Strategies for fitting accurate machine-learned inter-atomic potentials for solid electrolytes

Ion transport in materials is routinely probed through several experimental techniques,which introduce variability in reported ionic diffusivities and conductivities.The computational prediction of ionic diffusivities and conductivities helps in identifying good ionic conductors,and suitable solid electrolytes(SEs),thus establishing firm structure-property relationships.Machine-learned potentials are an attractive strategy to extend the capabilities of accurate ab initio molecular dynamics(AIMD)to longer simulations for larger systems,enabling the study of ion transport at lower temperatures.However,machine-learned potentials being in their infancy,critical assessments of their predicting capabilities are rare.Here,we identified the main factors controlling the quality of a machine-learning potential based on the moment tensor potential formulation,when applied to the properties of ion transport in ionic conductors,such as SEs.Our results underline the importance of high-quality and diverse training sets required to fit moment tensor potentials.We highlight the importance of considering intrinsic defects which may occur in SEs.We demonstrate the limitations posed by short-timescale and high-temperature AIMD simulations to predict the room-temperature properties of materials.

Designing interfaces in energy materials applications with firstprinciples calculations

Materials for energy-related applications,which are crucial for a sustainable energy economy,rely on combining materials that form complex heterogenous interfaces.Simultaneously,progress in computational materials science in describing complex interfaces is critical for improving the understanding and performance of energy materials.Hence,we present an in-depth review of the physical quantities regulating interfaces in batteries,photovoltaics,and photocatalysts,that are accessible from modern electronic structure methods,with a focus on density functional theory calculations.For each energy application,we highlight unique approaches that have been developed to calculate interfacial properties and explore the possibility of applying some of these approaches across disciplines,leading to a unified overview of interface design.Finally,we identify a set of challenges for further improving the theoretical description of interfaces in energy devices.

Effect of exchange-correlation functionals on the estimation of migration barriers in battery materials

Facile ionic mobility within host frameworks is crucial to the design of high-energy-density batteries with high-power-densities,where the migration barrier(Em)is the governing factor.Here,we assess the accuracy and computational performance of generalized gradient approximation(GGA),the strongly constrained and appropriately normed(SCAN),and their Hubbard U corrections,GGA+U and SCAN+U,within the density functional theory-nudged elastic band framework,in the prediction of Em as benchmarked against experimental data.Importantly,we observe SCAN to be more accurate than other frameworks,on average,albeit with higher computational costs and convergence difficulties,while GGA is a feasible choice for“quick”and“qualitative”Em predictions.Further,we quantify the sensitivity of Em with adding uniform background charge and/or the climbing image approximation in solid electrolytes,and the Hubbard U correction in electrodes.Our findings will improve the quality of Em predictions which will enable identifying better materials for energy storage applications.

Modeling the effects of salt concentration on aqueous and organic electrolytes

Understanding the thermodynamic properties of electrolyte solutions is of vital importance for a myriad of physiological and technological applications.The mean activity coefficientγ±is associated with the deviation of an electrolyte solution from its ideal behavior and may be obtained by combining the Debye-Hückel(DH)and Born(B)equations.However,the DH and B equations depend on the concentration and temperature-dependent static permittivity of the solutionεr(c,T)and the size of the solvated ions ri,whose experimental data is often not available.Here,we use a combination of molecular dynamics and density functional theory to predictεr(c,T)and ri,which enables us to apply the DH and B equations to any technologically relevant aqueous and nonaqueous electrolyte at any concentration and temperature of interest.