arXiv subjects

Peder Lyngby

Publications and source records attributed to Peder Lyngby.

4 recordsLinked to original sources

arXiv2024

Bayesian optimization of atomic structures with prior probabilities from universal interatomic potentials

The optimization of atomic structures plays a pivotal role in understanding and designing materials with desired properties. However, conventional computational methods often struggle with the formidable task of navigating the vast potential energy surface, especially in high-dimensional spaces with numerous local minima. Recent advancements in machine learning-driven surrogate models offer a promising avenue for alleviating this computational burden. In this study, we propose a novel approach that combines the strengths of universal machine learning potentials with a Bayesian approach using Gaussian processes. By using the machine learning potentials as priors for the Gaussian process, the Gaussian process has to learn only the difference between the machine learning potential and the target energy surface calculated for example by density functional theory. This turns out to improve the speed by which the global optimal structure is identified across diverse systems for a well-behaved machine learning potential. The approach is tested on periodic bulk materials, surface structures, and a cluster.

cond-mat.mtrl-sci↗

arXiv2024

Two-dimensional materials as ideal substrates for molecular quantum emitters

The generation and manipulation of non-classical states of light is central to quantum technologies. Color centers in insulators have been extensively studied for single-photon generation, but organic molecules immobilized on substrates have gained attention due to their superior scalability, large oscillator strengths, and tunable emission frequency. Here, we use first-principles calculations to investigate the photoemission from organic molecules adsorbed on 2D materials. Focusing on terrylene on hexagonal boron nitride (hBN), we obtain zero phonon line (ZPL) energies and emission lineshapes in excellent agreement with experiments. Notably, antisite defects in hBN can immobilize the molecule without influencing its key emission features. We further show that the main effect of the 2D substrate is to introduce sharp sidebands near the ZPL as a fingerprint of hindered rotational, translational, or bending modes of the molecule. Our findings provide insight into how substrate interactions shape the optical properties of molecular systems for quantum applications.

cond-mat.mtrl-sci↗

arXiv2024

Ab initio property characterisation of thousands of previously unknown 2D materials

We perform extensive density functional theory (DFT) calculations to determine the stability and elementary properties of 4249 previously unexplored monolayer crystals. The monolayers comprise the most stable subset (energy within 0.1 eV/atom of the convex hull) of a larger portfolio of two-dimensional (2D) materials recently discovered using a deep generative model and systematic lattice decoration schemes. The relaxed 2D structures are run through the basic property workflow of the Computational 2D Materials Database (C2DB) to evaluate the dynamical stability and obtain the stiffness tensor, piezoelectric tensor, deformation potentials, Born and Bader charges, electronic band structure, effective masses, plasma frequency, Fermi surface, projected density of states, magnetic moments, magnetic exchange couplings, magnetic anisotropy, topological indices, optical- and infrared polarisability. We provide statistical overviews of the property data and highlight a few specific examples of interesting materials. Our work exposes previously unknown parts of the 2D chemical space and provides a basis for the discovery of 2D materials with specific properties. All data is available in the C2DB.

cond-mat.mtrl-sci↗

arXiv2022

Data-driven discovery of novel 2D materials by deep generative models

Efficient algorithms to generate candidate crystal structures with good stability properties can play a key role in data-driven materials discovery. Here we show that a crystal diffusion variational autoencoder (CDVAE) is capable of generating two-dimensional (2D) materials of high chemical and structural diversity and formation energies mirroring the training structures. Specifically, we train the CDVAE on 2615 2D materials with energy above the convex hull $ΔH_{\mathrm{hull}}< 0.3$ eV/atom, and generate 5003 materials that we relax using density functional theory (DFT). We also generate 14192 new crystals by systematic element substitution of the training structures. We find that the generative model and lattice decoration approach are complementary and yield materials with similar stability properties but very different crystal structures and chemical compositions. In total we find 11630 predicted new 2D materials, where 8599 of these have $ΔH_{\mathrm{hull}}< 0.3$ eV/atom as the seed structures, while 2004 are within 50 meV of the convex hull and could potentially be synthesized. The relaxed atomic structures of all the materials are available in the open Computational 2D Materials Database (C2DB). Our work establishes the CDVAE as an efficient and reliable crystal generation machine, and significantly expands the space of 2D materials.

cond-mat.mtrl-sci↗