2024-04-03 08:00:00
The strong and highly anisotropic influence of grain boundary (GB) character on the plasticity of crystalline materials motivates an overarching goal of developing a bicrystallography-sensitive GB model applicable across all misorientations, inclinations, and crystal systems. Here, we focus on the shear coupling factor of…
Helium bubbles impact mechanical properties of nuclear materials. An Fe-Ni-Cr-H-He potential has been developed to enable molecular dynamics simulations of helium bubble nucleation and growth. This is accomplished by addressing three challenging paradoxes: (a) helium forms tightly bound dimers and clusters in the lattice…
Reverse Monte Carlo (RMC) is a technique to interpret experimental diffraction data by applying the Metropolis Monte Carlo algorithm to derive an atomistic model that fits the data. This optimization method is inherently stochastic and underconstrained, so it tends to yield highly disordered and…
Machine learning (ML) interatomic potentials offer new opportunities to accurately simulate larger material systems for longer periods of time. In the past decade, a large number of ML potential models have been proposed, but the assessment of their reliability and the quality of the…
Atomistic simulations are an important tool in materials modeling. Interatomic potentials (IPs) are at the heart of such molecular models, and the accuracy of a model’s predictions depends strongly on the choice of IP. Uncertainty quantification (UQ) is an emerging tool for assessing the…
Atomistic simulations are widely used in materials modeling, and there is a recognized need to quantify the uncertainty associated with the predictions of these models. A major source of uncertainty originates from the choice of Interatomic Potential (IP). For classical empirical potentials, the largest…
Machine learning interatomic potentials (MLIP) are now increasingly being used in materials simulations. They provide near first-principle’s accuracy at a computational cost comparable to empirical force fields. MLIPs have been growing in complexity and accuracy, however most are developed and used by a handful…
The emergence of data-driven approaches for developing interatomic potentials promises to transform materials design and synthesis. Machine learning interatomic potentials (MLIPs) build on recent advances in ML to accurately model the potential energy surface of a material system by inferring its form from a…
For decades, atomistic modeling has played a crucial role in predicting the behavior of materials in numerous fields ranging from nanotechnology to drug discovery. The most accurate methods in this domain are rooted in first-principles quantum mechanical calculations such as density functional theory (DFT)….
Interatomic potentials are invaluable tools in the fields of computational materials science, chemistry, and biology for their ability to accelerate atomic-scale simulations beyond the length- and time-scales that are accessible using first-principles techniques. With the advent of machine learning interatomic potentials (MLIPs), an increasingly…
