Laboratory of Materials Design and Simulation

The Laboratory of Materials Design and Simulation (MADES) is part of the Institute of Materials within the School of Engineering at École Polytechnique Fédérale de Lausanne. The laboratory is led by Prof. Anirudh Raju Natarajan.

Overview of research in the MADES laboratory

We develop first-principles models of materials with the goal of understanding and designing novel materials and processing techniques.

news

Sep 09, 2026	Our paper introducing pyeCE, a Python implementation of the embedded cluster expansion, is on arXiv – see the Software page for the code.
Sep 04, 2026	Lorenzo Piersante’s work on machine learning interatomic potentials for solid-state precipitation is published in Physical Review Materials and selected as an Editors’ Suggestion.
Jul 27, 2026	Our collaborative work with Manos Kioupakis’s group at the University of Michigan on the thermodynamic and electronic properties of rutile Sn₁₋ₓGeₓO₂ alloys is published in Physical Review Materials.
May 28, 2026	Xin Liu’s preprint on the synthesizability, hardness, and stacking order of multicomponent transition metal carbides from machine-learned potentials is on arXiv.
Mar 25, 2026	Preprint on computing diffusion coefficients of multi-principal element alloys is on arXiv.
Jan 07, 2026	Claire Paetsch’s work on the first-principles thermodynamics of hydrogen absorption in binary C15 Laves phases is published in Chemistry of Materials.
Nov 27, 2025	Damien Lee’s paper on modelling the equilibrium vacancy concentration in multi-principal element alloys is published in Acta Materialia.
Sep 26, 2025	Damien Lee’s preprint on modelling the equilibrium vacancy concentration in multi-principal element alloys from first principles is on arXiv.
Jun 17, 2025	Our collaboration on configuring a liquid-state high-entropy metal alloy electrocatalyst appears in Small.
Mar 06, 2025	Yann Müller’s work on building multicomponent cluster expansions with machine learning and chemical embedding is published in npj Computational Materials.

selected publications

ActaMat

Modeling the Equilibrium Vacancy Concentration in Multi-Principal Element Alloys from First-Principles

Damien K.J. Lee, Yann L. Müller, and Anirudh Raju Natarajan

Acta Materialia, 2026

DOI Bib

@article{lee_2026_modelingequilibrium,
  title = {Modeling the Equilibrium Vacancy Concentration in
                    Multi-Principal Element Alloys from First-Principles},
  author = {Lee, Damien K.J. and M{\"u}ller, Yann L. and Natarajan, Anirudh Raju},
  year = {2026},
  journal = {Acta Materialia},
  volume = {304},
  pages = {121752},
  issn = {13596454},
  doi = {10.1016/j.actamat.2025.121752},
  langid = {english},
}

ActaMat

First-Principles Thermodynamics of Precipitation in Aluminum-Containing Refractory Alloys

Yann L. Müller and Anirudh Raju Natarajan

Acta Materialia, 2024

DOI Bib

@article{muller_2024_firstprinciplesthermodynamics,
  title = {First-Principles Thermodynamics of Precipitation in
                    Aluminum-Containing Refractory Alloys},
  author = {M{\"u}ller, Yann L. and Natarajan, Anirudh Raju},
  year = {2024},
  journal = {Acta Materialia},
  volume = {274},
  pages = {119995},
  issn = {13596454},
  doi = {10.1016/j.actamat.2024.119995},
  langid = {english},
}

npjCompuMats

Constructing Multicomponent Cluster Expansions with Machine-Learning and Chemical Embedding

Yann L. Müller and Anirudh Raju Natarajan

npj Computational Materials, 2025

DOI Bib

@article{muller_2025_constructingmulticomponent,
  title = {Constructing Multicomponent Cluster Expansions with
                    Machine-Learning and Chemical Embedding},
  author = {M{\"u}ller, Yann L. and Natarajan, Anirudh Raju},
  year = {2025},
  journal = {npj Computational Materials},
  volume = {11},
  number = {1},
  pages = {60},
  issn = {2057-3960},
  doi = {10.1038/s41524-025-01543-3},
  langid = {english},
}

arXiv

pyeCE: A Python Implementation of the Embedded Cluster Expansion

Yann L. Müller, Claire A. Paetsch, and Anirudh Raju Natarajan

2026

DOI Bib

@misc{muller_2026_pyecepython,
  title = {{{pyeCE}}: {{A Python Implementation}} of the {{Embedded
                    Cluster Expansion}}},
  shorttitle = {{{pyeCE}}},
  author = {M{\"u}ller, Yann L. and Paetsch, Claire A. and Natarajan, Anirudh Raju},
  year = {2026},
  publisher = {arXiv},
  doi = {10.48550/ARXIV.2609.10190},
  copyright = {Creative Commons Attribution Non Commercial No Derivatives
                    4.0 International},
  langid = {english},
  keywords = {FOS: Physical sciences,Materials Science
                    (cond-mat.mtrl-sci),Statistical Mechanics
                    (cond-mat.stat-mech)},
}

PRM

Machine Learning Interatomic Potentials for Solid-State Precipitation

Lorenzo Piersante and Anirudh Raju Natarajan

Physical Review Materials, 2026

Editors’ Suggestion DOI Bib

Editors’ Suggestion

@article{piersante_2026_machinelearning,
  title = {Machine Learning Interatomic Potentials for Solid-State
                    Precipitation},
  author = {Piersante, Lorenzo and Natarajan, Anirudh Raju},
  year = {2026},
  journal = {Physical Review Materials},
  volume = {10},
  number = {9},
  pages = {093802},
  issn = {2475-9953},
  doi = {10.1103/qyb1-7j1p},
  langid = {english},
}