This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of atomic environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of molecules and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chemical level of theory as reference and enables high-fidelity molecular dynamics simulations over long time scales.

Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations.Within an active learning framework, the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single-and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.

Segregation and phase separation of aliovalent dopants on perovskite oxide (ABO 3 ) surfaces is detrimental to the performance of energy conversion systems such as solid oxide fuel/electrolysis cells and catalysts for thermochemical H 2 O and CO 2 splitting. One key reason behind the instability of perovskite oxide surfaces is the electrostatic attraction of the negatively We take La 0.8 Sr 0.2 CoO 3 (LSC) as a model perovskite oxide, and modify its surface with additive cations that are more and less reducible than Co on the B-site of LSC. By using ambient pressure X-ray absorption and photoelectron spectroscopy, we proved that the dominant role of the less reducible cations is to suppress the enrichment and phase separation of Sr while reducing the concentration of ! •• and making the LSC more oxidized at its surface. Consequently, we found that these less reducible cations significantly improve stability, with up to 30x acceleration of the oxygen exchange kinetics, after 54 hours in air at 550 o C achieved by Hf addition onto LSC.Finally, the results revealed a "volcano" relation between the oxygen exchange kinetics and the oxygen vacancy formation enthalpy of the binary oxides of the additive cations. This volcano relation highlights the existence of an optimum surface oxygen vacancy concentration that balances the gain in oxygen exchange kinetics and the chemical stability loss. However, significant degradation of the ORR kinetics because of dopant segregation and phase separation is also associated with surface oxygen vacancies 11 . Therefore, here we propose to decrease the surface oxygen vacancy concentration for suppressing the electrostatic driver to this detrimental process.In this paper we hypothesized that the perovskite oxide surface stability can be tuned as a function of the reducibility of the surface. We took La 0. and stability on LSC, while the addition of V and excess Co lead to stronger degradation.Ambient-pressure X-ray photoelectron spectroscopy (AP-XPS) and X-ray absorption spectroscopy (AP-XAS) 33,34 measurements up to 550 o C revealed that these less reducible cations make the LSC surface more oxidized and decrease the surface oxygen vacancy concentration, leading to a smaller electrostatic driving force for Sr segregation. Electrochemical performance of LSC with surface chemical modificationsWe compared the evolution of the surface oxygen exchange coefficients, k q , which represents the oxygen reduction reactivity of LSC cathodes as a function of time at 530 °C in air.The The morphology of the electrochemically tested cathode surfaces, shown in Fig. 1b, indicates the correlation of the electrochemical stability to the surface chemical stability. On the films with fast degradation of k q , i.e., LSC and LSC-V12, a large surface roughness and particle coverage is evident. Electrochemically stable films such as LSC-Ti15, LSC-Al15 and LSC-Hf16 have more stable surface morphology with significantly lower roughness. Our previous investigation on the nature of these segregated pa...

The effect of dislocations on the chemical, electrical and transport properties in oxide materials is important for electrochemical devices, such as fuel cells and resistive switches, but these effects have remained largely unexplored at the atomic level. In this work, by using large-scale atomistic simulations, we uncover how a ⟨100⟩{011} edge dislocation in SrTiO3, a prototypical perovskite oxide, impacts the local defect chemistry and oxide ion transport. We find that, in the dilute limit, oxygen vacancy formation energy in SrTiO3 is lower at sites close to the dislocation core, by as much as 2 eV compared to that in the bulk. We show that the formation of a space-charge zone based on the redistribution of charged oxygen vacancies can be captured quantitatively at atomistic level by mapping the vacancy formation energies around the dislocation. Oxide-ion diffusion was studied for a low vacancy concentration regime (ppm level) and a high vacancy concentration regime (up to 2.5%). In both cases, no evidence of pipe-diffusion, i.e., significantly enhanced mobility of oxide ions, was found as determined from the calculated migration barriers, contrary to the case in metals. However, in the low vacancy concentration regime, the vacancy accumulation at the dislocation core gives rise to a higher diffusion coefficient, even though the oxide-ion mobility itself is lower than that in the bulk. Our findings have important implications for applications of perovskite oxides for information and energy technologies. The observed lower oxygen vacancy formation energy at the dislocation core provides a quantitative and direct explanation for the electronic conductivity of dislocations in SrTiO3 and related oxides studied for red-ox based resistive switching. Reducibility and electronic transport at dislocations can also be quantitatively engineered into active materials for fuel cells, catalysis, and electronics.

Strained oxide thin films are of interest for accelerating oxide ion conduction in electrochemical devices. Although the effect of elastic strain has been uncovered theoretically, the effect of dislocations on the diffusion kinetics in such strained oxides is yet unclear. Here we investigate a 1/2o1104{100} edge dislocation by performing atomistic simulations in 4-12% doped CeO 2 as a model fast ion conductor. At equilibrium, depending on the size of the dopant, trivalent cations and oxygen vacancies are found to simultaneously enrich or deplete either in the compressive or in the tensile strain fields around the dislocation. The associative interactions among the point defects in the enrichment zone and the lack of oxygen vacancies in the depletion zone slow down oxide ion transport. This finding is contrary to the fast diffusion of atoms along the dislocations in metals and should be considered when assessing the effects of strain on oxide ion conductivity.