217 research outputs found
Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus Ab Initio Methods
Machine learning (ML) is increasingly becoming a helpful tool in the search
for novel functional compounds. Here we use classification via random forests
to predict the stability of half-Heusler (HH) compounds, using only
experimentally reported compounds as a training set. Cross-validation yields an
excellent agreement between the fraction of compounds classified as stable and
the actual fraction of truly stable compounds in the ICSD. The ML model is then
employed to screen 71,178 different 1:1:1 compositions, yielding 481 likely
stable candidates. The predicted stability of HH compounds from three previous
high throughput ab initio studies is critically analyzed from the perspective
of the alternative ML approach. The incomplete consistency among the three
separate ab initio studies and between them and the ML predictions suggests
that additional factors beyond those considered by ab initio phase stability
calculations might be determinant to the stability of the compounds. Such
factors can include configurational entropies and quasiharmonic contributions.Comment: 11 pages, 5 figures, 2 table
Effect of local chemistry and structure on thermal transport in doped GaAs
Using a first-principles approach, we analyze the impact of \textit{DX}
centers formed by S, Se, and Te dopant atoms on the thermal conductivity of
GaAs. Our results are in good agreement with experiments and unveil the physics
behind the drastically different effect of each kind of defect. We establish a
causal chain linking the electronic structure of the dopants to the thermal
conductivity of the bulk solid, a macroscopic transport coefficient.
Specifically, the presence of lone pairs leads to the formation of structurally
asymmetric \textit{DX} centers that cause resonant scattering of incident
phonons. The effect of such resonances is magnified when they affect the part
of the spectrum most relevant for thermal transport. We show that these
resonances are associated with localized vibrational modes in the perturbed
phonon spectrum. Finally, we illustrate the connection between flat adjacent
minima in the energy landscape and resonant phonon scattering through detailed
analyses of the energy landscape of the defective structures.Comment: 7 pages, 7 figure
Neural-Network Force Field Backed Nested Sampling: Study of the Silicon p-T Phase Diagram
Nested sampling is a promising method for calculating phase diagrams of
materials, however, the computational cost limits its applicability if
ab-initio accuracy is required. In the present work, we report on the efficient
use of a neural-network force field in conjunction with the nested-sampling
algorithm. We train our force fields on a recently reported database of silicon
structures and demonstrate our approach on the low-pressure region of the
silicon pressure-temperature phase diagram between 0 and \SI{16}{GPa}. The
simulated phase diagram shows a good agreement with experimental results,
closely reproducing the melting line. Furthermore, all of the experimentally
stable structures within the investigated pressure range are also observed in
our simulations. We point out the importance of the choice of
exchange-correlation functional for the training data and show how the meta-GGA
r2SCAN plays a pivotal role in achieving accurate thermodynamic behaviour using
nested-sampling. We furthermore perform a detailed analysis of the exploration
of the potential energy surface and highlight the critical role of a diverse
training data set
Machine learning boosted a b i n i t i o study of the thermal conductivity of Janus PtSTe van der Waals heterostructures
Calculating the thermal conductivity of heterostructures with multiple layers presents a significant challenge for state-of-the-art ab initio methods. In this study we introduce an efficient neural-network force field (NNFF) to explore the thermal transport characteristics of van der Waals heterostructures based on PtSTe, using both the phonon Boltzmann transport equation and molecular dynamics (MD) simulations. Besides demonstrating a remarkable level of agreement with both theoretical and experimental data, our predictions reveal that heterogeneous combinations like PtSTe − PtTe 2 display a notable reduction in thermal conductivity at room temperature, primarily due to broken out-of-plane symmetries and the presence of weak van der Waals interactions. Furthermore, our study highlights the superiority of MD simulations with NNFFs in capturing higher-order anharmonic phonon properties. This is demonstrated through the analysis of the temperature-dependent thermal conductivity curves of PtSTe-based van der Waals heterostructures and advances our understanding of phonon transport in those materials
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