Publication

Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

Publications associées (31)

Graph generative deep learning models with an application to circuit topologies

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

EPFL2024

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

Euclid: The search for primordial features

Georges Meylan, Frédéric Courbin, Fabio Finelli, Richard Massey, Marcello Farina, Yiming Li, Yi Wang

Primordial features, in particular oscillatory signals, imprinted in the primordial power spectrum of density perturbations represent a clear window of opportunity for detecting new physics at high-energy scales. Future spectroscopic and photometric measur ...

Edp Sciences S A2024

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Alireza Modirshanechi

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

EPFL2024

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

David Atienza Alonso, Ali Pahlevan, Marina Zapater Sancho, Luis Maria Costero Valero, Darong Huang

Computing servers have played a key role in developing and processing emerging compute-intensive applications in recent years. Consolidating multiple virtual machines (VMs) inside one server to run various applications introduces severe competence for limi ...

2024

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Chen Shen, Hong Zhang, Changpeng Lin, Yong Zhao

Existing machine learning potentials for predicting phonon properties of crystals are typically limited on a material-to-material basis, primarily due to the exponential scaling of model complexity with the number of atomic species. We address this bottlen ...

NATURE PORTFOLIO2023

Correlation properties of a one-dimensional repulsive Bose gas at finite temperature

Riccardo Rota

We present a comprehensive study shedding light on how thermal fluctuations affect correlations in a Bose gas with contact repulsive interactions in one spatial dimension. The pair correlation function, the static structure factor, and the one-body density ...

IOP Publishing Ltd2023

Plasmonic effects in the neutralization of slow ions at a metallic surface

Marnik Bercx

Secondary electron emission is an important process that plays a significant role in several plasma-related applications. As measuring the secondary electron yield experimentally is very challenging, quantitative modelling of this process to obtain reliabl ...

WILEY-V C H VERLAG GMBH2023

Towards a multiscale point cloud structural similarity metric

Touradj Ebrahimi, Davi Nachtigall Lazzarotto

Point clouds are effective data structures for the rep- resentation of three-dimensional media and hence adopted in a wide range of practical applications. In many cases, the portrayed data is expected to be visualized by humans. After acquisition, point c ...

IEEE2023

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Marilyne Andersen, Sabine Süsstrunk, Kynthia Chamilothori, Bahar Aydemir, Caroline Karmann, Seungryong Kim

Saliency models are image-based prediction models that estimate human visual attention. Such models, when applied to architectural spaces, could pave the way for design decisions where visual attention is taken into account. In this study, we tested the pe ...

2023

Robustness of Local Predictions in Atomistic Machine Learning Models

Michele Ceriotti, Chiheb Ben Mahmoud, Federico Grasselli, Sanggyu Chong

Machine learning (ML) models for molecules and materials commonly rely on a decomposition of the global target quantity into local, atom-centered contributions. This approach is convenient from a computational perspective, enabling large-scale ML-driven si ...

Amer Chemical Soc2023