CPS- Guest Faculty Research Participant- Wenjie Xia

Argonne National Laboratory

Lemont, IL

Job posting number: #7135903 (Ref:415442)

Posted: March 24, 2023

Application Deadline: Open Until Filled

Job Description

Training and validation of Neural Networks (NN) are very computationally intensive. Deep Learning (DL) methods have dominated image processing lately and are gaining relevance in visual simulations. Most DL models assume that the input data distribution is identical between test and validation, but most often, they are not. In other cases, the test data is uncertain, and labeling may differ based on individual expert evaluations. This discrepancy makes DL less reliable for tasks like traffic signal recognition for self-driving cars, medical images where labels are difficult to assign, and other tasks where the precision of the output is crucial. By adding the capability of propagating uncertainty to our results, image processing models can provide not just a single prediction of the identification but a distribution over predictions.

Position Requirements

Two-dimensional (2D) sheet materials continue to draw a lot of attention, particularly for their superior thermal and mechanical properties. At a nanoscale, 2D materials often bear certain defects during the synthesis and processing due to their atomically thin in nature, yielding intriguing physical behaviors and functionalities. Nowadays, defects can be artificially introduced into 2D materials through various preparation methodology. Therefore, it is important to better understand the effects of topological defects on the morphology and physical properties of 2D materials at a fundamental level. Molecular dynamics (MD) simulations are among the most powerful methods for computing these properties, but their reliability depends on the accuracy of interatomic interactions. While first principles approaches provide the most accurate description of interatomic forces, they are computationally expensive. In contrast, classical force fields are computationally efficient, but have limited accuracy in interatomic force description. Machine learning (ML) interatomic potentials, such as Gaussian Approximation Potentials, trained on density functional theory (DFT) calculations offer a compromise by providing both accurate estimation and computational efficiency. In this project, we will develop and apply the ML potentials in MD simulations of physical properties of selected 2D materials (e.g., graphene) under the influences of several different topological defects.

Job Family

Visiting Faculty Appointment

Job Profile

Guest Faculty Research Participant

Worker Type

Short-Term (Fixed Term)

Time Type

Full time

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Job posting number:#7135903 (Ref:415442)
Application Deadline:Open Until Filled
Employer Location:Argonne National Laboratory
Argonne,Illinois
United States
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