Publications associées à Finite-temperature materials modeling from the quantum nuclei to the hot electron regime

Publications associées (38)

Exploring the portability of ML-based lightning nowcasting models

Farhad Rachidi-Haeri, Marcos Rubinstein, Amirhossein Mostajabi, Seyed Mohammad Ehsan Mansouri Tehrani

Significant damages caused by lightning can be avoided by predicting lightning and taking precautionary measures. Here, a machine learning model is developed to nowcast lightning flashes using dew point temperature, precipitation, wind speed, wind directio ...

Elsevier2025

Probing the latent hierarchical structure of data via diffusion models

Noam Itzhak Levi, Alessandro Favero, Antonio Sclocchi

High-dimensional data must be highly structured to be learnable. Although the compositional and hierarchical nature of data is often put forward to explain learnability, quantitative measurements establishing these properties are scarce. Likewise, accessin ...

2025

High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

Roya Moghaddasi Fereidani

Molecular quantum dynamics simulations are essential for understanding many fundamental phenomena in physics and chemistry. They often require solving the time-dependent Schrödinger equation for molecular nuclei, which is challenging even for medium-sized ...

EPFL2024

Light Emission and Conductance Fluctuations in Electrically Driven and Plasmonically Enhanced Molecular Junctions

Zhiyuan Xie, Christophe Marcel Georges Galland, Wen Chen, Sakthi Priya Amirtharaj, Konstantin Malchow

Electrically connected and plasmonically enhanced molecular junctions combine the optical functionalities of high field confinement and enhancement (cavity function), and of high radiative efficiency (antenna function) with the electrical functionalities o ...

Amer Chemical Soc2024

Model Tuning and Performance Evaluation in Machine Learning Models for PV Power Forecasting: Case Study of a BIPV System in Switzerland

Hugo James André Quest

High PV penetration into the electricity grid can lead to issues such as congestion if PV systems are not integrated effectively. Accurate PV power forecasting helps to address this issue. In recent years machine learning approaches have gotten a lot of at ...

Institute of Electrical and Electronics Engineers Inc.2024

Room-Temperature Quantum Optomechanics and Free-Electron Quantum Optics

Guanhao Huang

Quantum optics studies how photons interact with other forms of matter, the understanding of which was crucial for the development of quantum mechanics as a whole. Starting from the photoelectric effect, the quantum property of light has led to the develop ...

EPFL2024

Towards Scalable Electronics: Synthetic 2D Materials for Large-Area 2D Circuit Integration

Zhenyu Wang

In the past decades, a significant increase of the transistor density on a chip has led to exponential growth in computational power driven by Moore's law. To overcome the bottleneck of traditional von-Neumann architecture in computational efficiency, effo ...

EPFL2024

Computational Homogenization for Inverse Design of Surface-based Inflatables

Mark Pauly, Florin Isvoranu, Francis Julian Panetta, Yingying Ren, Seiichi Eduardo Suzuki Erazo, Uday Kusupati

Surface-based inflatables are composed of two thin layers of nearly inextensible sheet material joined together along carefully selected fusing curves. During inflation, pressure forces separate the two sheets to maximize the enclosed volume. The fusing c ...

Association for Computing Machinery (ACM)2024

Meta-learning to address diverse Earth observation problems across resolutions

Devis Tuia, Marc Conrad Russwurm, Benjamin Alexander Kellenberger

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 ...

Springernature2024

Assessing the Performance of Deep Learning Predictions for Dynamic Aperture of a Hadron Circular Particle Accelerator

Tatiana Pieloni, Davide Di Croce

Understanding the concept of dynamic aperture provides essential insights into nonlinear beam dynamics, beam losses, and the beam lifetime in circular particle accelerators. This comprehension is crucial for the functioning of modern hadron synchrotrons li ...

2024

Ultrahigh-quality-factor micro- and nanomechanical resonators using dissipation dilution

Tobias Kippenberg, Nils Johan Engelsen, Alberto Beccari

Mechanical resonators are widely used in sensors, transducers and optomechanical systems, where mechanical dissipation sets the ultimate limit to performance. Over the past 15 years, the quality factors in strained mechanical resonators have increased by f ...

Nature Portfolio2024

Room-temperature quantum optomechanics using an ultralow noise cavity

Tobias Kippenberg, Guanhao Huang, Nils Johan Engelsen, Alberto Beccari

At room temperature, mechanical motion driven by the quantum backaction of light has been observed only in pioneering experiments in which an optical restoring force controls the oscillator stiffness1,2. For solid-state mechanical resonators in which oscil ...

Nature Portfolio2024

Solvation Free Energies from Machine Learning Molecular Dynamics

Nicola Marzari, Nicéphore Arthur François Bonnet

The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...

Amer Chemical Soc2024

Random matrix methods for high-dimensional machine learning models

Antoine Philippe Michel Bodin

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

EPFL2024

Computational fluid dynamics simulations of the heat transfer properties of graphene-based nanolubricants and application to hydrodynamic lubrication

Roberto Guarino

In this paper, we consider experimental data available for graphene-based nanolubricants to evaluate their convective heat transfer performance by means of computational fluid dynamics (CFD) simulations. Single-phase models with temperature-dependent prope ...

Aip Publishing2024

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

Lakmal Buddika Meegahapola

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 ...

EPFL2024

Frustrated magnets in the limit of infinite dimensions: Dynamics and disorder-free glass transition

Achille Mauri

We study the statistical mechanics and the equilibrium dynamics of a system of classical Heisenberg spins with frustrated interactions on a d -dimensional simple hypercubic lattice, in the limit of infinite dimensionality d -> infinity . In the analysis we ...

Amer Physical Soc2024

Design for demise applied to spacecraft structural panels and experiments for ClearSpace One platform

Alexandre Achille Looten

With growing awareness of the vulnerability of the near-Earth space region and the anticipated surge in satellite objects, efforts are underway to assess and implement various mitigation strategies. These aim to minimize the impact of space activities and ...

EPFL2024

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Ksenia Briling

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

EPFL2024

Magnetic orderings and electronic structure of complex oxides from advanced Hubbard functionals

Luca Binci

Transition metal oxides represent a class of materials displaying very unusual electronic, structural and magnetic properties. They are extremely interesting, both from a technological and fundamental point of view. The most important characteristic of the ...

EPFL2023