24 research outputs found
Complexity of Many-Body Interactions in Transition Metals via Machine-Learned Force Fields from the TM23 Data Set
This work examines challenges associated with the accuracy of machine-learned
force fields (MLFFs) for bulk solid and liquid phases of d-block elements. In
exhaustive detail, we contrast the performance of force, energy, and stress
predictions across the transition metals for two leading MLFF models: a
kernel-based atomic cluster expansion method implemented using sparse Gaussian
processes (FLARE), and an equivariant message-passing neural network (NequIP).
Early transition metals present higher relative errors and are more difficult
to learn relative to late platinum- and coinage-group elements, and this trend
persists across model architectures. Trends in complexity of interatomic
interactions for different metals are revealed via comparison of the
performance of representations with different many-body order and angular
resolution. Using arguments based on perturbation theory on the occupied and
unoccupied d states near the Fermi level, we determine that the large, sharp d
density of states both above and below the Fermi level in early transition
metals leads to a more complex, harder-to-learn potential energy surface for
these metals. Increasing the fictitious electronic temperature (smearing)
modifies the angular sensitivity of forces and makes the early transition metal
forces easier to learn. This work illustrates challenges in capturing intricate
properties of metallic bonding with current leading MLFFs and provides a
reference data set for transition metals, aimed at benchmarking the accuracy
and improving the development of emerging machine-learned approximations.Comment: main text: 21 pages, 9 figures, 2 tables. supplementary information:
57 pages, 83 figures, 20 table
Oxygen Vacancy Formation Energy in Metal Oxides: High Throughput Computational Studies and Machine Learning Predictions
The oxygen vacancy formation energy () governs defect dynamics
and is a useful metric to perform materials selection for a variety of
applications. However, density functional theory (DFT) calculations of come at a greater computational cost than the typical bulk calculations
available in materials databases due to the involvement of multiple
vacancy-containing supercells. As a result, available repositories of direct
calculations of remain relatively scarce, and the development
of machine learning models capable of delivering accurate predictions is of
interest. In the present, work we address both such points. We first report the
results of new high-throughput DFT calculations of oxygen vacancy formation
energies of the different unique oxygen sites in over 1000 different oxide
materials, which together form the largest dataset of directly computed oxygen
vacancy formation energies to date, to our knowledge. We then utilize the
resulting dataset of 2500 values to train random forest
models with different sets of features, examining both novel features
introduced in this work and ones previously employed in the literature. We
demonstrate the benefits of including features that contain information
specific to the vacancy site and account for both cation identity and oxidation
state, and achieve a mean absolute error upon prediction of 0.3 eV/O,
which is comparable to the accuracy observed upon comparison of DFT
computations of oxygen vacancy formation energy and experimental results.
Finally, we demonstrate the predictive power of the developed models in the
search for new compounds for solar-thermochemical water-splitting applications,
finding over 250 new AABBO double perovskite
candidates
Decoding reactive structures in dilute alloy catalysts
Rational catalyst design is crucial toward achieving more energy-efficient and sustainable catalytic processes. Understanding and modeling catalytic reaction pathways and kinetics require atomic level knowledge of the active sites. These structures often change dynamically during reactions and are difficult to decipher. A prototypical example is the hydrogen-deuterium exchange reaction catalyzed by dilute Pd-in-Au alloy nanoparticles. From a combination of catalytic activity measurements, machine learning-enabled spectroscopic analysis, and first-principles based kinetic modeling, we demonstrate that the active species are surface Pd ensembles containing only a few (from 1 to 3) Pd atoms. These species simultaneously explain the observed X-ray spectra and equate the experimental and theoretical values of the apparent activation energy. Remarkably, we find that the catalytic activity can be tuned on demand by controlling the size of the Pd ensembles through catalyst pretreatment. Our data-driven multimodal approach enables decoding of reactive structures in complex and dynamic alloy catalysts