Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Abstract

such as "ab initio" molecular dynamics (AIMD). ML-based interatomic potentials, therefore, are beginning to be applied to a range of challenging materials-science research questions, such as the modeling of phase-change memory materials, [12][13][14] catalysts, [15] or battery materials. [16] Recently, a number of "general-purpose" ML potentials have been reported, which can accurately describe a broad range of atomic configurations and materials properties-including silicon, [17] carbon, [18] aluminum, [19,20] and the binary Ga-As system. [21] The hope for such potentials is to enable "off-the-shelf" use without further modification: for example, the aforementioned silicon ML potential has been used to study complex structural transitions under pressure [22] or unusual mechanical properties of amorphous silicon (a-Si). [23] The starting point for the present study is a general-purpose Gaussian approximation potential (GAP) ML model for bulk and nanostructured phosphorus-which was shown to be flexible enough to be applicable to the pressure-induced liquidliquid phase transition from the molecular fluid to the network liquid, whilst also accurately describing the crystalline allotropes and the layered structure of phosphorene. [24] This GAP is now set to facilitate even more challenging studies on more extended length or time scales, and the exploration of other structurally complex phases for which it has not been explicitly "trained", such as amorphous phosphorus (a-P).Research interest in a-P has grown because of emerging applications in batteries. [25][26][27][28] As a commercially available anode material, red phosphorus provides a large cation-storage ability with high theoretical capacities by forming binary X-P compounds (X = Li, Na, K), but it suffers from low conductivity and a large volumetric change during cycling. [29] As discussed in ref. [30], these issues can be ameliorated by creating composites of a-P and carbonaceous materials: on the one hand, increasing electronic conductivity; [31,32] on the other hand, minimizing the mechanical stress induced by volume changes. [30,33] The structure and properties of phosphorus itself are clearly important for battery applications: for example, experimental work showed that the size of a-P particles in phosphoruscarbon composite anodes has an effect on the electrochemical performance, [34] and in situ transmission electron microscopy (TEM) revealed images of red phosphorus segments within a carbon nanofiber. [35] Computationally, structurally ordered phases have been studied with density-functional theory (DFT; Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a-P at the atomistic level remains a challenge. Here, it is shown that large-scale molecular-dynamics simulations, enabled by a machine-learning (ML)-based interatomic potential for phosphorus, can give new insights into the atomic structure of a-P and how...

Green and Facile Fabrication of Metal Oxide/Red Phosphorus Composite Catalysts for CO2Photoreduction

“…For the proof-of-principle simulation here, we do not attempt to provide a detailed description of the RP molecular structure but to select a representative building unit for the modeling of localized interphase interactions. Recently, a five-membered pentagonal ring is found to be the dominant building unit of amorphous RP instead of the frequently reported P8–P9 clusters . This ring is thus chosen as the simplified model to represent RP (Figure A).…”

“…Proof-of-principle simulation of interphase binding between TiO 2 and fresh or oxidized RP. (A) Five-membered pentagonal ring representing RP . The fresh and oxidized P atoms are denoted as P 0 and P θ , respectively.…”

“…We have performed proof-of-principle density functional theory (DFT) calculations using the Gaussian 16 A.03 computational package . TiO 2 nanocrystals were simulated using Ti n O 2 n clusters ( n = 1–3), and RP was modeled using a characteristic pentagonal ring of five phosphorus atoms . Oxidized P x O y species were simplified and represented by the phosphorus atom attached with one dangling oxygen.…”

“…31 TiO 2 nanocrystals were simulated using Ti n O 2n clusters (n = 1−3), 32 and RP was modeled using a characteristic pentagonal ring of five phosphorus atoms. 33 Oxidized P x O y species were simplified and represented by the phosphorus atom attached with one dangling oxygen. Eight conformations with intact cluster structures were optimized at the B3LYP-D3/6-31G(d, p) level of theory 34 from a total of 12 combinations (three Ti n O 2n clusters, two phosphorus rings, and two options of binding atoms, Ti or O).…”

Modelling amorphous materials via a joint solid-state NMR and X-ray absorption spectroscopy and DFT approach: application to alumina

“…Recent progress in applying machine learning to this problem has enabled models of amorphous single element structures such as silicon, carbon, and phosphorus 4,[11][12][13] , as well as progress in some two-element systems including HfO2 and LixSi 3,14 . In these methods, machine learning is applied to describe the atomic level interactions using training sets from first principles energy and force calculations; machine learned models therefore have comparable radial distribution factors, bond-lengths, and structure factors to first principles models with improved simulation time and length scales 3 .…”