On machine learning force fields for metallic nanoparticles

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

We introduce two new approximation methods for the numerical evaluation of the long-range component of the range-separated Coulomb potential and the approximation of the resulting high dimensional Two-Electron Integrals tensor (TEI) with long-range interac ...

On machine learning force fields for metallic nanoparticles

Chattez avec Graph Search

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

Programming Nonlinear Computation with Nonlinear and Linear Optics

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

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

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

Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

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

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

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

Frequency and time domains interactions in nanophotonics

Computational Studies for the Design of Metalloenzyme Mimics

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

A Functional Extension of Semi-Structured Networks

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

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

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

Efficient and insightful descriptors for representing molecular and material space

Factorized structure of the long-range two-electron integrals tensor and its application in quantum chemistry