Publications associées à Multitask methods for predicting molecular properties from heterogeneous data

Publications associées (17)

Machine learning Hubbard parameters with equivariant neural networks

Nicola Marzari, Iurii Timrov, Martin Uhrin, Luca Binci, Austin Jacob Zadoks

Density-functional theory with extended Hubbard functionals (DFT + U + V) provides a robust framework to accurately describe complex materials containing transition-metal or rare-earth elements. It does so by mitigating self-interaction errors inherent to ...

NATURE PORTFOLIO2025

Controlled Training Data Generation with Diffusion Models

Aleksandr Alekseev, Shuqing Teresa Yeo, Andrei Atanov

We present a method to control a text-to-image generative model to produce training data useful for supervised learning. Unlike previous works that employ an open-loop approach via pre-defined prompts to generate new data using either a language model or h ...

2025

Applications of semiclassical and mixed quantum-classical dynamics to study molecular systems with multiple electronic states

Alan Scheidegger

Computational simulations of quantum molecular dynamics are indispensable for understanding and predicting phenomena in physical chemistry. This thesis explores molecular processes where multiple electronic states play a role. A significant part of this wo ...

EPFL2025

Adversarial Training For Defense Against Label Poisoning Attacks

Volkan Cevher

As machine learning models grow in complexity and increasingly rely on publicly sourced data, such as the human-annotated labels used in training large language models, they become more vulnerable to label poisoning attacks. These attacks, in which adversa ...

2025

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem

Fernando Perez Cruz

Training high-quality deep learning models is a challenging task due to computational and technical requirements. A growing number of individuals, institutions, and companies increasingly rely on pre-trained, third-party models made available in public rep ...

2025

Monolithic silicon nitride electro-optic modulator enabled by optically-assisted poling

Camille Sophie Brès, Boris Zabelich, Christian André Clément Lafforgue

Electro-optic (EO) modulation is a key functionality to have on-chip. However, achieving a notable linear EO effect in stoichiometric silicon nitride has been a persistent challenge due to the material's intrinsic properties. Recent advancements revealed t ...

NATURE PORTFOLIO2025

Joint Multiband Deconvolution for Euclid and Vera C. Rubin Images

Frédéric Courbin, Pascale Jablonka, Utsav Akhaury

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to ...

EDP Sciences SA2025

Helmet material design for mitigating traumatic axonal injuries through AI-driven constitutive law enhancement

Dominique Pioletti, Pierre-Etienne Bourban, Naser Nasrollahzadeh Mamaghani, Vincent Marc Varanges, Pezhman Eghbalishamsabadi

Sports helmets provide incomplete protection against brain injuries. Here we aim to improve helmet liner efficiency by employing a novel approach that optimizes their properties. By exploiting a finite element model that simulates head impacts, we develope ...

2025

Unravelling the components of diffuse scattering using deep learning

Lucas Sebastian Powell-Rudden

Many technologically important material properties are underpinned by disorder and short-range structural correlations; therefore, elucidating structure-property relationships in functional materials requires understanding both the average and the local st ...

Int Union Crystallography2024

Enhancing Peer Review with AI-Powered Suggestion Generation Assistance: Investigating the Design Dynamics

Xiaotian Su, Seyed Parsa Neshaei

While writing peer reviews resembles an important task in science, education, and large organizations, providing fruitful suggestions to peers is not a straightforward task, as different user interaction designs of text suggestion interfaces can have diver ...

Association for Computing Machinery2024

Density functional perturbation theory for one-dimensional systems: Implementation and relevance for phonons and electron-phonon interactions

Nicola Marzari, Norma Rivano

The electronic and vibrational properties and electron-phonon couplings of one-dimensional materials will be key to many prospective applications in nanotechnology. Dimensionality strongly affects these properties and has to be correctly accounted for in f ...

2024

Microscale droplet assembly enables biocompatible multifunctional modular iontronics

Yujia Zhang

Hydrogel iontronic devices can emulate biological functions and communicate with living matter. But the fabrication of miniature, soft iontronic devices according to modular designs has not been achieved. In this work, we report the use of surfactant-suppo ...

AMER ASSOC ADVANCEMENT SCIENCE2024

Reaction-Agnostic Featurization of Bidentate Ligands for Bayesian Ridge Regression of Enantioselectivity

Jérôme Waser, Anne-Clémence Corminboeuf, Matthew Wodrich, Rubén Laplaza Solanas, Alexandre Alain Schöpfer

Chiral ligands are important components in asymmetric homogeneous catalysis, but their synthesis and screening can be both time-consuming and resource-intensive. Data-driven approaches, in contrast to screening procedures based on intuition, have the poten ...

Amer Chemical Soc2024

Qualify-as-you-go: sensor fusion of optical and acoustic signatures with contrastive deep learning for multi-material composition monitoring in laser powder bed fusion process

Christian Leinenbach, Antonios Nikolaos Baganis

Growing demand for multi-material Laser Powder Bed Fusion (LPBF) faces process control and quality monitoring challenges, particularly in ensuring precise material composition. This study explores optical and acoustic emission signals during LPBF processes ...

Taylor & Francis Ltd2024

Fast prediction of anharmonic vibrational spectra for complex organic molecules

Lorenzo Monacelli

Interpreting Raman and IR vibrational spectra in complex organic molecules lacking symmetries poses a formidable challenge. In this study, we propose an innovative approach for simulating vibrational spectra and attributing observed peaks to molecular moti ...

NATURE PORTFOLIO2024

FVLLMONTI: The 3D Neural Network Compute Cube (N2C2) Concept for Efficient Transformer Architectures Towards Speech-to-Speech Translation

David Atienza Alonso, Giovanni Ansaloni, Alireza Amirshahi

This multi-partner-project contribution introduces the midway results of the Horizon 2020 FVLLMONTI project. In this project we develop a new and ultra-efficient class of ANN accelerators, the neural network compute cube (N2C2), which is specifically desig ...

Institute of Electrical and Electronics Engineers Inc.2024

Machine Learning for Optical Motion Capture-Driven Musculoskeletal Modelling from Inertial Motion Capture Data

Rahul Sharma

Marker-based Optical Motion Capture (OMC) systems and associated musculoskeletal (MSK) modelling predictions offer non-invasively obtainable insights into muscle and joint loading at an in vivo level, aiding clinical decision-making. However, an OMC system ...

MDPI2023