Winter highlights

It has become a group tradition to prepare a greetings card for the winter holidays, evoking a COSMO publication (or pre-print!) from the previous year. A colorful way to look back at what we have been up to over the years.

2024

Can you make something useful out of non-conservative ML predictions of the atomic forces?
F. Bigi, M. Langer, M. Ceriotti, “The dark side of the forces: assessing non-conservative force models for atomistic machine learning”, arxiv:2412.11569 (2024)

2023

A high-entropy alloy nanotree, with forces computed using a ML potential
A. Mazitov, M. A. Springer, N. Lopanitsyna, G. Fraux, S. De, and M. Ceriotti, “Surface segregation in high-entropy alloys from alchemical machine learning,” J. Phys. Mater. 7(2), 025007 (2024).

2022

Using ML descriptors and regression to rationalize the contribution to binding energy from different molecular fragments
R. K. Cersonsky, M. Pakhnova, E. A. Engel, and M. Ceriotti, “A data-driven interpretation of the stability of organic molecular crystals,” Chem. Sci. 14(5), 1272–1285 (2023).

2021

An Iterative construction of atom-centered density correlations
J. Nigam, S. Pozdnyakov, and M. Ceriotti, “Recursive evaluation and iterative contraction of N -body equivariant features,” J. Chem. Phys. 153(12), 121101 (2020).

2020

Transferable models of molecular polarization combining atomic charges and atomic dipoles
M. Veit, D. M. Wilkins, Y. Yang, R. A. DiStasio, and M. Ceriotti, “Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles,” J. Chem. Phys. 153(2), 024113 (2020).

2019

A data-driven, three-dimensional periodic table of the elements
M. J. Willatt, F. Musil, and M. Ceriotti, “Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements,” Physical Chemistry Chemical Physics 20(47), 29661–29668 (2018).

2018

Difference between a reference electron density for a benzene dimer and its machine-learning prediction.
A. Grisafi, A. Fabrizio, B. Meyer, D. M. Wilkins, C. Corminboeuf, and M. Ceriotti, “Transferable Machine-Learning Model of the Electron Density,” ACS Central Science 5(1), 57–64 (2019).

2017

A snowball of exotic ices
E. A. Engel, A. Anelli, M. Ceriotti, C. J. Pickard, and R. J. Needs, “Mapping uncharted territory in ice from zeolite networks to ice structures,” Nature Communications 9(1), 2173 (2018).

2016

Snowflakes and ring-polymers
P. Gasparotto, A. A. Hassanali, and M. Ceriotti, “Probing Defects and Correlations in the Hydrogen-Bond Network of ab Initio Water,” Journal of Chemical Theory and Computation 12(4), 1953–1964 (2016).

2015

Low-dimensional visualization of chemical datasets based on structural descriptors and kernel PCA
S. De, A. P. BartĂłk, G. CsĂĄnyi, and M. Ceriotti, “Comparing molecules and solids across structural and alchemical space,” Physical Chemistry Chemical Physics 18(20), 13754–13769 (2016).

2014

Quantum delocalization of a proton along a hydrogen bond
M. Ceriotti, J. Cuny, M. Parrinello, and D. E. Manolopoulos, “Nuclear quantum effects and hydrogen bond fluctuations in water,” Proceedings of the National Academy of Sciences of the United States of America 110(39), 15591–15596 (2013).