Machine Learning for Novel Ternary Materials Discovery
Citation:
Rossignol, Hugo Alexandre, Machine Learning for Novel Ternary Materials Discovery, Trinity College Dublin, School of Physics, Physics, 2024Download Item:
Abstract:
First principles codes based on DFT are now sufficiently accurate and efficient that
they can be used in the design of novel materials with specifically selected properties.
Doing so requires checking whether a compound considered is chemically stable or
not, which in turn involves constructing an appropriate convex hull. While such an
approach is valid for identifying new, synthesizable materials, it demands numerous
DFT calculations to ensure reliability. This work aims at accelerating this task through
the use of machine learning interatomic potentials (MLIAPs) as screening agents. The
process is facilitated by using pre-existing data on large material repositories to form
the training sets of the models. Owing to the relative wealth in the number of binary
systems compared to the ternaries, the former provide an ideal and extensive database
for this training. In contrast, the space of ternaries, being only sparsely examined,
forms fertile ground for exploration.
In a first part of this work, an ensemble of spectral neighbour analysis potentials
(SNAPs) is trained on binary data of the Ag-Au-Cu system taken from the AFLOWlib
repository. The model is tested on different datasets composed entirely of ternary
intermetallics. It is shown that an accuracy below 30 meV/atom can be achieved for
alloys in their equilibrium structures, sufficient for an effective screening model. The
MLIAPs are however unable to perform relaxation due to their poor energy predictions
on out-of-equilibrium structures. Since suitable error metrics, capable of pinpointing
unrelaxed structures, are verified, the devised model can be used in a high-throughput
screening setting, in which candidates are physically sound compounds.
In the follow-up study, this surrogate to DFT is incorporated into a workflow aimed
at constructing reliable, DFT-level ternary convex hulls. This is achieved by two means.
Firstly, the prototypes used as candidate ternary compounds are built from the structures
of the low-enthalpy alloys of the binary subsystems. These form reasonable
guesses for equilibrium structures, owing to the close similarity between binary and
ternary alloys for transition metals. Secondly, measures are taken in order to increase
the robustness of the screening process. These notably involve undertaking partial ionic relaxation, driven by SNAP, as well as an assessment of the reliability of the predictions
made, through the use of an error metric. The final workflow developed is tested
on Ag-Au-Cu and Mo-Ta-W, and is capable of identifying novel ternary compounds,
absent from AFLOWlib, and thus produces DFT-accurate ternary convex hulls. This
is achieved by probing a large number of candidates and focusing all the heavy ab
initio calculations on the most promising candidates. In a final section, the recently
introduced M3GNet universal force-field is inserted into the workflow. This enables
higher accuracy and throughput, as exemplified by the better convex hulls obtained
and the larger number of compounds tested. It is shown how this M3GNet workflow
can be used to identify promising regions of ternary convex hulls, even for magnetic
systems.
Sponsor
Grant Number
Irish Research Council Advanced Laureate Award (IRCLA/2019/127)
Author's Homepage:
https://tcdlocalportal.tcd.ie/pls/EnterApex/f?p=800:71:0::::P71_USERNAME:ROSSIGNHDescription:
APPROVED
Author: Rossignol, Hugo Alexandre
Advisor:
Sanvito, StefanoPublisher:
Trinity College Dublin. School of Physics. Discipline of PhysicsType of material:
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