相关论文: The Open Catalyst 2020 (OC20) Dataset and Communit…

Face relighting aims to synthesize realistic portraits under novel illumination while preserving identity and geometry. However, progress remains constrained by the limited availability of large-scale, physically consistent illumination…

The performance of optical flow algorithms greatly depends on the specifics of the content and the application for which it is used. Existing and well established optical flow datasets are limited to rather particular contents from which…

How much data is needed to optimally schedule distributed energy resources (DERs)? Does the distribution system operator (DSO) have to know load demands at each bus of the feeder to solve an optimal power flow (OPF)? This work exploits…

Understanding the performance of machine learning models across diverse data distributions is critically important for reliable applications. Motivated by this, there is a growing focus on curating benchmark datasets that capture…

Accurate lane detection is essential for automated driving, enabling safe and reliable vehicle navigation across a variety of road scenarios. Numerous datasets have been introduced to support the development and evaluation of lane detection…

Density functional theory (DFT) can be quite advantageous in advancing the field of catalysis because of the microscopic insights it provides, and thus can guide experimental searches of novel catalysts. Several recent works have…

Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the…

We show that classical molecular density functional theory (MDFT), here in the homogeneous reference fluid approximation in which the functional is inferred from the properties of the bulk solvent, is a powerful new tool to study, at a…

Resilient operation of the power system during ongoing wildfires is challenging because of the uncertain ways in which the fires impact the electric power infrastructure (multiple arc-faults, complete melt-down). To address this challenge,…

Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through the calculation of…

Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. However, the application of machine learning models in these…

As deep learning models grow in complexity and the volume of training data increases, reducing storage and computational costs becomes increasingly important. Dataset distillation addresses this challenge by synthesizing a compact set of…

Object detection (OD) has become vital for numerous computer vision applications, but deploying it on resource-constrained IoT devices presents a significant challenge. These devices, often powered by energy-efficient microcontrollers,…

The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational…

A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more representative or informative than others. This unequal importance…

A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels. In this work, we introduce the openXDATA tool that completes the missing labels in partially labelled or unlabelled datasets in…

Machine learning approaches have the potential to approximate Density Functional Theory (DFT) for atomistic simulations in a computationally efficient manner, which could dramatically increase the impact of computational simulations on…

Foundation models (e.g. ChatGPT, StableDiffusion) pervasively influence society, warranting immediate social attention. While the models themselves garner much attention, to accurately characterize their impact, we must consider the broader…

Interactions between negatively charged aluminosilicate species and positively charged metal cations are critical to many important engineering processes and applications, including sustainable cements and aluminosilicate glasses. In an…

Density Functional Theory (DFT) calculations have been widely used to predict the activity of catalysts based on the free energies of reaction intermediates. The incorporation of the state of the catalyst surface under the electrochemical…