Imperial College London
Alternate

ProfessorSerafimKalliadasis

Faculty of Engineering, Department of Chemical Engineering

Prof in Engineering Science & Applied Mathematics
 
 
 
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Contact

 

+44 (0)20 7594 1373s.kalliadasis Website

 
 
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Assistant

 

Miss Jessica Baldock +44 (0)20 7594 5699

 
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Location

 

516ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Yatsyshin:2022:10.1063/5.0071629,
author = {Yatsyshin, P and Kalliadasis, S and Duncan, AB},
doi = {10.1063/5.0071629},
journal = {Journal of Chemical Physics},
pages = {074105--1--074105--10},
title = {Physics-constrained Bayesian inference of state functions in classical density-functional theory},
url = {http://dx.doi.org/10.1063/5.0071629},
volume = {156},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We develop a novel data-driven approach to the inverse problem of classical statistical mechanics: given experimental data on the collective motion of a classical many-body system, how does one characterise the free energy landscape of that system? By combining non-parametric Bayesian inference with physically-motivated constraints, we develop an efficient learning algorithm which automates the construction of approximate free energy functionals. In contrast to optimisation-based machine learning approaches, which seek to minimise a cost function, the centralidea of the proposed Bayesian inference is to propagate a set of prior assumptions through the model, derived from physical principles. The experimental data is usedto probabilistically weigh the possible model predictions. This naturally leads to humanly interpretable algorithms with full uncertainty quantification of predictions. In our case, the output of the learning algorithm is a probability distribution over a family of free energy functionals, consistent with the observed particle data. We find that surprisingly small data samples contain sufficient information for inferring highly accurate analytic expressions of the underlying free energy functionals, making our algorithm highly data efficient. We consider excluded volume particle interactions, which are ubiquitous in nature, whilst being highly challenging for modelling in terms of free energy. To validate our approach we consider the paradigmaticcase of one-dimensional fluid and develop inference algorithms for the canonical and grand-canonical statistical-mechanical ensembles. Extensions to higher dimensional systems are conceptually straightforward, whilst standard coarse-graining techniques allow one to easily incorporate attractive interactions
AU  - Yatsyshin,P
AU  - Kalliadasis,S
AU  - Duncan,AB
DO  - 10.1063/5.0071629
EP  - 1
PY  - 2022///
SN  - 0021-9606
SP  - 074105
TI  - Physics-constrained Bayesian inference of state functions in classical density-functional theory
T2  - Journal of Chemical Physics
UR  - http://dx.doi.org/10.1063/5.0071629
UR  - https://aip.scitation.org/doi/pdf/10.1063/5.0071629
UR  - http://hdl.handle.net/10044/1/93435
VL  - 156
ER  -
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