Improving Quantum Mechanical Calculations Using Graph Neural Networks to Predict Energies from Atomic Structure

Description

Graph neural networks (GNN) offer a potential method of bypassing the Kohn-Sham equations in density functional theory (DFT) calculations by learning both the Hohenberg-Kohn (HK) mapping of electron density to energy, allowing for calculations of much larger atomic systems and…

Graph neural networks (GNN) offer a potential method of bypassing the Kohn-Sham equations in density functional theory (DFT) calculations by learning both the Hohenberg-Kohn (HK) mapping of electron density to energy, allowing for calculations of much larger atomic systems and time scales and enabling large-scale MD simulations with DFT-level accuracy. In this work, we investigate the feasibility of GNNs to learn the HK map from the external potential approximated as Gaussians to the electron density 𝑛(π‘Ÿ), and the mapping from 𝑛(π‘Ÿ) to the energy density 𝑒(π‘Ÿ) using Pytorch Geometric. We develop a graph representation for densities on radial grid points and determine that a k-nearest neighbor algorithm for determining node connections is an effective approach compared to a distance cutoff model, having an average graph size of 6.31 MB and 32.0 MB for datasets with π‘˜ = 10 and π‘˜ = 50 respectively. Furthermore, we develop two GNNs in Pytorch Geometric, and demonstrate a decrease in training losses for a 𝑛(π‘Ÿ) to 𝑒(π‘Ÿ) of 8.52 Β· 10^14 and 3.10 Β· 10^14 for π‘˜ = 10 and π‘˜ = 20 datasets respectively, suggesting the model could be further trained and optimized to learn the electron density to energy functional.

Date Created
2023-05
Agent

Analysis of Machine Learning Assisted Fatigue Identification in Radiology Readings

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Description
Fatigue in radiology is a readily studied area. Machine learning concepts appliedto the identification of fatigue are also readily available. However, the intersection between the two areas is not a relative commonality. This study looks to explore the intersection of fatigue in…

Fatigue in radiology is a readily studied area. Machine learning concepts appliedto the identification of fatigue are also readily available. However, the intersection between the two areas is not a relative commonality. This study looks to explore the intersection of fatigue in radiology and machine learning concepts by analyzing temporal trends in multivariate time series data. A novel methodological approach using support vector machines to observe temporal trends in time-based aggregations of time series data is proposed. The data used in the study is captured in a real-world, unconstrained radiology setting where gaze and facial metrics are captured from radiologists performing live image reviews. The captured data is formatted into classes whose labels represent a window of time during the radiologist’s review. Using the labeled classes, the decision function and accuracy of trained, linear support vector machine models are evaluated to produce a visualization of temporal trends and critical inflection points as well as the contribution of individual features. Consequently, the study finds valid potential justification in the methods suggested. The study offers a prospective use of maximummargin classification to demarcate the manipulation of an abstract phenomenon such as fatigue on temporal data. Potential applications are envisioned that could improve the workload distribution of the medical act.
Date Created
2022
Agent
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