There is significant interest in improving the performance of batteries to increase electrification of transportation and aviation. Recently, performance improvements have been in large part due to changes in the composition of the cathode material family, LiNixMnyCo(1−x−y)O2 (e.g., 111–622–811). Despite the importance of these materials and tremendous progress with density functional theory (DFT) calculations in understanding basic design principles, it is computationally prohibitively expensive to make this problem tractable. Specifically, predicting the open circuit voltage for any cathode material in this family requires evaluation of stability in a quaternary phase space. In this work, we develop machine-learning potentials using fingerprinting based on atom-centered symmetry functions, used with a neural network model, trained on DFT calculations with a prediction accuracy of 3.7 meV/atom and 0.13 eV/Å for energy and force, respectively. We perform hyperparameter optimization of the fingerprinting parameters using Bayesian optimization through the Dragonfly package. Using this ML calculator, we first test its performance in predicting thermodynamic properties within the Debye–Grüneisen model and find good agreement for most thermodynamic properties, including the Gibbs free energy and entropy. Then, we use this to calculate the Li-vacancy ordering as a function of Li composition to simulate the process of discharging/charging of the cathode using grand canonical Monte Carlo simulations. The predicted voltage profiles are in good agreement with the experimental ones and provide an approach to rapidly perform design optimization in this phase space. This study serves as a proof-point of machine-learned DFT surrogates to enable battery materials optimization.

Singlet oxygen has emerged as a real mystery puzzling battery science, having been observed in Li−O 2 and Na−O 2 batteries, in conventional Li-ion batteries with NMC cathodes, and during the oxidation of Li 2 CO 3 . The formation of singlet oxygen has been directly linked to the degradation and catastrophic fade seen in these battery chemistries. While there are several proposed hypothesis for its origin, the exact mechanism for the formation of singlet oxygen remains unclear. In this Letter, we attempt to unify these findings by proposing a mechanism of singlet oxygen production in metal−air and Li-ion batteries. We show that a potential dependence of surface termination explains the onset potentials of singlet oxygen release, and in all considered cases the mechanism of singlet oxygen generation is through the chemical disproportionation of the uncoordinated superoxide anion in solution; therefore, the singlet oxygen yield is determined by the concentration of free superoxide versus alkali superoxide ion pairs in solution.

Density functional theory (DFT) simulations, at the generalized gradient approximation (GGA) level, are being routinely used for material discovery based on high-throughput descriptor-based searches. The success of descriptor-based material design relies on eliminating bad candidates and keeping good candidates for further investigation. While DFT has been widely successfully for the former, often times good candidates are lost due to the uncertainty associated with the DFTpredicted material properties. Uncertainty associated with DFT predictions has gained prominence and has led to the development of exchange correlation functionals that have built-in error estimation capability. In this work, we demonstrate the use of built-in error estimation capabilities within the BEEF-vdW exchange correlation functional for quantifying the uncertainty associated with the magnetic ground state of solids. We demonstrate this approach by calculating the uncertainty estimate for the energy difference between the different magnetic states of solids and compare them against a range of GGA exchange correlation functionals as is done in many first principles calculations of materials. We show that this estimate reasonably bounds the range of values obtained with the different GGA functionals. The estimate is determined as a post-processing step and thus provides a computationally robust and systematic approach to estimating uncertainty associated with predictions of magnetic ground states. We define a confidence value (c-value) that incorporates all calculated magnetic states in order to quantify the concurrence of the prediction at the GGA level and argue that predictions of magnetic ground states from GGA level DFT is incomplete without an accompanying c-value. We demonstrate the utility of this method using a case study of Li and Na-ion cathode materials and the c-value metric correctly identifies that GGA level DFT will have low predictability for NaFePO4F. Further, there needs to be a systematic test of a collection of plausible magnetic states, especially in identifying anti-ferromagnetic (AFM) ground states. We believe that our approach of estimating uncertainty can be readily incorporated into all high-throughput computational material discovery efforts and this will lead to a dramatic increase in the likelihood of finding good candidate materials.PACS numbers: 1.15. Mb,

Density functional theory (DFT) calculations are routinely used to screen for functional materials for a variety of applications. This screening is often carried out with a few descriptors, which uses ground-state properties that typically ignores finite temperature effects. Finite-temperature effects can be included by calculating the vibrations properties and this can greatly improve the fidelity of computational screening. An important challenge for DFT-based screening is the sensitivity of the predictions to the choice of the exchange correlation function. In this work, we rigorously explore the sensitivity of finite temperature thermodynamic properties to the choice of the exchange correlation functional using the built-in error estimation capabilities within the Bayesian Error Estimation Functional (BEEF-vdw). The vibrational properties are estimated using the Debye model and we quantify the uncertainty associated with finite-temperature properties for a diverse collection of materials. We find good agreement with experiment and small spread in predictions over different exchange correlation functionals for Mg, Al 2 O 3 , Al, Ca, and GaAs. In the case of Li, Li 2 O, and 1 arXiv:1910.07891v1 [cond-mat.mtrl-sci] 4 Oct 2019NiO, however, we find a large spread in predictions as well as disagreement between experiment and functionals due to complex bonding environments. While the energetics generated by BEEF-vdW ensemble is typically normal, the complex mapping through the Debye model leads to the derived finite temperature properties having non-Gaussian behavior. We test a wide variety of probability distributions that best represent the finite temperature distribution and find that properties such as specific heat, Gibbs free energy, entropy, and the thermal expansion coefficient are well described by normal or transformed normal distributions, while the prediction spread of volume at a given temperature does not appear to be drawn from a single distribution. Given the computational efficiency of the approach, we believe that uncertainty quantification should be routinely incorporated into finite-temperature predictions. In order to facilitate this, we have open-sourced the code base, under the name, Depye.

Machine-learning potentials are accelerating the development of energy materials, especially in identifying phase diagrams and other thermodynamic properties. In this work, we present a neural network potential based on atom-centered symmetry function descriptors to model the energetics of lithium intercalation into graphite. The potential was trained on a dataset of over 9000 diverse lithium–graphite configurations that varied in applied stress and strain, lithium concentration, lithium–carbon and lithium–lithium bond distances, and stacking order to ensure wide sampling of the potential atomic configurations during intercalation. We calculated the energies of these structures using density functional theory (DFT) through the Bayesian error estimation functional with van der Waals correlation exchange-correlation functional, which can accurately describe the van der Waals interactions that are crucial to determining the thermodynamics of this phase space. Bayesian optimization, as implemented in Dragonfly, was used to select optimal set of symmetry function parameters, ultimately resulting in a potential with a prediction error of 8.24 meV atom−1 on unseen test data. The potential can predict energies, structural properties, and elastic constants at an accuracy comparable to other DFT exchange-correlation functionals at a fraction of the computational cost. The accuracy of the potential is also comparable to similar machine-learned potentials describing other systems. We calculate the open circuit voltage with the calculator and find good agreement with experiment, especially in the regime x ≥ 0.3, for x in Li x C6. This study further illustrates the power of machine learning potentials, which promises to revolutionize design and optimization of battery materials.

Layered Li(Ni,Mn,Co,)O2 (NMC) presents an intriguing ternary alloy design space for the optimization of performance as a cathode material in Li-ion batteries. In the case of NMC, however, only a select few proportions of transition metal cations have been attempted and even fewer show promise. Recently, due to cost and resource limitations of Co, high Ni-containing NMC alloys have gained enormous attention. Here, we present a high fidelity computational search of the ternary phase diagram with an emphasis on high-Ni containing compositional phases. This is done through the use of density functional theory training data fed into a reduced order model Hamiltonian that accounts for effective electronic and spin interactions of neighboring transition metal atoms at various lengths in a background of fixed lithium and oxygen atoms. This model can then be solved to include finite temperature thermodynamics into a convex hull analysis. We also provide a method to propagate the uncertainty at every level of the analysis to the final prediction of thermodynamically favorable compositional phases thus providing a quantitative measure of confidence for each prediction made. Due to the complexity of the three component system, as well as the intrinsic error of density functional theory, we argue that this propagation of uncertainty, particularly the uncertainty due to exchange-correlation functional choice is necessary to have reliable and interpretable results. With our final result, we recover the prediction of already known phases such as LiNi0.33Mn0.33Co0.33O2 (111) and LiNi0.8Mn0.1Co0.1O2 (811) in exact proportion while finding other proportions very close to the experimentally claimed LiNi0.6Mn0.2Co0.2O2 (622) and LiNi0.5Mn0.3Co0.2O2 (532) phases, and overall predict a total of 37 phases with reasonable confidence and 69 more phases with a lower level of confidence. Through our analysis, we also can identify the phases with the highest average operational voltage at a given Co composition. Our method presents a framework that can be extended to searches for other high Ni cathode materials by substituting other transition metal atoms into the lattice such as aluminum and magnesium, which have already shown promise. arXiv:1805.08171v1 [cond-mat.mtrl-sci]

We examine the possibility of using graphene nanoribbons (GNRs) with directly substituted chromium atoms as spintronic device. Using density functional theory, we simulate a voltage bias across a constructed GNR in a device setup, where a magnetic dimer has been substituted into the lattice. Through this first principles approach, we calculate the electronic and magnetic properties as a function of Hubbard U, voltage, and magnetic configuration. By calculating of the total energy of each magnetic configuration, we determine that initial antiferromagnetic ground state flips to a ferromagnetic state with applied bias. Mapping this transition point to the calculated conductance for the system reveals that there is a distinct change in conductance through the GNR, which indicates the possibility of a spin valve. We also show that this corresponds to a distinct change in the induced magnetization within the graphene.

Through the use of Heisenberg spin-spin interactions, we provide analytical representations for inelastic neutron scattering eigenstates and excitation cross-sections of the general S1-S2 spin dimeric systems. Using an exact diagonalization approach to the spin Hamiltonian, we analyze various spin coefficients to provide general representations for the neutron scattering cross-sections of two interacting spins. We also detail a generalized method for the determination of Sz polarized excitations, which provide an approximation for the excitations within an applied magnetic field. These calculations provide a general understanding of the interactions between two individual or compound spin systems, which can help provide insight into condensed matter systems like molecular magnets, quantum dots, and spintronic systems, as well as particle physics investigations into quark matter and meson interactions. * Corresponding Author: Dr. Jason T. Haraldsen (haraldjt@jmu.edu) PACS numbers: 75.30.Et,75.50.Ee,75.50.Xx,78.70.Nx The study of quantum nanomagnets has been expanding rapidly due to the possible technological applications for systems like molecular magnets and quantum dots due to the presence of quantum tunneling phenomena and anisotropic effects.1-10 The complete understanding of quantum excitations and the ability to detect and observe them are two critical components for the development of applications in spintronics and spin switches for quantum computing 10,11 .Molecular magnets are clusters of magnetic ions that are typically isolated from long-range magnetic interactions by non-magnetic ligands [12][13][14][15][16][17][18][19] , and they typically have many magnetic ions like Mn 12 and V 15 15,16 . Recently, it has been shown that many excitations within large magnetic clusters are governed by individual subgeometry (smaller two-and three-body components) excitations 20 . Therefore, examining the smallest components of magnetic interactions is critical for moving forward in gaining information for the larger and more complex systems.From an experimental point of view, there are many techniques that can be employed to characterize and measure the properties of antiferromagnetic spin systems. These include magnetic susceptibility, inelastic neutron scattering (INS), optical/Raman spectroscopy, and electron spin resonance. 19,21,22 While many of these techniques are important for the study of the bulk properties for magnetic systems, INS provides the unique ability to investigate individual excitations and examine local interactions and structural data.Typically, discussions of magnetic clusters are limited to specific material systems [23][24][25][26][27][28][29] , which doesn't always provide a complete picture of the interactions being studied. Spin 1/2 clusters have been studied in great detail by a number of theoretical and experimental groups [30][31][32][33][34][35] . With regards to the spin dimer, Whangbo et al. presents a detailed analysis of general excitations 31 ; however, this work doesn't exa...