Publication

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Chat with Graph Search

Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [82Rb] PET for MACE prediction

On the Generalization of Stochastic Gradient Descent with Momentum

Reliable data-driven decision-making through optimal transport

Dynamic Behavior of Spatially Confined Sn Clusters and Its Application in Highly Efficient Sodium Storage with High Initial Coulombic Efficiency

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Machine Learning for Screening Small Molecules as Passivation Materials for Enhanced Perovskite Solar Cells

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

Magneto-Thermoelectric Transport in Graphene Quantum Dot with Strong Correlations

Towards Trustworthy Deep Learning for Image Reconstruction

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

Realization of Organocerium-Based Fullerene Molecular Materials Showing Mott Insulator-Type Behavior