Particle Size Optimization of Thermochemical Salt Hydrates for High Energy Density Thermal Storage

Thermal energy storage(TES)solutions offer opportunities to reduce energy consumption,greenhouse gas emissions,and cost.Specifically,they can help reduce the peak load and address the intermittency of renewable energy sources by time shifting the load,which are critical toward zero energy buildings.Thermochemical materials(TCMs)as a class of TES undergo a solid-gas reversible chemical reaction with water vapor to store and release energy with high storage capacities(600 kWh m^(-3))and negligible self-discharge that makes them uniquely suited as compact,stand-alone units for daily or seasonal storage.However,TCMs suffer from instabilities at the material(salt particles)and reactor level(packed beds of salt),resulting in poor multi-cycle efficiency and high-levelized cost of storage.In this study,a model is developed to predict the pulverization limit or Rcrit of various salt hydrates during thermal cycling.This is critical as it provides design rules to make mechanically stable TCM composites as well as enables the use of more energy-efficient manufacturing process(solid-state mixing)to make the composites.The model is experimentally validated on multiple TCM salt hydrates with different water content,and effect of Rcrit on hydration and dehydration kinetics is also investigated.

Light-Material Interactions Using Laser and Flash Sources for Energy Conversion and Storage Applications

This review provides a comprehensive overview of the progress in light-material interactions(LMIs),focusing on lasers and flash lights for energy conversion and storage applications.We discuss intricate LMI parameters such as light sources,interaction time,and fluence to elucidate their importance in material processing.In addition,this study covers various light-induced photothermal and photochemical processes ranging from melting,crystallization,and ablation to doping and synthesis,which are essential for developing energy materials and devices.Finally,we present extensive energy conversion and storage applications demonstrated by LMI technologies,including energy harvesters,sensors,capacitors,and batteries.Despite the several challenges associated with LMIs,such as complex mechanisms,and high-degrees of freedom,we believe that substantial contributions and potential for the commercialization of future energy systems can be achieved by advancing optical technologies through comprehensive academic research and multidisciplinary collaborations.

Coupling of Adhesion and Anti‑Freezing Properties in Hydrogel Electrolytes for Low‑Temperature Aqueous‑Based Hybrid Capacitors

Solid-state zinc-ion capacitors are emerging as promising candidates for large-scale energy storage owing to improved safety,mechanical and thermal stability and easy-to-direct stacking.Hydrogel electrolytes are appealing solid-state electrolytes because of eco-friendliness,high conductivity and intrinsic flexibility.However,the electrolyte/electrode interfacial contact and anti-freezing properties of current hydrogel electrolytes are still challenging for practical applications of zinc-ion capacitors.Here,we report a class of hydrogel electrolytes that couple high interfacial adhesion and anti-freezing performance.The synergy of tough hydrogel matrix and chemical anchorage enables a well-adhered interface between hydrogel electrolyte and electrode.Meanwhile,the cooperative solvation of ZnCl2 and LiCl hybrid salts renders the hydrogel electrolyte high ionic conductivity and mechanical elasticity simultaneously at low temperatures.More significantly,the Zn||carbon nanotubes hybrid capacitor based on this hydrogel electrolyte exhibits low-temperature capacitive performance,delivering high-energy density of 39 Wh kg^(-1)at-60°C with capacity retention of 98.7%over 10,000 cycles.With the benefits of the well-adhered electrolyte/electrode interface and the anti-freezing hydrogel electrolyte,the Zn/Li hybrid capacitor is able to accommodate dynamic deformations and function well under 1000 tension cycles even at-60°C.This work provides a powerful strategy for enabling stable operation of low-temperature zinc-ion capacitors.

Solvation Effects on the Dielectric Constant of 1 M LiPF6 in Ethylene Carbonate:Ethyl Methyl Carbonate 3:7

We report the dielectric constant of 1 M LiPF_(6)in EC:EMC 3∶7 w/w(ethylene carbonate/ethyl methyl carbonate)in addition to neat EC:EMC 3∶7 w/w.Using three Debye relaxations,the static permittivity value,or dielectric constant,is extrapolated to 18.5,which is compared to 18.7 for the neat solvent mixture.The EC solvent is found to strongly coordinate with the Li^(+)cations of the salt,which results in a loss of dielectric contribution to the electrolyte.However,the small amplitude and large uncertainty in relaxation frequency for EMC cloud definitive identification of the Li^(+)solvation shell.Importantly,the loss of the free EC permittivity contribution due to Li^(+)solvation is almost completely balanced by the positive contribution of the associated LiPF_(6)salt,demonstrating that a significant quantity of dipolar ion pairs exists in 1 M LiPF_(6)in EC:EMC 3∶7.

Precisely quantifying bulk transition metal valence evolution in conventional battery electrode by inverse partial fluorescence yield

Precisely quantifying transition metal(TM) redox in bulk is a key to understand the fundamental of optimizing cathode materials in secondary batteries. At present, the commonly used methods to probe TM redox are hard X-ray absorption spectroscopy(hXAS) and soft X-ray absorption spectroscopy(sXAS).However, they are both facing challenges to precisely quantify the valence states of some transition metals such as Mn. In this paper, Mn-L iPFY(inverse partial fluorescence yield) spectra extracted from Mn-L m RIXS(mapping of resonant inelastic X-ray scattering) is adopted to quantify Mn valence states. Mn-L i PFY spectra has been considered as a bulk-sensitive, non-distorted probe of TM valence states.However, the exact precision of this method is still unclear in quantifying practical battery electrodes.Herein, a series of LiMn_(2)O_(4) electrodes with different charge and discharge states are prepared. Based on their electrochemical capacity(generally considered to be very precise), the precision of Mn iPFY in quantifying bulk Mn valence state is confirmed, and the error range is unraveled. Mn-L mRIXS iPFY thus is identified as one of the best methods to quantify the bulk Mn valence state comparing with hXAS and sXAS.

A critical examination of compound stability predictions from machine-learned formation energies

Machine learning has emerged as a novel tool for the efficient prediction of material properties,and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory(DFT).The models tested in this work include five recently published compositional models,a baseline model using stoichiometry alone,and a structural model.By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions,we show that while formation energies can indeed be predicted well,all compositional models perform poorly on predicting the stability of compounds,making them considerably less useful than DFT for the discovery and design of new solids.Most critically,in sparse chemical spaces where few stoichiometries have stable compounds,only the structural model is capable of efficiently detecting which materials are stable.The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery,with the constraint that for any new composition,the ground-state structure is not known a priori.This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability,emphasizing the importance of assessing model performance on stability predictions,for which we provide a set of publicly available tests.

Benchmarking materials property prediction methods:the Matbench test set and Automatminer reference algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning(ML)models for predicting properties of inorganic bulk materials.The test suite,Matbench,is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources.

High-throughput prediction of the ground-state collinear magnetic order of inorganic materials using Density Functional Theory

We present a robust,automatic high-throughput workflow for the calculation of magnetic ground state of solid-state inorganic crystals,whether ferromagnetic,antiferromagnetic or ferrimagnetic,and their associated magnetic moments within the framework of collinear spin-polarized Density Functional Theory.This is done through a computationally efficient scheme whereby plausible magnetic orderings are first enumerated and prioritized based on symmetry,and then relaxed and their energies determined through conventional DFT+U calculations.This automated workflow is formalized using the atomate code for reliable,systematic use at a scale appropriate for thousands of materials and is fully customizable.The performance of the workflow is evaluated against a benchmark of 64 experimentally known mostly ionic magnetic materials of non-trivial magnetic order and by the calculation of over 500 distinct magnetic orderings.A non-ferromagnetic ground state is correctly predicted in 95% of the benchmark materials,with the experimentally determined ground state ordering found exactly in over 60% of cases.Knowledge of the ground state magnetic order at scale opens up the possibility of high-throughput screening studies based on magnetic properties,thereby accelerating discovery and understanding of new functional materials.

Predicting defect behavior in B2 intermetallics by merging ab initio modeling and machine learning

We present a combination of machine learning and high throughput calculations to predict the points defects behavior in binary intermetallic(A–B)compounds,using as an example systems with the cubic B2 crystal structure(with equiatomic AB stoichiometry).To the best of our knowledge,this work is the first application of machine learning-models for point defect properties.High throughput first principles density functional calculations have been employed to compute intrinsic point defect energies in 100 B2 intermetallic compounds.The systems are classified into two groups:(i)those for which the intrinsic defects are antisites for both A and B rich compositions,and(ii)those for which vacancies are the dominant defect for either or both composition ranges.The data was analyzed by machine learning-techniques using decision tree,and full and reduced multiple additive regression tree(MART)models.Among these three schemes,a reduced MART(r-MART)model using six descriptors(formation energy,minimum and difference of electron densities at the Wigner–Seitz cell boundary,atomic radius difference,maximal atomic number and maximal electronegativity)presents the highest fit(98%)and predictive(75%)accuracy.This model is used to predict the defect behavior of other B2 compounds,and it is found that 45%of the compounds considered feature vacancies as dominant defects for either A or B rich compositions(or both).The ability to predict dominant defect types is important for the modeling of thermodynamic and kinetic properties of intermetallic compounds,and the present results illustrate how this information can be derived using modern tools combining high throughput calculations and data analytics.

A charge-density-based general cation insertion algorithm for generating new Li-ion cathode materials

Future lithium(Li)energy storage technologies,in particular solid-state configurations with a Li metal anode,opens up the possibility of using cathode materials that do not necessarily contain Li in its as-made state.To accelerate the discovery and design of such materials,we develop a general,chemically,and structurally agnostic methodology for identifying the optimal Li sites in any crystalline material.For a given crystal structure,we attempt multiple Li insertions at symmetrically in-equivalent positions by analyzing the electronic charge density obtained from first-principles density functional theory.In this report,we demonstrate the effectiveness of this procedure in successfully identifying the positions of the Li ion in well-known cathode materials using only the empty host(charged)material as guidance.Furthermore,applying the algorithm to over 2000 candidate cathode empty host materials we obtain statistics of Li site preferences to guide future developments of novel Li-ion cathode materials,particularly for solid-state applications.

Recent advances and applications of deep learning methods in materials science

Deep learning(DL)is one of the fastest-growing topics in materials data science,with rapidly emerging applications spanning atomistic,image-based,spectral,and textual data modalities.DL allows analysis of unstructured data and automated identification of features.The recent development of large materials databases has fueled the application of DL methods in atomistic prediction in particular.In contrast,advances in image and spectral data have largely leveraged synthetic data enabled by high-quality forward models as well as by generative unsupervised DL methods.In this article,we present a high-level overview of deep learning methods followed by a detailed discussion of recent developments of deep learning in atomistic simulation,materials imaging,spectral analysis,and natural language processing.For each modality we discuss applications involving both theoretical and experimental data,typical modeling approaches with their strengths and limitations,and relevant publicly available software and datasets.We conclude the review with a discussion of recent cross-cutting work related to uncertainty quantification in this field and a brief perspective on limitations,challenges,and potential growth areas for DL methods in materials science.

Machine Learning Chemical Guidelines for Engineering Electronic Structures in Half-Heusler Thermoelectric Materials

Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors,which is known to be correlated to valley degeneracy in the electronic band structure.However,there are over 50 known semiconducting half-Heusler phases,and it is not clear how the chemical composition affects the electronic structure.While all the n-type electronic structures have their conduction band minimum at either theΓ-or X-point,there is more diversity in the p-type electronic structures,and the valence band maximum can be at either theΓ-,L-,or W-point.Here,we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W-point to the valence band maximum.We do this by constructing an“orbital phase diagram”to cluster the variety of electronic structures expressed by these phases into groups,based on the atomic orbitals that contribute most to their valence bands.Then,with the aid of machine learning,we develop new chemical rules that predict the location of the valence band maximum in each of the phases.These rules can be used to engineer band structures with band convergence and high valley degeneracy.

Optimal band structure for thermoelectrics with realistic scattering and bands

Understanding how to optimize electronic band structures for thermoelectrics is a topic of long-standing interest in the community.Prior models have been limited to simplified bands and/or scattering models.In this study,we apply more rigorous scattering treatments to more realistic model band structures—upward-parabolic bands that inflect to an inverted-parabolic behavior—including cases of multiple bands.In contrast to common descriptors(e.g.,quality factor and complexity factor),the degree to which multiple pockets improve thermoelectric performance is bounded by interband scattering and the relative shapes of the bands.We establish that extremely anisotropic“flat-and-dispersive”bands,although best-performing in theory,may not represent a promising design strategy in practice.Critically,we determine optimum bandwidth,dependent on temperature and lattice thermal conductivity,from perfect transport cutoffs that can in theory significantly boost zT beyond the values attainable through intrinsic band structures alone.Our analysis should be widely useful as the thermoelectric research community eyes zT>3.

High-throughput predictions of metal-organic framework electronic properties:theoretical challenges,graph neural networks,and data exploration

With the goal of accelerating the design and discovery of metal–organic frameworks(MOFs)for electronic,optoelectronic,and energy storage applications,we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations.Compared to more accurate hybrid functionals,we find that the widely used PBE generalized gradient approximation(GGA)functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character.However,an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations.With regards to partial atomic charges,we find that different density functional approximations predict similar charges overall,although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference.Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes.We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work,paving the way for future high-throughput screening studies.To encourage exploration and reuse of the theoretical calculations presented in this work,the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

An improved symmetry-based approach to reciprocal space path selection in band structure calculations

Band structures for electrons,phonons,and other quasiparticles are often an important aspect of describing the physical properties of periodic solids.Most commonly,energy bands are computed along a one-dimensional path of high-symmetry points and line segments in reciprocal space(the“k-path”),which are assumed to pass through important features of the dispersion landscape.However,existing methods for choosing this path rely on tabulated lists of high-symmetry points and line segments in the first Brillouin zone,determined using different symmetry criteria and unit cell conventions.Here we present a new“on-the-fly”symmetry-based approach to obtaining paths in reciprocal space that attempts to address the previous limitations of these conventions.Given a unit cell of a magnetic or nonmagnetic periodic solid,the site symmetry groups of points and line segments in the irreducible Brillouin zone are obtained from the total space group.The elements in these groups are used alongside general and maximally inclusive high-symmetry criteria to choose segments for the final k-path.A smooth path connecting each segment is obtained using graph theory.This new framework not only allows for increased flexibility and user convenience but also identifies notable overlooked features in certain electronic band structures.In addition,a more intelligent and efficient method for analyzing magnetic materials is also enabled through proper accommodation of magnetic symmetry.

A flexible and scalable scheme for mixing computed formation energies from different levels of theory

Computational materials discovery efforts are enabled by large databases of properties derived from high-throughput density functional theory(DFT),which now contain millions of calculations at the generalized gradient approximation(GGA)level of theory.It is now feasible to carry out high-throughput calculations using more accurate methods,such as meta-GGA DFT;however recomputing an entire database with a higher-fidelity method would not effectively leverage the enormous investment of computational resources embodied in existing(GGA)calculations.Instead,we propose here a general procedure by which higher-fidelity,low-coverage calculations(e.g.,meta-GGA calculations for selected chemical systems)can be combined with lower-fidelity,high-coverage calculations(e.g.,an existing database of GGA calculations)in a robust and scalable manner.We then use legacy PBE(+U)GGA calculations and new r2SCAN meta-GGA calculations from the Materials Project database to demonstrate that our scheme improves solid and aqueous phase stability predictions,and discuss practical considerations for its implementation.

High-throughput calculations of charged point defect properties with semi-local density functional theory— performance benchmarks for materials screening applications

Calculations of point defect energetics with Density Functional Theory(DFT)can provide valuable insight into several optoelectronic,thermodynamic,and kinetic properties.These calculations commonly use methods ranging from semi-local functionals with a-posteriori corrections to more computationally intensive hybrid functional approaches.For applications of DFT-based high-throughput computation for data-driven materials discovery,point defect properties are of interest,yet are currently excluded from available materials databases.This work presents a benchmark analysis of automated,semi-local point defect calculations with a-posteriori corrections,compared to 245“gold standard”hybrid calculations previously published.We consider three different a-posteriori correction sets implemented in an automated workflow,and evaluate the qualitative and quantitative differences among four different categories of defect information:thermodynamic transition levels,formation energies,Fermi levels,and dopability limits.We highlight qualitative information that can be extracted from high-throughput calculations based on semi-local DFT methods,while also demonstrating the limits of quantitative accuracy.

The ab initio non-crystalline structure database:empowering machine learning to decode diffusivity

Non-crystalline materials exhibit unique properties that make them suitable for various applications in science and technology,ranging from optical and electronic devices and solid-state batteries to protective coatings.However,data-driven exploration and design of non-crystalline materials is hampered by the absence of a comprehensive database covering a broad chemical space.In this work,we present the largest computed non-crystalline structure database to date,generated from systematic and accurate ab initio molecular dynamics(AIMD)calculations.We also show how the database can be used in simple machine-learning models to connect properties to composition and structure,here specifically targeting ionic conductivity.These models predict the Li-ion diffusivity with speed and accuracy,offering a cost-effective alternative to expensive density functional theory(DFT)calculations.Furthermore,the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures,energies,and force landscape,and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials,impacting design beyond that of non-crystalline materials.In addition,combining diffusion trajectories from our dataset withmodels that predict liquidus viscosity and melting temperature could be utilized to develop models for predicting glass-forming ability.

A high-throughput framework for lattice dynamics

We develop an automated high-throughput workflow for calculating lattice dynamical properties from first principles including those dictated by anharmonicity.The pipeline automatically computes interatomic force constants(IFCs)up to 4th order from perturbed training supercells,and uses the IFCs to calculate lattice thermal conductivity,coefficient of thermal expansion,and vibrational free energy and entropy.It performs phonon renormalization for dynamically unstable compounds to obtain real effective phonon spectra at finite temperatures and calculates the associated free energy corrections.The methods and parameters are chosen to balance computational efficiency and result accuracy,assessed through convergence testing and comparisons with experimental measurements.Deployment of this workflow at a large scale would facilitate materials discovery efforts toward functionalities including thermoelectrics,contact materials,ferroelectrics,aerospace components,as well as general phase diagram construction.