In silico chemical experiments in the Age of AI: From quantum chemistry to machine learning and back
Computational chemistry is an indispensable tool for understanding molecules and
predicting chemical properties. However, traditional computational methods face significant …
predicting chemical properties. However, traditional computational methods face significant …
Deep learning workflow for the inverse design of molecules with specific optoelectronic properties
The inverse design of novel molecules with a desirable optoelectronic property requires
consideration of the vast chemical spaces associated with varying chemical composition …
consideration of the vast chemical spaces associated with varying chemical composition …
Mixed precision support in HPC applications: What about reliability?
In their quest for exascale and beyond, High-Performance Computing (HPC) systems
continue becoming ever larger and more complex. Application developers, on the other …
continue becoming ever larger and more complex. Application developers, on the other …
DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets
Graph neural networks (GNNs) are a class of Deep Learning models used in designing
atomistic materials for effective screening of large chemical spaces. To ensure robust …
atomistic materials for effective screening of large chemical spaces. To ensure robust …
Graph-based networks for accurate prediction of ground and excited state molecular properties from minimal features
Graph neural networks (GNN) have been demonstrated to correlate molecular structure with
properties, enabling rapid evaluation of molecules for a given application. Molecular …
properties, enabling rapid evaluation of molecules for a given application. Molecular …
Invariant features for accurate predictions of quantum chemical uv-vis spectra of organic molecules
Including invariance of global properties of a phys-ical system as an intrinsic feature in
graph neural networks (GNNs) enhances the model's robustness and generalizability and …
graph neural networks (GNNs) enhances the model's robustness and generalizability and …
Graph Convolutional Neural Networks for Micro-Expression Recognition-Fusion of Facial Action Units for Optical Flow Extraction
X Yang, Y Fang, CRJ Rodolfo - IEEE Access, 2024 - ieeexplore.ieee.org
Micro-expression recognition is an important problem in the field of computer vision and
affective computing. To improve the accuracy of micro-expression recognition, the study …
affective computing. To improve the accuracy of micro-expression recognition, the study …
MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training
MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training
Page 1 MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks …
Page 1 MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks …
MDLoader: A Hybrid Model-Driven Data Loader for Distributed Graph Neural Network Training
Scalable data management is essential for processing large scientific dataset on HPC
platforms for distributed deep learning. In-memory distributed storage is preferred for its …
platforms for distributed deep learning. In-memory distributed storage is preferred for its …
Machine learning for ultraviolet spectral prediction
LH Manh - 2023 - search.proquest.com
Abstract Machine Learning has found wide applications in material science, including
dielectric polymers, superconducting materials, and drug property prediction. The use of …
dielectric polymers, superconducting materials, and drug property prediction. The use of …