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Machine learning interatomic potentials (MLIPs) are routinely used to model diverse atomistic phenomena, yet parameterizing them to accurately capture solid-state phase transformations remains difficult. We present error metrics and…
Machine-learning interatomic potential (MLIP) has been of growing interest as a useful method to describe the energetics of systems of interest. In the present study, we examine the accuracy of linearized pairwise MLIPs and…
In this work a representative nickel superalloy, Ni70W20Nb10, was investigated in the presence of chlorine to quantify its early-stage dissolution behavior. Surface structures were generated from a bulk configuration sampled in equilibrium…
Machine learning interatomic potentials (MLIPs) offer near-ab initio accuracy with the efficiency of classical force fields, making them attractive for modeling electrolytes. Collecting a diverse training set is essential for their accuracy…
TiFe alloys are attractive compounds for solid-state stationary hydrogen storage. They can absorb hydrogen gas reversibly at near ambient temperatures and practical pressures with high volumetric capacities surpassing that of cryogenically…
We develop FIRE-Swap, a first-principles framework for sampling intrinsic compositional structures in complex perovskites with machine-learning interatomic potentials (MLIPs). Using both dedicated and universal MLIPs, we study the relaxor…
Seismic observations show a reduced compressional-wave gradient at the base of the outer core relative to the preliminary reference Earth model and seismic wave asymmetry between the east-west hemispheres at the top of the inner core. Here,…
Steric hindered frustrated Lewis pairs (FLPs) have been shown to activate hydrogen molecules, and their reactivity is strongly determined by the geometric parameters of the Lewis acid s and bases. A recent experimental study showed that…
Understanding the solvation structure of electrolytes is crucial for optimizing the performance and stability of lithium-ion batteries. Novel electrolytes are essential for enhancing electrolyte structure and ensuring better integration…
Foundational machine learning interatomic potentials (MLIPs) are being developed at a rapid pace, promising closer and closer approximation to ab initio accuracy. This unlocks the possibility to simulate much larger length and time scales.…
In the present article, we have studied the effect of the salt concentration (LiPF6) on transport properties and ion dynamics of blend solid polymer electrolyte (PEO PAN) prepared by solution cast technique. Fourier transform infrared…
Machine learned interaction potentials (MLIPs) have become a critical component of large-scale, high-quality simulations for a range of chemical and biochemical systems. Yet, despite their in-distribution accuracy, molecular dynamics…
Lithium fluoride (LiF) is a critical component for stabilizing lithium metal anode and high-voltage cathodes towards the next-generation high-energy-density lithium batteries. Recent modeling study reported the formation of wurtzite LiF…
Accurate atomistic simulations of gas-surface scattering require potential energy surfaces that remain reliable over broad configurational and energetic ranges while retaining the efficiency needed for extensive trajectory sampling. Here,…
Using atomic force microscopy experiments and molecular dynamics simulations of gold nanoislands on graphite, we investigate why ultra-small friction commonly associated with structural lubricity can be observed even under ambient…
We introduce Flexible Cutoff Learning (FCL), a method for training machine learning interatomic potentials (MLIPs) whose cutoff radii can be adjusted after training. Unlike conventional MLIPs that fix the cutoff radius during training, FCL…
Properties of doped and natural impurities in Lithium Niobate single crystals are studied using the Whispering Gallery Mode method at low temperatures as a function of magnetic field. The study reveals considerable coupling of microwave…
Interatomic potentials are key to uncovering microscopic structure-property relationships, essential for multiscale simulations and high-throughput experiments. For metallic glasses, their disordered atomic structure makes the development…
Machine learning interatomic potentials (MLIPs) are revolutionizing the field of molecular dynamics (MD) simulations. Recent MLIPs have tended towards more complex architectures trained on larger datasets. The resulting increase in…
This work investigates solute transport due to self-interstitial defects and radiation induced segregation tendencies in dilute ferritic alloys, by computing the transport coefficients of each system based on ab initio calculations of…