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Found 7 papers, 3 papers with code
Cascading Blackout Severity Prediction with Statistically-Augmented Graph Neural Networks
Higher variability in grid conditions, resulting from growing renewable penetration and increased incidence of extreme weather events, has increased the difficulty of screening for scenarios that may lead to catastrophic cascading failures.
Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models
In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method to predict 3D structures of disordered materials from a target property.
Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model
Our current SD implementation is about two orders of magnitude faster than the MD counterpart for the systems studied in this work.
Score-based denoising for atomic structure identification
We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter.
Challenges and approaches to privacy preserving post-click conversion prediction
In this paper we outline the recent privacy-related changes in the online advertising ecosystem from a machine learning perspective.
Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy
In this work, we extend the recently proposed ALIGNN encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d).
Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials
Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes.
