Experiment-Driven
Atomistic Materials Modeling: A
Case Study Combining X‑Ray Photoelectron Spectroscopy and Machine
Learning Potentials to Infer the Structure of Oxygen-Rich Amorphous
Carbon
posted on 2024-05-15, 23:45authored byTigany Zarrouk, Rina Ibragimova, Albert P. Bartók, Miguel A. Caro
An important yet
challenging aspect of atomistic materials modeling
is reconciling experimental and computational results. Conventional
approaches involve generating numerous configurations through molecular
dynamics or Monte Carlo structure optimization and selecting the one
with the closest match to experiment. However, this inefficient process
is not guaranteed to succeed. We introduce a general method to combine
atomistic machine learning (ML) with experimental observables that
produces atomistic structures compatible with experiment by
design. We use this approach in combination with grand-canonical
Monte Carlo within a modified Hamiltonian formalism, to generate configurations
that agree with experimental data and are chemically sound (low in
energy). We apply our approach to understand the atomistic structure
of oxygenated amorphous carbon (a-COx),
an intriguing carbon-based material, to answer the question of how
much oxygen can be added to carbon before it fully decomposes into
CO and CO2. Utilizing an ML-based X-ray photoelectron spectroscopy
(XPS) model trained from GW and density functional
theory (DFT) data, in conjunction with an ML interatomic potential,
we identify a-COx structures compliant
with experimental XPS predictions that are also energetically favorable
with respect to DFT. Employing a network analysis, we accurately deconvolve
the XPS spectrum into motif contributions, both revealing the inaccuracies
inherent to experimental XPS interpretation and granting us atomistic
insight into the structure of a-COx. This
method generalizes to multiple experimental observables and allows
for the elucidation of the atomistic structure of materials directly
from experimental data, thereby enabling experiment-driven materials
modeling with a degree of realism previously out of reach.