Beyond potentials: Integrated machine learning models for materials

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A range of behavioral and contextual factors, including eating and drinking behavior, mood, social context, and other daily activities, can significantly impact an individual's quality of life and overall well-being. Therefore, inferring everyday life aspe ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Over the years, multimodal mobile sensing has been used extensively for inferences regarding health and well-being, behavior, and context. However, a significant challenge hindering the widespread deployment of such models in real-world scenarios is the is ...

The application of computational biology in drug development for membrane protein targets has experienced a boost from recent developments in deep learning-driven structure prediction, increased speed and resolution of structure elucidation, machine learni ...

While momentum-based accelerated variants of stochastic gradient descent (SGD) are widely used when training machine learning models, there is little theoretical understanding on the generalization error of such methods. In this work, we first show that th ...

The vast amount of computational studies on electrical conduction in solid-state electrolytes is not mirrored by comparable efforts addressing thermal conduction, which has been scarcely investigated despite its relevance to thermal management and (over)he ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

Earth scientists study a variety of problems with remote sensing data, but they most often consider them in isolation from each other, which limits information flows across disciplines. In this work, we present METEOR, a meta-learning methodology for Earth ...

This dataset accompanies the publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films" published in Light: Science & Applications (https://doi.org/10.1038/s41377-024-01408-2). The data can be used to reproduce plots 1-4 ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Beyond potentials: Integrated machine learning models for materials

Chattez avec Graph Search

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Digital Twins of Stone Masonry Buildings for Damage Assessment

Residual-based attention in physics-informed neural networks

M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training

Computational drug development for membrane protein targets

On the Generalization of Stochastic Gradient Descent with Momentum

Thermal conductivity of Li 3 PS 4 solid electrolytes with ab initio accuracy

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Meta-learning to address diverse Earth observation problems across resolutions

Dataset to accompany publication "Quantum-mechanical effects in photoluminescence from thin crystalline gold films"

Topics in statistical physics of high-dimensional machine learning