Publications

All-solid-state lithium-ion batteries have attracted significant research interest for providing high power and energy densities with enhanced operational safety. Despite the discoveries of solid electrolyte materials with superionic conductivities, it remains a challenge to maintain high rate capability in all-solid lithium-ion batteries in long-term operation. The observed rate degradation has been attributed to reactivity and resistance at the electrode–electrolyte interfaces. We examine interfaces formed between eight electrolytes including garnet, LiPON, and Li10GeP2S12 (LGPS) and seven electrode materials including an NCM cathode and a metallic Li anode and identify the most rapid lithium-ion diffusion pathways through metastable arrangements of product phases that may precipitate out at each interface. Our analysis accounts for possible density functional theory (DFT) error, metastability, and finite-temperature effects by statistically sampling thousands of possible phase diagrams for each interface. The lithium-ion conductivities in the product phases at the interface are evaluated using machine-learned interatomic potentials trained on the fly. In nearly all electrode–electrolyte interfaces we evaluate, we predict that lithium-ion conduction in the product phases making up the interphase region becomes the rate-limiting step for battery performance. READ MORE

Thermal transport phenomena are ubiquitous and play a critical role in the performance of various microelectronic and energy-conversion devices. Binary rocksalt and zinc blende compounds, despite their rather simple crystal structures, exhibit an extraordinary range of lattice thermal conductivity (κL) spanning over 3 orders of magnitude. A comprehensive understanding of the underlying heat transfer mechanism through the development of microscopic theories is therefore of fundamental importance, yet it remains elusive because of the challenges arising from explicitly treating higher-order anharmonicity. Recent theoretical and experimental advances have revealed the essential role of quartic anharmonicity in suppressing heat transfer in zinc blende boron arsenide (BAs) with ultrahigh κL. However, critical questions concerning the general effects of higher-order anharmonicity in the broad classes and chemistries of binary solids are still unanswered. Using our recently developed high-throughput phonon framework based on first-principles density functional theory calculations, we systematically investigate the lattice dynamics and thermal transport properties of 37 binary compounds with rocksalt and zinc blende structures at room temperature, with a particular focus on unraveling the impacts of quartic anharmonicity on κL. Our advanced theoretical model for computing κL embraces current state-of-the-art methods, featuring a complete treatment of quartic anharmonicity for both phonon frequencies and lifetimes at finite temperatures, as well as contributions from off-diagonal terms in the heat-flux operator. We find the impacts of quartic anharmonicity on κL to be strikingly different in rocksalt and zinc blende compounds, owing to the countervailing effects on finite-temperature-induced shifts in phonon frequencies and scattering rates. By correlating κL with the phonon scattering phase space, we outline a qualitative but efficient route to assess the importance of four-phonon scattering from harmonic phonon calculations. Among notable examples, in zinc blende HgTe, we identify an unprecedented sixfold reduction in κL due to four-phonon scattering, which dominates over the three-phonon scattering in the acoustic region at room temperature. We also demonstrate a possible breakdown of the phonon gas model in rocksalt AgCl, wherein the phonon states are significantly broadened due to strong intrinsic anharmonicity, inducing off-diagonal contributions to κL comparable to the diagonal ones. The deep physical insights gained in this work can be used to guide the rational design of thermal management materials. READ MORE

In this work, using the SCAN functional, we develop a simple method on top of the Materials Project (MP) Pourbaix diagram framework to accurately predict the aqueous stability of solids. We extensively evaluate the SCAN functional’s performance in computed formation enthalpies for a broad range of oxides and develop Hubbard U corrections for transition-metal oxides where the standard SCAN functional exhibits large deviations. The performance of the calculated Pourbaix diagram using the SCAN functional is validated with comparison to the experimental and the MP PBE Pourbaix diagrams for representative examples. Benchmarks indicate the SCAN Pourbaix diagram systematically outperforms the MP PBE in aqueous stability prediction. We further show applications of this method in accurately predicting the dissolution potentials of the state-of-the-art catalysts for oxygen evolution reaction in acidic media. READ MORE

The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material’s structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO2 and IrO3. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach, we find 196 IrO2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable, termed α-IrO3. To test the algorithms performance, we compare to a random search of the candidate space and report at least a 2-fold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with fewer than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low-energy structures. Subsequent Pourbaix Ir–H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO2. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO3 than on any other considered iridium oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry. READ MORE

TRI Authors: Muratahan Aykol, Patrick Herring, & Abraham Anapolsky All Authors: Muratahan Aykol, Patrick Herring, & Abraham Anapolsky

Batteries, as complex materials systems, pose unique challenges for the application of machine learning. Although a shift to data-driven, machine learning-based battery research has started, new initiatives in academia and industry are needed to fully exploit its potential.  Read more

Citation: Aykol, Muratahan, Patrick Herring, Abraham Anapolsky. “Machine learning for continuous innovation in battery technologies.” Nature Reviews Materials (2020). https://doi.org/10.1038/s41578-020-0216-y 

Hydrogen peroxide is a valuable chemical oxidant with a wide range of applications in a variety of industrial processes, especially in water sanitization. Electrochemical synthesis of hydrogen peroxide (H2O2) through a two-electron oxygen reduction reaction (2e-ORR) or a two-electron water oxidation reaction (2e-WOR) has emerged as an appealing process for onsite production of this chemically valuable oxidant. On-site produced H2O2 can be applied for wastewater treatment in remote locations or any applications where H2O2 is needed as an oxidizing agent. This Review studies the theoretical efforts in understanding the challenges in catalysis for electrochemical synthesis of H2O2 as well as providing design principles for more efficient catalyst materials. READ MORE

TRI Authors: Muratahan Aykol, Joseph Montoya, Jens Hummelshøj

All Authors: Roni Choudhury, Muratahan Aykol, Samuel Gratzl, Joseph Montoya, Jens Hummelshøj

Materials science research deals primarily with understanding the relationship between the structure and properties of materials. With recent advances in computational power and automation of simulation techniques, material structure and property databases have emerged (Curtarolo et al., 2012; Jain et al., 2013; Kirklin et al., 2015), allowing a more data-driven approach to carrying out materials research. Recent studies have demonstrated that representing these databases as material networks can enable extraction of new materials knowledge (Hegde, Aykol, Kirklin, & Wolverton, 2018; Isayev et al., 2015) or help tackle challenges like predictive synthesis (Aykol, Hegde, et al., 2019) that require relational information between materials. Materials databases have become very popular because they enable their users to do rapid prototyping by searching near globally for figures of merit for their target application. However, both scientists and engineers have little in the way of visualization of aggregates from these databases, that is, intuitive layouts that help understand which materials are related and how they are related. The need for a tool that does this is particularly crucial in materials science because properties like phase stability and crystal structure similarity are themselves functions of a material dataset, rather than of individual materials. Read More

Citation: Choudhury, Roni, Muratahan Aykol, Samuel Gratzl, Joseph Montoya, and Jens Hummelshøj. "MaterialNet: A web-based graph explorer for materials science data." Journal of Open Source Software 5, no. 47 (2020): 2105.

TRI Authors: Muratahan Aykol*

All Authors: Vinay Hegde, Muratahan Aykol*, Scott Kirklin, Chris Wolverton*

One of the holy grails of materials science, unlocking structure-property relationships, has largely been pursued via bottom-up investigations of how the arrangement of atoms and interatomic bonding in a material determine its macroscopic behavior. Here, we consider a complementary approach, a top-down study of the organizational structure of networks of materials, based on the interaction between materials themselves. We unravel the complete “phase stability network of all inorganic materials” as a densely connected complex network of 21,000 thermodynamically stable compounds (nodes) interlinked by 41 million tie line (edges) defining their two-phase equilibria, as computed by high-throughput density functional theory. Analyzing the topology of this network of materials has the potential to uncover previously unidentified characteristics inaccessible from traditional atoms-to-materials paradigms. Using the connectivity of nodes in the phase stability network, we derive a rational, data-driven metric for material reactivity, the “nobility index,” and quantitatively identify the noblest materials in nature. Read More

Citation: Hegde, Vinay I., Muratahan Aykol, Scott Kirklin, and Chris Wolverton. "The phase stability network of all inorganic materials." Science Advances 6, no. 9 (2020): eaay5606.

TRI Authors: Patrick K Herring, Muratahan Aykol

All Authors: Peter M Attia, Aditya Grover, Norman Jin, Kristen A Severson, Todor M Markov, Yang-Hung Liao, Michael H Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick K Herring, Muratahan Aykol, Stephen J Harris, Richard D Braatz, Stefano Ermon, William C Chueh

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3,4,5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces. Read More

Citation: Attia, Peter M., Aditya Grover, Norman Jin, Kristen A. Severson, Todor M. Markov, Yang-Hung Liao, Michael H. Chen et al. "Closed-loop optimization of fast-charging protocols for batteries with machine learning." Nature 578, no. 7795 (2020): 397-402.

TRI Author: Brian D. Storey

All Authors: Hongbo Zhao, Brian D. Storey, Richard D. Braatz, and Martin Z. Bazant

Using a framework of partial differential equation-constrained optimization, we demonstrate that multiple constitutive relations can be extracted simultaneously from a small set of images of pattern formation. Examples include state-dependent properties in phase-field models, such as the diffusivity, kinetic prefactor, free energy, and direct correlation function, given only the general form of the Cahn-Hilliard equation, Allen-Cahn equation, or dynamical density functional theory (phase-field crystal model). Constraints can be added based on physical arguments to accelerate convergence and avoid spurious results. Reconstruction of the free energy functional, which contains nonlinear dependence on the state variable and differential or convolutional operators, opens the possibility of learning nonequilibrium thermodynamics from only a few snapshots of the dynamics. Read More

Citation: Zhao, Hongbo, Brian D. Storey, Richard D. Braatz, and Martin Z. Bazant. "Learning the Physics of Pattern Formation from Images." Physical Review Letters 124, no. 6 (2020): 060201.