The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic structures preserve symmetry. In this study, we leverage novel probabilistic (DP) models denoise atomic coordinates rather than adopting the standard approach in CDVAE. Our proposed DP-CDVAE can reconstruct and whose qualities are statistically comparable those of original Furthermore, notably, when comparing carbon generated by with relaxed obtained from...

Abstract The structural stability and internal properties of hybrid organic–inorganic perovskites (HOIPs) have been widely investigated over the past few years. interplay between organic cations inorganic framework is one prominent features. Herein we report evolution Raman modes under pressure in perovskite MAPbI $$_3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>3</mml:mn> </mml:msub> </mml:math> by combining experimental approach with...

Determining whether a candidate crystalline material is thermodynamically stable depends on identifying its true ground-state structure, central challenge in computational materials science. We introduce CrystalGRW, diffusion-based generative model Riemannian manifolds that proposes novel crystal configurations and can predict phases validated by density functional theory. The properties, such as fractional coordinates, atomic types, lattice matrices, are represented suitable manifolds,...

The existence of single-layer diamond or diamane, which could adopt the properties its bulk counterpart, has been verified by previous calculations and experiments. Even though, carbon atoms on top bottom surfaces need to form dangling bonds with and/or molecules stabilize their ${\mathrm{sp}}^{3}$ hybridization. In this paper, diamane is substituted N assisting diamondlike structure be without any passivation studied ab initio calculation. One-fourth substitution vertically stacking as...

Accurately predicting the elastic properties of crystalline solids is vital for computational materials science. However, traditional atomistic-scale ab initio approaches are computationally intensive, especially studying complex with a large number atoms in unit cell. We introduce data-driven approach to efficiently predict crystal structures using SE(3)-equivariant graph neural networks (GNNs). This yields important scalar moduli an accuracy comparable that recent studies. Importantly, our...

High-pressure phase stabilities up to 600 K and the related properties of Na₂O₂under pressures 300 GPa were investigated using first-principles calculations quasi-harmonic approximation.

Abstract The representation of atomic configurations through cluster correlations, along with the expansion approach, has long been used to predict formation energies and determine thermodynamic stability alloys. In this work, a comparison is conducted between traditional method based on density functional theory other potential machine learning models, including decision tree‐based ensembles multi‐layer perceptron regression, explore alloying behavior different elements in multi‐component...

We used density functional theory to calculate the phase stability of hcp (hexagonal close packed) and fcc (face centered cubic) structures ScH3. The form is stable up 22 GPa according generalized gradient approximation calculation. Then becomes energetically more stable. In order provide insight into transition mechanism, we modeled by sliding basal planes, i.e. (001)h in such a way that ABABAB sequence altered ABCABC form. This was suggested experiment. configurations these steps are our...

A microscopic viewpoint of structure and dipolar configurations in hybrid organic-inorganic perovskites is crucial to understanding their stability phase transitions. The necessity incorporating dispersion interactions the state-of-the-art density functional theory for [Formula: see text] perovskite (MAPI) demonstrated this work. Some vdW methods were selected evaluate corresponding energetics properties cubic MAPI with various azimuthally rotated MA organic cation orientations. highest...

Abstract We investigated the thermodynamic stability of Mo 2 (Ti 1− x V )C MXenes (MTVCs), using first-principles calculation based on density functional theory and cluster-expansion formalism. Our results suggest that atoms prefer to substitute in outer metal layers MTVCs, such occupying behavior is a crucial factor determines MTVCs. On other hand, Ti residing inner layer them are predicted weakly interact with each their atomic configuration has minimal impact The transition temperatures...

Accurately predicting the elastic properties of crystalline solids is vital for computational materials science. However, traditional atomistic scale ab initio approaches are computationally intensive, especially studying complex with a large number atoms in unit cell. We introduce novel data-driven approach to efficiently predict crystal structures using SE(3)-equivariant graph neural networks (GNNs). This yields important scalar moduli accuracy comparable recent studies. Importantly, our...

The crystal diffusion variational autoencoder (CDVAE) is a machine learning model that leverages score matching to generate realistic structures preserve symmetry. In this study, we leverage novel probabilistic (DP) models denoise atomic coordinates rather than adopting the standard approach in CDVAE. Our proposed DP-CDVAE can reconstruct and whose qualities are statistically comparable those of original Furthermore, notably, when comparing carbon generated by with relaxed obtained from...

X-ray diffraction, Raman spectroscopy, and electrical resistivity measurements on polycrystalline WTe2-xSex(0 ⩽ x 0.8) reveal aTd-1T'structural phase transition suppression of magnetoresistance atx = 0.2. These phenomena are consistent with the pressure diagram WTe2. However, chemical due to substitution smaller Se ion cannot generate required for transition. Strain induced by sample inhomogeneity is believed be a trigger behaviors. In agreement previous predictions reports, mixed of1T'and...