The kinetics of the κ(ε) to β-Ga2O3 phase transformation were investigated in five batches of nominally phase-pure κ(ε)-Ga2O3 thin films heteroepitaxially grown on c-plane sapphire, with film thickness ranging from 700 to 1100 nm, using in situ high-temperature x-ray diffraction. Phase fractions were quantitatively extracted through modified Rietveld refinement that accounts for preferred orientation, and the transformation kinetics were analyzed using the Johnson–Mehl–Avrami–Kolmogorov (JMAK) model. The applicability of the JMAK model to thin-film materials was evaluated, and its lower and upper bounds for thin films and bulk materials were established. Based on this analysis, a model specifically suited for thin-film kinetic studies was developed and yielded reproducible and robust results across all five sample batches. The results indicate that the κ(ε) to β phase transformation in ∼700–1100 nm films is best described as a diffusionless transformation comprising local atomic rearrangements with effectively two-dimensional (2D) growth, as evidenced by an Avrami exponent near 2 throughout the transformation interval.

In this study, we investigate the effect of Mg incorporation on the relative phase stability of the four primary Ga2O3 polymorphs using density functional theory (DFT) calculations, with the goal of rationalizing experimental observations suggesting that diffusion from MgAl2O4 substrates contributes to the relative stabilization of the γ phase. Mg incorporation is modeled up to 25% of Ga sites within supercells derived from fully relaxed unit cells of each polymorph. Our results show that, while β-Ga2O3 remains the thermodynamically most stable phase, the enthalpy and free energy differences between polymorphs decrease with increasing Mg content. The inherently disordered γ phase, with its high configurational entropy, becomes less energetically unfavorable under Mg substitution, suggesting that entropy-driven stabilization may facilitate its formation under high-temperature and/or nonequilibrium growth conditions, such as those previously reported. These findings provide a thermodynamic rationale for the experimental observation of the γ phase during epitaxial growth on MgAl2O4 spinel substrates.

In this study, we investigate the effect of Mg incorporation on the relative phase stability of the four primary Ga_2O_3 polymorphs using density functional theory (DFT) calculations, with the goal of rationalizing experimental observations suggesting that diffusion from MgAl_2O_4 substrates contributes to relative stabilization of the γ phase. Mg incorporation is modeled up to 25

The kinetics of the κ to β-Ga_2O_3 phase transformation were investigated in five batches of nominally phase-pure κ-Ga2O3 thin films heteroepitaxially grown on c-plane sapphire, with film thickness ranging from 700 to 1100 nm, using in-situ high-temperature X-ray diffraction. Phase fractions were quantitatively extracted through modified Rietveld refinement that accounts for preferred orientation, and the transformation kinetics were analyzed using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model. The applicability of the JMAK model to thin-film materials was evaluated and its lower and upper bounds for thin films and bulk materials were established. Based on this analysis, a method specifically suited for thin-film kinetic studies was developed and yielded reproducible and robust results across all five sample batches. The results indicate that the κ to β phase transformation in 700-1100 nm films is best described as an interface-controlled, site-saturated nucleation with thickness-limited or effectively two-dimensional growth.

Optimizing process parameters for directed energy deposition is crucial to achieve high-quality printed parts. However, this optimization process often entails significant time and cost investments. An initial investigation into the process window can be conducted through the examination of single tracks. In this work, we investigate the utility of constraint active search (CAS) to efficiently identify process window that yield 4340 low-alloy steel single tracks with desired geometrical features. The effectiveness of the CAS method was assessed through experiments with physical and interpolated measurement. Fifty single tracks from randomly sampled process parameter combinations with different power, scan velocity, and laser spot size and ten single tracks from CAS-generated parameters were produced and analyzed. The results demonstrate that our search method outperforms random search, with 80% of parameter sets identified as desirable compared to only 4% in the case of random search. Moreover, an interpolated ground truth in input spaces of various dimensionalities was built in order to assess repeatability without excessive experimental cost. The results indicate that the CAS achieves higher precision compared to grid search and random search, especially in higher-dimensional process parameter spaces.

In this work, we develop a twist-dependent electrochemical activity map, combining a low-energy continuum electronic structure model with modified Marcus-Hush-Chidsey kinetics in trilayer graphene. We identify a counterintuitive rate enhancement region spanning the magic angle curve and incommensurate twists in the system geometry. We find a broad activity peak with a ruthenium hexamine redox couple in regions corresponding to both magic angles and incommensurate angles, a result qualitatively distinct from the twisted bilayer case. Flat bands and incommensurability offer new avenues for reaction rate enhancements in electrochemical transformations.

Although density functional theory (DFT) has aided in accelerating the discovery of new materials, such calculations are computationally expensive, especially for high-throughput efforts. This has prompted an explosion in exploration of machine learning (ML) assisted techniques to improve the computational efficiency of DFT. In this study, we present a comprehensive investigation of the broader application of Finetuna, an active learning framework to accelerate structural relaxation in DFT with prior information from Open Catalyst Project pretrained graph neural networks. We explore the challenges associated with out-of-domain systems: alcohol (C->2) on metal surfaces as larger adsorbates, metal oxides with spin polarization, and three-dimensional (3D) structures like zeolites and metal organic frameworks. By pre-training ML models on large datasets and fine-tuning the model along the simulation, we demonstrate the framework's ability to conduct relaxations with fewer DFT calculations. Depending on the similarity of the test systems to the training systems, a more conservative querying strategy is applied. Our best-performing Finetuna strategy reduces the number of DFT single-point calculations by 80% for alcohols and 3D structures, and 42% for oxide systems.

In an age of expensive experiments and hype around new data-driven methods, researchers understandably want to ensure they are gleaning as much insight from their data as possible. Rachel C. Kurchin argues that there is still plenty to be learned from older approaches without turning to black boxes.

With density functional theory (DFT), it is possible to calculate the formation energy of charged point defects and in turn to predict a range of experimentally relevant quantities, such as defect concentrations, charge transition levels, or recombination rates. While prior efforts have led to marked improvements in the accuracy of such calculations, comparatively modest effort has been directed at quantifying their uncertainties. However, in the broader DFT research space, the development of Bayesian Error Estimation Functionals (BEEF) has enabled uncertainty quantification (UQ) for other properties. In this paper, we investigate the utility of BEEF as a tool for UQ of defect formation energies. We build a pipeline for propagating BEEF energies through a formation-energy calculation and test it on intrinsic defects in several materials systems spanning a variety of chemistries, bandgaps, and crystal structures, comparing to prior published results where available. We also assess the impact of aligning to a deep-level transition rather than to the VBM (valence band maximum). We observe negligible dependence of the estimated uncertainty upon a supercell size, though the relationship may be obfuscated by the fact that finite-size corrections cannot be computed separately for each member of the BEEF ensemble. Additionally, we find an increase in estimated uncertainty with respect to the absolute charge of a defect and the relaxation around the defect site without deep-level alignment, but this trend is absent when the alignment is applied. While further investigation is warranted, our results suggest that BEEF could be a useful method for UQ in defect calculations.

Phase transitions in metastable α-, κ(ε)-, and γ-Ga2O3 films to thermodynamically stable β-Ga2O3 during annealing in air, N2, and vacuum have been systematically investigated via in situ high-temperature x-ray diffraction (HT-XRD) and scanning electron microscopy (SEM). These respective polymorphs exhibited thermal stability to ∼471–525 °C, ∼773–825 °C, and ∼490–575 °C before transforming into β-Ga2O3, across all tested ambient conditions. Particular crystallographic orientation relationships were observed before and after the phase transitions, i.e., (0001) α-Ga2O3 → (2¯01) β-Ga2O3, (001) κ(ε)-Ga2O3 → (310) and (2¯01) β-Ga2O3, and (100) γ-Ga2O3 → (100) β-Ga2O3. The phase transition of α-Ga2O3 to β-Ga2O3 resulted in catastrophic damage to the film and upheaval of the surface. The respective primary and possibly secondary causes of this damage are the +8.6% volume expansion and the dual displacive and reconstructive transformations that occur during this transition. The κ(ε)- and γ-Ga2O3 films converted to β-Ga2O3 via singular reconstructive transformations with small changes in volume and unchanged surface microstructures.

Chemellia is an open-source framework for atomistic machine learning in the Julia programming language. The framework takes advantage of Julia's high speed as well as the ability to share and reuse code and interfaces through the paradigm of multiple dispatch. Chemellia is designed to make use of existing interfaces and avoid ``reinventing the wheel'' wherever possible. A key aspect of the Chemellia ecosystem is the ChemistryFeaturization interface for defining and encoding features -- it is designed to maximize interoperability between featurization schemes and elements thereof, to maintain provenance of encoded features, and to ensure easy decodability and reconfigurability to enable feature engineering experiments. This embodies the overall design principles of the Chemellia ecosystem: separation of concerns, interoperability, and transparency. We illustrate these principles by discussing the implementation of crystal graph convolutional neural networks for material property prediction.

Accurate models of electrochemical kinetics at electrode-electrolyte interfaces are crucial to understanding the high-rate behavior of energy storage devices. Phase transformation of electrodes is typically treated under equilibrium thermodynamic conditions, while realistic operation is at finite rates. Analyzing phase transformations under nonequilibrium conditions requires integrating nonlinear electrochemical kinetic models with thermodynamic models. This had only previously been demonstrated for Butler-Volmer kinetics, where it can be done analytically. In this work, we develop a software package capable of the efficient numerical inversion of rate relationships for general kinetic models. We demonstrate building nonequilibrium phase maps, including for models such as Marcus-Hush-Chidsey that require computation of an integral, and also discuss the impact of a variety of assumptions and model parameters, particularly on high-rate phase behavior. Even for a fixed set of parameters, the magnitude of the critical current can vary by more than a factor of 2 among kinetic models.

Electrochemical reactions at electrode-electrolyte interfaces are often controlled by modifying the substrate and thereby tuning the adsorption energy to reach peaks of activity volcanoes. In this work, we attempt to enhance interfacial charge transfer kinetics through modifying the electronic density of states of an electrode which offers highly tunable flat bands such as the twisted graphene system, allowing greater overlap with the redox couple states. Correspondingly, we develop a twist-dependent electrochemical activity map, combining a tight-binding electronic structure model with modified Marcus-Hush-Chidsey kinetics in trilayer graphene. We identify a counterintuitive rate enhancement region spanning the magic angle curve and incommensurate twists of the system geometry. At room temperature, we find a broad activity peak with a ruthenium hexamine redox couple in regions corresponding to both magic angles and incommensurate angles, a result qualitatively distinct from the twisted bilayer case. Flat bands and incommensurability offer new avenues for reaction rate enhancements in electrochemical transformations.

Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, mesoscale, and continuum simulations. We present an automated workflow, AutoMat, which accelerates these computational steps by introducing both automated input generation and management of simulations across scales from first principles to continuum device modeling. Furthermore, we show how to seamlessly integrate multi-fidelity predictions, such as machine learning surrogates or automated robotic experiments “in-the-loop.” The automated framework is implemented with design space search techniques to dramatically accelerate the overall materials discovery pipeline by implicitly learning design features that optimize device performance across several metrics. We discuss the benefits of AutoMat using examples in electrocatalysis and energy storage and highlight lessons learned.

Recent advances in generative models have made exploring design spaces easier for de novo molecule generation. However, popular generative models like GANs and normalizing flows face challenges such as training instabilities due to adversarial training and architectural constraints, respectively. Score-based generative models sidestep these challenges by modelling the gradient of the log probability density using a score function approximation, as opposed to modelling the density function directly, and sampling from it using annealed Langevin Dynamics. We believe that score-based generative models could open up new opportunities in molecule generation due to their architectural flexibility, such as replacing the score function with an SE(3) equivariant model. In this work, we lay the foundations by testing the efficacy of score-based models for molecule generation. We train a Transformer-based score function on Self-Referencing Embedded Strings (SELFIES) representations of 1.5 million samples from the ZINC dataset and use the Moses benchmarking framework to evaluate the generated samples on a suite of metrics.

ADVERTISEMENT RETURN TO ISSUEPREVEnergy FocusNEXTA Minimal Information Set To Enable Verifiable Theoretical Battery ResearchAashutosh MistryAashutosh MistryChemical Sciences and Engineering Division, Argonne National Laboratory, Lemont, Illinois 60439, United StatesMore by Aashutosh Mistryhttps://orcid.org/0000-0002-4359-4975, Ankit VermaAnkit VermaEnergy Conversion and Storage Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United StatesMore by Ankit Vermahttps://orcid.org/0000-0002-7610-8574, Shashank SripadShashank SripadDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesMore by Shashank Sripadhttps://orcid.org/0000-0003-1785-2042, Rebecca CiezRebecca CiezSchool of Mechanical Engineering, Division of Environmental & Ecological Engineering, Purdue University, West Lafayette, Indiana 47907, United StatesMore by Rebecca Ciezhttps://orcid.org/0000-0001-5528-2680, Valentin SulzerValentin SulzerDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesMore by Valentin Sulzer, Ferran Brosa PlanellaFerran Brosa PlanellaUniversity of Warwick, Coventry CV4 7AL, United KingdomMore by Ferran Brosa Planellahttps://orcid.org/0000-0001-6363-2812, Robert TimmsRobert TimmsMathematical Institute, University of Oxford, Oxford OX2 6GG United KingdomMore by Robert Timms, Yumin ZhangYumin ZhangDepartment of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesMore by Yumin Zhanghttps://orcid.org/0000-0001-8282-4107, Rachel KurchinRachel KurchinDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesMore by Rachel Kurchinhttps://orcid.org/0000-0002-2147-4809, Philipp DechentPhilipp DechentInstitute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jaegerstrasse 17-19, 52066, Aachen, GermanyMore by Philipp Dechenthttps://orcid.org/0000-0003-3041-1436, Weihan LiWeihan LiInstitute for Power Electronics and Electrical Drives (ISEA), RWTH Aachen University, Jaegerstrasse 17-19, 52066, Aachen, GermanyMore by Weihan Li, Samuel GreenbankSamuel GreenbankBattery Intelligence Lab, Department of Engineering Sciences, University of Oxford, Oxford, OX1 3PJ, United KingdomMore by Samuel Greenbankhttps://orcid.org/0000-0002-2091-717X, Zeeshan AhmadZeeshan AhmadPritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, United StatesMore by Zeeshan Ahmadhttps://orcid.org/0000-0001-9758-8952, Dilip KrishnamurthyDilip KrishnamurthyDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United StatesMore by Dilip Krishnamurthyhttps://orcid.org/0000-0001-8231-5492, Alexis M. Fenton Jr.Alexis M. Fenton, Jr.Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United StatesMore by Alexis M. Fenton, Jr.https://orcid.org/0000-0003-2195-9408, Kevin TennyKevin TennyDepartment of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United StatesMore by Kevin Tennyhttps://orcid.org/0000-0002-3104-3746, Prehit PatelPrehit PatelDepartment of Mechanical and Aerospace Engineering, University of Alabama in Huntsville, Huntsville, Alabama35899, United StatesMore by Prehit Patel, Daniel Juarez RoblesDaniel Juarez RoblesElectrochemical Safety Research Institute, Underwriters Laboratories Inc., Northbrook, Illinois 60062, United StatesMore by Daniel Juarez Robleshttps://orcid.org/0000-0003-2746-5775, Paul GasperPaul GasperEnergy Conversion and Storage Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United StatesMore by Paul Gasperhttps://orcid.org/0000-0001-8834-9458, Andrew ColclasureAndrew ColclasureEnergy Conversion and Storage Systems Center, National Renewable Energy Laboratory, Golden, Colorado 80401, United StatesMore by Andrew Colclasurehttps://orcid.org/0000-0002-9574-5106, Artem BaskinArtem BaskinNASA Ames Research Center, Moffett Field, California 94035, United StatesMore by Artem Baskinhttps://orcid.org/0000-0002-3156-6256, Corinne D. ScownCorinne D. ScownEnergy Analysis and Environmental Impacts Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United StatesMore by Corinne D. Scownhttps://orcid.org/0000-0003-2078-1126, Venkat R. SubramanianVenkat R. SubramanianWalker Department of Mechanical Engineering & Material Science Engineering, The University of Texas at Austin, Austin, Texas 78712, United StatesMore by Venkat R. Subramanian, Edwin KhooEdwin KhooInstitute for Infocomm Research, Agency for Science, Technology, and Research (A*STAR), 1 Fusionopolis Way, Connexis, Singapore 138632, SingaporeMore by Edwin Khoohttps://orcid.org/0000-0002-3171-7982, Srikanth AlluSrikanth AlluComputational Sciences & Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United StatesMore by Srikanth Allu, David HoweyDavid HoweyBattery Intelligence Lab, Department of Engineering Sciences, University of Oxford, Oxford, OX1 3PJ, United KingdomMore by David Howeyhttps://orcid.org/0000-0002-0620-3955, Steven DeCaluweSteven DeCaluweDepartment of Mechanical Engineering, Colorado School of Mines, Golden, Colorado 80401, United StatesMore by Steven DeCaluwehttps://orcid.org/0000-0002-3356-8247, Scott A. RobertsScott A. RobertsEngineering Sciences Center, Sandia National Laboratories, Albuquerque, New Mexico 87185, United StatesMore by Scott A. Robertshttps://orcid.org/0000-0002-4196-6771, and Venkatasubramanian Viswanathan*Venkatasubramanian ViswanathanDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States*E-mail: [email protected]More by Venkatasubramanian Viswanathanhttps://orcid.org/0000-0003-1060-5495Cite this: ACS Energy Lett. 2021, 6, 11, 3831–3835Publication Date (Web):October 11, 2021Publication History Received14 August 2021Accepted1 October 2021Published online11 October 2021Published inissue 12 November 2021https://pubs.acs.org/doi/10.1021/acsenergylett.1c01710https://doi.org/10.1021/acsenergylett.1c01710newsACS PublicationsCopyright © Published 2021 by American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. 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Large-scale electrification is vital to addressing the climate crisis, but several scientific and technological challenges remain to fully electrify both the chemical industry and transportation. In both of these areas, new electrochemical materials will be critical, but their development currently relies heavily on human-time-intensive experimental trial and error and computationally expensive first-principles, meso-scale and continuum simulations. We present an automated workflow, AutoMat, that accelerates these computational steps by introducing both automated input generation and management of simulations across scales from first principles to continuum device modeling. Furthermore, we show how to seamlessly integrate multi-fidelity predictions such as machine learning surrogates or automated robotic experiments "in-the-loop". The automated framework is implemented with design space search techniques to dramatically accelerate the overall materials discovery pipeline by implicitly learning design features that optimize device performance across several metrics. We discuss the benefits of AutoMat using examples in electrocatalysis and energy storage and highlight lessons learned.

Defect-assisted recombination processes are critical to understand, as they frequently limit the photovoltaic (PV) device performance. However, the physical parameters governing these processes can be extremely challenging to measure, requiring specialized techniques and sample preparation. And yet the fact that they limit performance as measured by current-voltage (JV) characterization indicates that they must have some detectable signal in that measurement. In this work, we use numerical device models that explicitly account for these parameters alongside high-throughput JV measurements and Bayesian inference to construct probability distributions over recombination parameters, showing the ability to recover values consistent with previously reported literature measurements. The Bayesian approach enables easy incorporation of data and models from other sources; we demonstrate this with temperature dependence of carrier capture cross-sections. The ability to extract these fundamental physical parameters from standardized, automated measurements on completed devices is promising for both established industrial PV technologies and newer research-stage ones.

Electrochemical kinetics at electrode-electrolyte interfaces limit the performance of devices including fuel cells and batteries. While the importance of moving beyond Butler-Volmer kinetics and incorporating the effect of electronic density of states of the electrode has been recognized, a unified framework that incorporates these aspects directly into electrochemical performance models is still lacking. In this work, we explicitly account for the density functional theory-calculated density of states numerically in calculating electrochemical reaction rates for a variety of electrode-electrolyte interfaces. We first show the utility of this for two cases related to Li metal electrodeposition and stripping on a Li surface and a Cu surface (anode-free configuration). The deviation in reaction rates is minor for cases with flat densities of states such as Li, but is significant for Cu due to nondispersive d-bands creating large variation. Finally, we consider a semiconducting case of a solid-electrolyte interphase consisting of LiF and Li2CO3 and note the importance of the Fermi level at the interface pinned by the redox reaction occurring there. We identify the asymmetry in reaction rates as a function of discharge/charge naturally within this approach.