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