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Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys

Publications associées (53)

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The present work proposes an extension to the approach of [Xi, C; et al. J. Chem. Theory Comput. 2022, 18, 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approa ...

Amer Chemical Soc2024

Modeling the mechanics and rheology of porous and granular media: an elastoplastic continuum approach

Lars Kristoffer Uhlen Blatny

The complex mechanics of porous and granular media play a significant role in various industrial processes and natural phenomena. As an example, understanding the mechanics of how failure occurs, localizes and propagates in porous brittle solids under vari ...

EPFL2023

2023 Roadmap on molecular modelling of electrochemical energy materials

Alfredo Pasquarello, Chunmin Zhang, Assil Bouzid

New materials for electrochemical energy storage and conversion are the key to the electrification and sustainable development of our modern societies. Molecular modelling based on the principles of quantum mechanics and statistical mechanics as well as em ...

Bristol2023

We study the spontaneous out-of-plane bending of a planar untwisted ribbon composed of nematic polymer networks activated by a change in temperature. Our theory accounts for both stretching and bending energies, which compete to establish equilibrium. We s ...

SPRINGER2023

A complete description of thermodynamic stabilities of molecular crystals

Venkat Kapil, Edgar Albert Engel

Predictions of relative stabilities of (competing) molecular crystals are of great technological relevance, most notably for the pharmaceutical industry. However, they present a long-standing challenge for modeling, as often minuscule free energy differenc ...

NATL ACAD SCIENCES2022

Structure-preserving approaches and data-driven closure modeling for model order reduction

Nicolò Ripamonti

In this thesis, we propose model order reduction techniques for high-dimensional PDEs that preserve structures of the original problems and develop a closure modeling framework leveraging the Mori-Zwanzig formalism and recurrent neural networks. Since high ...

EPFL2022

Control-oriented modeling and analysis of tubular dielectric elastomer actuators dedicated to cardiac assist devices

Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Armando Matthieu Walter, Ning Liu

This paper deals with the control-oriented modeling of a multilayered dielectric elastomer actuator based tube. The actuator is clamped at both sides and performs a radial displacement. The hyperelastic deformation and the viscoelastic performance, togethe ...

2022

Atomistic simulations are a bottom up approach that predict properties of materials by modelling the quantum mechanical behaviour of all electrons and nuclei present in a system. These simulations, however, routinely assume nuclei to be classical particles ...

EPFL2020

Inverse Modelling and Predictive Inference in Continuum Mechanics: a Data-Driven Approach

Claire Marianne Charlotte Capelo

The explosive growth of machine learning in the age of data has led to a new probabilistic and data-driven approach to solving very different types of problems. In this paper we study the feasibility of using such data-driven algorithms to solve classic ph ...

2020

Charge fluctuations from molecular simulations in the constant-potential ensemble

Sara Bonella, Alessandro Coretti

We revisit the statistical mechanics of charge fluctuations in capacitors. In constant-potential classical molecular simulations, the atomic charges of electrode atoms are treated as additional degrees of freedom which evolve in time so as to satisfy the c ...

2020