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Physics-Inspired Structural Representations for Molecules and Materials
Related publications (45)
Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets
Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...
Lausanne2024Bayes-optimal Learning of Deep Random Networks of Extensive-width
Florent Gérard Krzakala, Lenka Zdeborová, Hugo Chao Cui
We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...
2023Distributional Regression and Autoregression via Optimal Transport
We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of . Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...
EPFL2023Distribution-on-distribution regression via optimal transport maps
Victor Panaretos, Laya Ghodrati
We present a framework for performing regression when both covariate and response are probability distributions on a compact interval. Our regression model is based on the theory of optimal transportation, and links the conditional Frechet mean of the resp ...
OXFORD UNIV PRESS2022Mechanistic modelling of solar disinfection (SODIS) kinetics of Escherichia coli, enhanced with H2O2 - part 1: The dark side of peroxide
César Pulgarin, Stefanos Giannakis, Ling Feng
The present bi-partite work describes the development and validation of a mechanistic kinetic model of SODIS E. coli inactivation, enhanced with H2O2. In this first part, the mechanism of the baseline dark phenomena is modelled. A mechanistic model involvi ...
ELSEVIER SCIENCE SA2022Physics-enhanced machine learning with symmetry-adapted and long-range representations
Theoretical and computational approaches to the study of materials and molecules have, over the last few decades, progressed at an exponential rate. Yet, the possibility of producing numerical predictions that are on par with experimental measurements is t ...
EPFL2021Gaussian Process Regression for Materials and Molecules
Michele Ceriotti, David Mark Wilkins
We provide an introduction to Gaussian process regression (GPR) machinelearning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of in ...
AMER CHEMICAL SOC2021Impact of quantum-chemical metrics on the machine learning prediction of electron density
Ksenia Briling, Alberto Fabrizio
Machine learning (ML) algorithms have undergone an explosive development impacting every aspect of computational chemistry. To obtain reliable predictions, one needs to maintain a proper balance between the black-box nature of ML frameworks and the physics ...
AMER INST PHYSICS2021Multi-ion-sensing emulator and multivariate calibration optimization by machine learning models
Giovanni De Micheli, Sandro Carrara, Mandresy Ivan Ny Hanitra, Francesca Criscuolo
One paramount challenge in multi-ion-sensing arises from ion interference that degrades the accuracy of sensor calibration. Machine learning models are here proposed to optimize such multivariate calibration. However, the acquisition of big experimental da ...
2021Analysing the effects of residential mobility behaviours on the composition of personal network in Switzerland
Vincent Kaufmann, Guillaume Simon Joseph Drevon, Gil Viry
Individual life courses are marked by residential mobility often associated with family and workplace changes and therefore likely to be related to the types of personal relationships people develop and maintain. Evidence about the relationship between res ...
2021