Speaker
Miguel A Caro
(Aalto)
Description
Molecular dynamics (MD) simulations are a useful tool to
understand the interactions between atoms and to get insight
into the processes that take place at the nanoscale and give
rise to the observed properties of materials. "Classical"
interatomic potentials, based on i) harmonic description of
bonds, ii) partial electrostatic charges and iii)
Lennard-Jones approximations for dispersion interactions,
are computationally efficient but do not grant accurate
representation of the real underlying physics/chemistry.
They tend to fail at flexibly describing molecules in
changing environments, especially when there is bond
rearrangement, i.e., when chemical reactions take place.
Density functional theory (DFT), on the other hand, offers a
satisfactory description of interatomic interactions and can
be used to characterize bond formation and annihilation.
Unfortunately, DFT becomes prohibitively expensive when
running MD of systems beyond a few hundreds of atoms or for
time scales longer than a nanosecond. To bridge this gap
between computational efficiency and accuracy, algorithmic
developments that make use of machine learning techniques
are being adopted by the community. In this seminar, I will
briefly introduce one of such approaches, the Gaussian
approximation potential (GAP) framework [1]. Then I will go
on to discuss two applications of this approach. In the
first part of the seminar, I will present GAP simulations
of amorphous carbon depositions which allowed us to explain,
for the first time, how the "diamond-like" properties of
dense amorphous carbon arise [2]. In the second part, I will
introduce a new method that we have developed to predict
adsorption energies, with application to amorphous carbon
surfaces [3]. I will also present a new type of atomic
descriptor which allows us to improve the predictive ability
of GAP models and therefore bring them closer to full DFT
accuracy [3].
[1] A.P. Bartók, M.C. Payne, R. Kondor, G. Csányi. Phys.
Rev. Lett. 104, 136403 (2010).
[2] M.A. Caro, V.L. Deringer, J. Koskinen, T. Laurila, and
G. Csányi. Phys. Rev. Let. 120, 166101 (2018).
[3] M.A. Caro, A. Aarva, V.L. Deringer, G. Csányi, and T.
Laurila. Chem. Mater. 30, 7446 (2018).
