Publications related to On machine learning force fields for metallic nanoparticles

Related publications (44)

Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems

David Atienza Alonso, Giovanni Ansaloni, Rafael Medina Morillas, Pedro Palacios Almendros

Efficient deployment of resource-intensive transformers on edge devices necessitates cross-stack optimization. We thus study the interrelation between structured pruning and systolic acceleration, matching the size of pruned blocks with the systolic array ...

Association for Computing Machinery2025

Programming Nonlinear Computation with Nonlinear and Linear Optics

Jih-Liang Hsieh

The escalating demands of artificial intelligence have underscored the limitations of traditional electronic computing, particularly in terms of power consumption and scalability. Optical computing offers a promising alternative, providing advantages in sp ...

EPFL2025

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

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

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

Dilution versus fractionation: Separation technologies hyphenated with spICP-MS for characterizing metallic nanoparticles in aerosols

Jian Wang, Christian Ludwig, Andrea Testino, Tianyu Cen

The presence of metal salts has become one of the major limitations for measuring metallic nanoparticles (NPs) in single particle inductively coupled plasma mass spectrometry (spICP-MS). Their presence generates a background signal in spICP-MS that can be ...

2024

SPAHM(a,b): Encoding the Density Information from Guess Hamiltonian in Quantum Machine Learning Representations

Anne-Clémence Corminboeuf, Alberto Fabrizio, Ksenia Briling, Yannick Calvino Alonso

Recently, we introduced a class of molecular representations for kernel-based regression methods─the spectrum of approximated Hamiltonian matrices (SPAHM)─that takes advantage of lightweight one-electron Hamiltonians traditionally used as a self-consistent ...

2024

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Julian Thomas Blackwell, Tanja Christina Käser Jacober, Paola Mejia Domenzain, Vinitra Swamy

Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

2024

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Justin Villard

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

EPFL2023

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Paolo Pegolo

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Amer Chemical Soc2024

Optical manipulation of plasmonic nanoparticles: Applications in surface chemistry and nano-optics

Jeonghyeon Kim

Optical tweezers are devices that can manipulate nano- and microparticles using a laser. The principle of optical tweezers is to apply a force to an object using the momentum of light. This force is very small, but it is sufficient to move things in the mi ...

EPFL2023

An Overview of Heterogeneous Catalysts Based on Hypercrosslinked Polystyrene for the Synthesis and Transformation of Platform Chemicals Derived from Biomass

Lioubov Kiwi

Platform chemicals, also known as chemical building blocks, are substances that serve as starting materials for the synthesis of various value-added products, which find a wide range of applications. These chemicals are the key ingredients for many fine an ...

MDPI2023

Thermal transport of glasses via machine learning driven simulations

Federico Grasselli, Paolo Pegolo

Accessing the thermal transport properties of glasses is a major issue for the design of production strategies of glass industry, as well as for the plethora of applications and devices where glasses are employed. From the computational standpoint, the che ...

Frontiers Media Sa2024

Photogeneration of Hydrogen: Insights from a Pt(II)-Complex Incorporated into a Covalent Organic Framework

Federico De Biasi

Pt(II)-based molecular catalysts stand as a prototypical system in hydrogen evolution reactions (HER) owing to their consistently elevated activity levels. Their integration into heterogeneous systems thus provides an ideal platform to develop catalytic ma ...

Wiley-VCH Verlag2024

Frequency and time domains interactions in nanophotonics

Andrei Kiselev

In this thesis, we discuss the problems of scattering and optical manipulation related to nanosystems of different complexities. The multipolar decomposition method is used to represent scattering processes in nanosystems as a series of elementary excitati ...

EPFL2023

Computational Studies for the Design of Metalloenzyme Mimics

Guido Frisari

At present, there is no general standard automated method for engineering metalloenzymes, industrially-relevant systems able to catalyze environmentally friendly reactions. One of the most studied natural metalloenzymes is the second isoform of human carbo ...

EPFL2023

Development of Hyphenated Analytical Techniques using spICP-MS for Online Characterization of Metallic Nanoparticles in Aerosols

Tianyu Cen

The presence of metallic nanoparticles (NPs) in aerosols has raised concern about their fate in the environment through natural processes and anthropological activities. The state-of-the-art technologies for direct aerosol characterization cannot reach rea ...

EPFL2024

A Functional Extension of Semi-Structured Networks

Jan Almond Stöcker

Semi-structured networks (SSNs) merge the structures familiar from additive models with deep neural networks, allowing the modeling of interpretable partial feature effects while capturing higher-order non-linearities at the same time. A significant challe ...

Curran Associates, Inc.2024

Stabilizing Polymer Coatings Alter the Protein Corona of DNA Origami and Can Be Engineered to Bias the Cellular Uptake

Maartje Martina Cornelia Bastings, Jorieke Weiden, Hugo José Rodriguez Franco

With DNA-based nanomaterials being designed for applicationsincellular environments, the need arises to accurately understand theirsurface interactions toward biological targets. As for any materialexposed to protein-rich cell culture conditions, a protein ...

AMER CHEMICAL SOC2023

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

In Situ and Time-Resolved Transmission Electron Microscopy of Nanoscale Processes

Chengcheng Yan

Observing the fast dynamics of nanoscale systems is crucial in order to understand and ultimately control their behavior. Characterizing these dynamic processes requires techniques with atomic spatial resolution and a temporal resolution that matches the t ...

EPFL2023

Efficient and insightful descriptors for representing molecular and material space

Alexander Jan Goscinski

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

EPFL2024