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A complete description of thermodynamic stabilities of molecular crystals

Publications associées (40)

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

Anirudh Raju Natarajan

The free energy plays a fundamental role in theories of phase transformations and microstructure evolution. It encodes the thermodynamic coupling between different fields, such as mechanics and chemistry, within continuum descriptions of non-equilibrium ma ...

Elsevier2020

Nuclear Quantum Effects: Fast and Accurate

Venkat Kapil

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

The search of novel materials using in-silico high-throughput screening is emerging as a fundamental step in the pipeline of materials discovery, but its low yields in terms of synthesisable structures limit its effectiveness. In order to isolate configura ...

EPFL2020

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

In recent work [Coretti et al., J. Chem. Phys., 2018, 149, 191102], a new algorithm to solve numerically the dynamics of the shell model for polarization was presented. The approach, broadly applicable to systems involving adiabatically separated dynamical ...

2020

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Electroencephalogram microstates are recurrent scalp potential configurations that remain stable for around 90 ms. The dynamics of two of the four canonical classes of microstates, commonly labeled as C and D, have been suggested as a potential endophenoty ...

2020

Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

Anirudh Raju Natarajan

The free energy of a system is central to many material models. Although free energy data is not generally found directly, its derivatives can be observed or calculated. In this work, we present an Integrable Deep Neural Network (IDNN) that can be trained ...

Elsevier2019

Predicting homogeneous nucleation rate from atomistic simulations

Bingqing Cheng

Predictive modelling and quantitative understanding of nucleation is essential for predicting phase transformation processes in nature and precisely controlling material synthesis and processing. Atomistic modeling is a powerful tool for capturing the dyna ...

EPFL2019

Assessment of Approximate Methods for Anharmonic Free Energies

Michele Ceriotti, Venkat Kapil, Edgar Albert Engel, Mariana Rossi Carvalho

Quantitative evaluation of the thermodynamic properties of materials—most notably their stability, as measured by the free energy—must take into account the role of thermal and zero-point energy fluctuations. While these effects can easily be estimated wit ...

2019

Quantifying high-order interdependencies via multivariate extensions of the mutual information

Michael Christoph Gastpar, Fernando Rosas de Andraca

This paper introduces a model-agnostic approach to study statistical synergy, a form of emergence in which patterns at large scales are not traceable from lower scales. Our framework leverages various multivariate extensions of Shannon's mutual information ...

2019