Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userModeling molecular crystals formed by spin-active metal complexes by atom-atom potentials
147
0
0.0
(
0
)
Added by
Andrei Tchougr\\'eeff DSc
Publication date
2009
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
We apply the atom-atom potentials to molecular crystals of iron (II) complexes with bulky organic ligands. The crystals under study are formed by low-spin or high-spin molecules of Fe(phen)$_{2}$(NCS)$_{2}$ (phen = 1,10-phenanthroline), Fe(btz)$_{2}$(NCS)$_{2}$ (btz = 5,5$^{prime }$,6,6$^{prime}$-tetrahydro-4textit{H},4$^{prime}$textit{H}-2,2$^{prime }$-bi-1,3-thiazine), and Fe(bpz)$_{2}$(bipy) (bpz = dihydrobis(1-pyrazolil)borate, and bipy = 2,2$^{prime}$-bipyridine). All molecular geometries are taken from the X-ray experimental data and assumed to be frozen. The unit cell dimensions and angles, positions of the centers of masses of molecules, and the orientations of molecules corresponding to the minimum energy at 1 atm and 1 GPa are calculated. The optimized crystal structures are in a good agreement with the experimental data. Sources of the residual discrepancies between the calculated and experimental structures are discussed. The intermolecular contributions to the enthalpy of the spin transitions are found to be comparable with its total experimental values. It demonstrates that the method of atom-atom potentials is very useful for modeling organometalic crystals undergoing the spin transitions.
rate research
Read More
Within the framework of Kohn-Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacobs ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric-isothermal ensemble. In order to generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on ab initio data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water towards the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional.
We propose a simple, but efficient and accurate machine learning (ML) model for developing high-dimensional potential energy surface. This so-called embedded atom neural network (EANN) approach is inspired by the well-known empirical embedded atom method (EAM) model used in condensed phase. It simply replaces the scalar embedded atom density in EAM with a Gaussian-type orbital based density vector, and represents the complex relationship between the embedded density vector and atomic energy by neural networks. We demonstrate that the EANN approach is equally accurate as several established ML models in representing both big molecular and extended periodic systems, yet with much fewer parameters and configurations. It is highly efficient as it implicitly contains the three-body information without an explicit sum of the conventional costly angular descriptors. With high accuracy and efficiency, EANN potentials can vastly accelerate molecular dynamics and spectroscopic simulations in complex systems at ab initio level.
The applications of machine learning techniques to chemistry and materials science become more numerous by the day. The main challenge is to devise representations of atomic systems that are at the same time complete and concise, so as to reduce the number of reference calculations that are needed to predict the properties of different types of materials reliably. This has led to a proliferation of alternative ways to convert an atomic structure into an input for a machine-learning model. We introduce an abstract definition of chemical environments that is based on a smoothed atomic density, using a bra-ket notation to emphasize basis set independence and to highlight the connections with some popular choices of representations for describing atomic systems. The correlations between the spatial distribution of atoms and their chemical identities are computed as inner products between these feature kets, which can be given an explicit representation in terms of the expansion of the atom density on orthogonal basis functions, that is equivalent to the smooth overlap of atomic positions (SOAP) power spectrum, but also in real space, corresponding to $n$-body correlations of the atom density. This formalism lays the foundations for a more systematic tuning of the behavior of the representations, by introducing operators that represent the correlations between structure, composition, and the target properties. It provides a unifying picture of recent developments in the field and indicates a way forward towards more effective and computationally affordable machine-learning schemes for molecules and materials.
We predict the enhanced light harvesting of a protein-pigment complex when assembled to a quantum dot (QD) antenna. Our prototypical nanoassembly setup is composed of a Fenna-Mattews-Olson system hosting 8 Bacteriochlorophyll (BChl) a dyes, and a near-infrared emitting CdSe$_x$Te$_{(1-x)}$/ZnS alloy-core/shell nanocrystal. BChl a has two wide windows of poor absorption in the green and orange-red bands, precisely where most of the sunlight energy lies. The selected QD is able to collect sunlight efficiently in a broader band and funnel its energy by a (non-radiative) Forster resonance energy transfer mechanism to the dyes embedded in the protein. By virtue of the coupling between the QD and the dyes, the nanoassembly absorption is dramatically improved in the poor absorption window of the BChl a.
We report on improvements extending the capabilities of the atom-by-atom assembler described in [Barredo et al., Science 354, 1021 (2016)] that we use to create fully-loaded target arrays of more than 100 single atoms in optical tweezers, starting from randomly-loaded, half-filled initial arrays. We describe four variants of the sorting algorithm that (i) allow decrease the number of moves needed for assembly and (ii) enable the assembly of arbitrary, non-regular target arrays. We finally demonstrate experimentally the performance of this enhanced assembler for a variety of target arrays.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
