2025-04-09 09:00:00
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…
The development of interatomic models requires a scientific workflow which combines a wide range of different tools and utilities. It typically starts with the generation of atomistic structures, continues with the evaluation of these structures with an ab-initio reference method like density functional theory…
In this hands-on tutorial, attendees will be introduced to the KIM API, and to several features of the OpenKIM Property Testing Framework. The KIM API allows for seamless use of the 600+ interatomic models (IMs) archived in OpenKIM with any compatible simulation code. The…
The quality of classical molecular and multiscale simulations hinges on the fidelity of the employed interatomic model (IM) for a given application. Reproducibility of simulations depends on the ability of researchers to retrieve the original IM that was used. These two issues are addressed…
With the advent of atomistic modeling and high-performance computing, the search for crystalline compounds that are distinct from previously explored systems is an ever-growing problem. The multitude of different crystal representations exacerbates the issue, obfuscating structural similarity. To explore novel regions of materials space,…
