We present a new interatomic potential for solids and liquids called Spectral Neighbor Analysis Potential (SNAP). The SNAP potential has a very general form and uses machinelearning techniques to reproduce the energies, forces, and stress tensors of a large set of small configurations of atoms, which are obtained using high-accuracy quantum electronic structure (QM) calculations. The local environment of each atom is characterized by a set of bispectrum components of the local neighbor density projected onto a basis of hyperspherical harmonics in four dimensions. The bispectrum components are the same bond-orientational order parameters employed by the GAP potential [1]. The SNAP potential, unlike GAP, assumes a linear relationship between atom energy and bispectrum components. The linear SNAP coefficients are determined using weighted least-squares linear regression against the full QM training set. This allows the SNAP potential to be fit in a robust, automated manner to large QM data sets using many bispectrum components. The calculation of the bispectrum components and the SNAP potential are implemented in the LAMMPS parallel molecular dynamics code. We demonstrate that a previously unnoticed symmetry property can be exploited to reduce the computational cost of the force calculations by more than one order of magnitude. We present results for a SNAP potential for tantalum, showing that it accurately reproduces a range of commonly calculated properties of both the crystalline solid and the liquid phases. In addition, unlike simpler existing potentials, SNAP correctly predicts the energy barrier for screw dislocation migration in BCC tantalum.

Controlling site disorder in ternary and multinary compounds enables tuning optical and electronic properties at fixed lattice constants and stoichiometries, moving beyond many of the challenges facing binary alloy systems. Here, we consider possible enhancements to energy-related applications through the integration of disorder-tunable materials in devices such as light-emitting diodes, photonics, photovoltaics, photocatalytic materials, batteries, and thermoelectrics. However, challenges remain in controlling and characterizing disorder. Focusing primarily on II–IV–V2 materials, we identify three metrics for experimentally characterizing cation site disorder. Complementary to these experiments, we discuss simulation methods to understand disordered materials. Nonidealities, such as off-stoichiometry and oxygen incorporation, can occur while synthesizing metastable disordered materials. While nonidealities may seem undesirable, we describe how if harnessed they could provide another knob for tuning disorder and subsequently properties. To illustrate the effects of disorder on device-relevant properties, we provide case examples of disordered materials and their potential in device applications.

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It is well known that screw dislocation motion dominates the plastic deformation in body-centeredcubic metals at low temperatures. The nature of the non-planar structure of screw dislocations gives rise to high lattice friction which results in strong temperature and strain rate dependence of plastic flow. Thus, the nature of the Peierls potential, which is responsible for the high lattice resistance, is an important physical property of the material. However, current empirical potentials give a complicated picture of the Peierls potential. Here, we investigate the nature of the Peierls potential using Density Functional Theory in the BCC transition metals. The results show that the shape of the Peierls potential is sinusoidal for every material investigated. Furthermore, we show the magintiude of the potential scales strongly with the energy per-unit-length of the screw dislocation in the material.