Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of ML-IAPs based on four local environment descriptors -Behler-Parrinello symmetry functions, smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment tensors -using a diverse data set generated using high-throughput density functional theory (DFT) calculations. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic constants and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model, and consequently computational cost. We will discuss these trade-offs in the context of model selection for molecular dynamics and other applications.

Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

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Machine-learned interatomic potentials based on local environment descriptors represent a transformative leap over traditional potentials based on rigid functional forms in terms of prediction accuracy. However, a challenge in their application to ionic systems is the treatment of long-ranged electrostatics. Here, we present a highly accurate electrostatic Spectral Neighbor Analysis Potential (eSNAP) for ionic α-Li 3 N, a prototypical lithium superionic conductor of interest as a solid electrolyte or coating for rechargeable lithium-ion batteries. We show that the optimized eSNAP model substantially outperforms traditional Coulomb-Buckingham potential in the prediction of energies and forces, as well as various properties, such as lattice constants, elastic constants and phonon dispersion curves. We also demonstrate the application of eS-NAP in long-time, large-scale Li diffusion studies in Li 3 N, providing atomistic insights into measures of concerted ionic motion (e.g., the Haven ratio) and grain boundary diffusion. This work aims at providing an approach to developing quantum-accurate force fields for multi-component ionic systems under the SNAP formalism, enabling large scale atomistic simulations for such systems. *

In recent years, efficient inter-atomic potentials approaching the accuracy of density functional theory (DFT) calculations have been developed using rigorous atomic descriptors satisfying strict invariances, for example, to translation, rotation, permutation of homonuclear atoms, among others. In this work, we generalize the spectral neighbor analysis potential (SNAP) model to bcc-fcc binary alloy systems. We demonstrate that machine-learned SNAP models can yield significant improvements even over well-established, high-performing embedded atom method (EAM) and modified EAM (MEAM) potentials for fcc Cu and Ni. We also report on the development of a SNAP model for the fcc Ni-bcc Mo binary system by machine learning a carefully-constructed large computed data set of elemental and intermetallic compounds. We demonstrate that this binary Ni-Mo SNAP model can achieve excellent agreement with experiments in the prediction of Ni-Mo phase diagram as well as near-DFT accuracy in the prediction of many key properties such as elastic constants, formation energies, melting points, etc., across the entire binary composition range. In contrast, the existing Ni-Mo EAM has significant errors in the prediction of the phase diagram and completely fails in binary compounds. This work provides a systematic model development process for multicomponent alloy systems, including an efficient procedure to optimize the hyper-parameters in the model fitting, and paves the way to long-time, large-scale simulations of such systems. arXiv:1806.04777v2 [cond-mat.mtrl-sci] 16 Aug 2018 Machine learning (ML) models based on robust local environment descriptors have recently emerged as an approach to describe the potential energy surface (PES) of systems of atoms with near-quantum accuracy at several of orders magnitude lower cost than ab initio methods. 1-6 Effective local environment descriptors must be invariant under translation, rotation, and permutation of homonuclear atoms, and have the properties of uniqueness and differentiability. 7 Examples of such descriptors include symmetry functions 1,8 , smooth overlap of atomic positions (SOAP) 4,9 , bispectrum 2,5 , Coulomb matrix 3,10,11 , among others.A typical approach is to fit the PES as a function of these descriptors by machine learning on ab initio data sets, using techniques ranging from simple linear regression 5,12 to kernel ridge regression 6,7 to neural networks [13][14][15][16] .Thus far, the development of ML potentials based on local environment descriptors have largely been limited to elements and oxides. The Gaussian approximation potential (GAP) using the SOAP descriptor has been applied on Si 4 , C 17,18 , W 9 , P 19 , and Fe 20 , and neural network models based on symmetry functions have been fitted for Si 21 , C 22 , Na 23 , ZnO 24 , TiO 2 25 , GeTe 26 , and Li 3 PO 4 27 . Thompson and Wood 5,28 have developed linear and quadratic models based on the SO(4) bispectrum -the Spectral Analysis Neighbor Potential or SNAP -for bcc Ta and W. Chen et al. 12 later showed that a line...

Refractory multi-principal element alloys (MPEAs) have exceptional mechanical properties, including high strength-to-weight ratio and fracture toughness, at high temperatures. Here we elucidate the complex interplay between segregation, short-range order, and strengthening in the NbMoTaW MPEA through atomistic simulations with a highly accurate machine learning interatomic potential. In the single crystal MPEA, we find greatly reduced anisotropy in the critically resolved shear stress between screw and edge dislocations compared to the elemental metals. In the polycrystalline MPEA, we demonstrate that thermodynamically driven Nb segregation to the grain boundaries (GBs) and W enrichment within the grains intensifies the observed short-range order (SRO). The increased GB stability due to Nb enrichment reduces the von Mises strain, resulting in higher strength than a random solid solution MPEA. These results highlight the need to simultaneously tune GB composition and bulk SRO to tailor the mechanical properties of MPEAs.

A new 18 F-labeled tetrazine derivative was developed aiming at optimal radiochemistry, fast reaction kinetics in inverse electron-demand Diels−Alder cycloaddition (IEDDA), and favorable pharmacokinetics for in vivo bioorthogonal chemistry. The radiolabeling of the tetrazine was achieved in high yield, purity, and specific activity under mild reaction conditions via conjugation with 5-[ 18 F]fluoro-5-deoxyribose, providing a glycosylated tetrazine derivative with low lipophilicity. The 18 F-tetrazine showed fast reaction kinetics toward the most commonly used dienophiles in IEDDA reactions. It exhibited excellent chemical and enzymatic stability in mouse plasma and in phosphate-buffered saline (pH 7.41). Biodistribution in mice revealed favorable pharmacokinetics with major elimination via urinary excretion. The results indicate that the glycosylated 18 F-labeled tetrazine is an excellent candidate for in vivo bioorthogonal chemistry applications in pretargeted PET imaging approaches.

In this work, we performed a comprehensive study of Prussian blue and its analogues (PBAs), one of the most promising cathode materials for aqueous sodium-ion batteries for large-scale energy-storage systems, using first-principles calculations. It is confirmed that dry PBAs generally undergo a phase transition from a rhombohedral Na2PR­(CN)6 (where P and R are transition metals) to a tetragonal/cubic PR­(CN)6 during Na extraction, in agreement with experimental observations. Using a grand potential phase diagram construction, we show that water and Na co-intercalation result in fundamentally different phase transition behavior and, hence, electrochemical voltage profiles in wet versus dry electrolytes. Lattice water increases the average voltage and reduces the volume change during electrochemical cycling, resulting in both higher energy density and better cycling stability. Finally, we identified four new PBA compositions, Na2CoMn­(CN)6, Na2NiMn­(CN)6, Na2CuMn­(CN)6, and Na2ZnMn­(CN)6, that show great promise as cathodes for aqueous rechargeable Na-ion batteries.