/j.actamat.2020.116525
38. Nadkarni, N., Rejovitsky, E., Fraggedakis, D., Di Leo, C. V., Smith, R. B., Bai, P., & Bazant, M. Z. (2018). Interplay of phase boundary anisotropy and electro-auto-catalytic surface reactions on the lithium intercalation dynamics in Li X FePO 4 plateletlike nanoparticles. Physical Review Materials, 2(8), 085406. https://doi.org/10.1103/PhysRevMaterials.2.085406
39. Nadkarni, N., Zhou, T., Fraggedakis, D., Gao, T., & Bazant, M. Z. (2019). Modeling the Metal–Insulator Phase Transition in Lix CoO2 for Energy and Information Storage. Advanced Functional Materials, 29(40), 1902821. https://doi.org/10.1002/adfm.201902821
40. Park, J., Zhao, H., Kang, S. D., Lim, K., Chen, C.-C., Yu, Y.-S., Braatz, R. D., Shapiro, D. A., Hong, J., Toney, M. F., Bazant, M. Z., & Chueh, W. C. (2021). Fictitious phase separation in Li layered oxides driven by electro-autocatalysis. Nature Materials, 20(7), 991–999. https://doi.org/10.1038/s41563-021-00936-1
41. Rhyu, J., Schaeffer, J., Li, M. L., Cui, X., Chueh, W. C., Bazant, M. Z., & Braatz, R. D. (2025). Systematic feature design for cycle life prediction of lithium-ion batteries during formation. Joule, 9(5), 101884. https://doi.org/10.1016/j.joule.2025.101884
42. Rhyu, J., Zhuang, D., Bazant, M. Z., & Braatz, R. D. (2024). Optimum Model-Based Design of Diagnostics Experiments (DOE) with Hybrid Pulse Power Characterization (HPPC) for Lithium-Ion Batteries. Journal of The Electrochemical Society, 171(7), 070544. https://doi.org/10.1149/1945-7111/ad63ce
43. Samantaray, Y., Cogswell, D. A., Cohen, A. E., & Bazant, M. Z. (2025). Electrochemically resolved acoustic emissions from Li-ion batteries. Joule, 102108. https://doi.org/10.1016/j.joule.2025.102108
44. Schaeffer, J., Lenz, E., Gulla, D., Bazant, M. Z., Braatz, R. D., & Findeisen, R. (2024). Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data. Cell Reports Physical Science, 5(11), 102258. https://doi.org/10.1016/j.xcrp.2024.102258
45. Sendek, A. D., Antoniuk, E. R., Cubuk, E. D., Ransom, B., Francisco, B. E., Buettner-Garrett, J., Cui, Y., & Reed, E. J. (2020). Combining Superionic Conduction and Favorable Decomposition Products in the Crystalline Lithium–Boron–Sulfur System: A New Mechanism for Stabilizing Solid Li-Ion Electrolytes. ACS Applied Materials & Interfaces, 12(34), 37957–37966. https://doi.org/10.1021/acsami.9b19091
46. Sendek, A. D., Cheon, G., Pasta, M., & Reed, E. J. (2020). Quantifying the Search for Solid Li-Ion Electrolyte Materials by Anion: A Data-Driven Perspective. The Journal of Physical Chemistry C, 124(15), 8067–8079. https://doi.org/10.1021/acs.jpcc.9b10650
47. Sendek, A. D., Cubuk, E. D., Antoniuk, E. R., Cheon, G., Cui, Y., & Reed, E. J. (2019). Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials. Chemistry of Materials, 31(2), 342–352. https://doi.org/10.1021/acs.chemmater.8b03272
48. Sendek, A. D., Yang, Q., Cubuk, E. D., Duerloo, K.-A. N., Cui, Y., & Reed, E. J. (2017). Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials. Energy & Environmental Science, 10(1), 306–320. https://doi.org/10.1039/C6EE02697D
49. Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., Bazant, M. Z., Harris, S. J., Chueh, W. C., & Braatz, R. D. (2019). Data-driven prediction of battery cycle life before capacity degradation. Nature Energy, 4(5), 383–391. https://doi.org/10.1038/s41560-019-0356-8
50. Shah, K., Subramaniam, A., Mishra, L., Jang, T., Bazant, M. Z., Braatz, R. D., & Subramanian, V. R. (2020). Editors’ Choice — Perspective — Challenges in Moving to Multiscale Battery Models: Where Electrochemistry Meets and Demands More from Math. Journal of The Electrochemical Society, 167(13), 133501. https://doi.org/10.1149/1945-7111/abb37b
51. Smith, R. B., & Bazant, M. Z. (2017). Multiphase Porous Electrode Theory. Journal of The Electrochemical Society, 164(11), E3291–E3310. https://doi.org/10.1149/2.0171711jes
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Open-source software

1. BEEP. https://tri-amdd.github.io/beep/
2. MPET. https://github.com/TRI-AMDD/mpet
3. PETLION. https://github.com/MarcBerliner/PETLION.jl
4. Hybrid MPET. https://github.com/HarryQL/Hybrid-MPET