SearcharxivSearch

arXiv subjects

Angel Albavera-Mata

Publications and source records attributed to Angel Albavera-Mata.

3 recordsLinked to original sources

How Accurately Can We Describe Spin Crossover?

The complicated physicochemical properties of metal complexes that exhibit thermal spin crossover make it difficult for routine electronic structure calculations to yield an accurate transition temperature prediction, $T_{1/2}$. The difficulty lies in the intricate connection between the spin-crossover energy, which is a molecular spectroscopic property, and $T_{1/2}$, a condensed phase property. Here we show how to obtain spin-crossover energies systematically by reverse engineering of experimental $T_{1/2}$ data. The protocol is based upon fitting the range separation parameter, $ω$, in the hybrid LC-$ω$PBE density functional to reproduce the experimental $T_{1/2}$ values for a series of metal complexes. We provide insights into the sources of variations of at least $\pm 15$ kJ mol$^{-1}$ found from common exchange and correlation functionals by comparing their performance against our reference data. By analysis of the sensitivity of transition temperatures to $\pm 1$ \% shifts in the range separation parameter, we determined a typical uncertainty of $\pm 50$ K for them, and a $\pm 2$ kJ mol$^{-1}$ uncertainty in the extracted spin-crossover energies due to $\pm 1$ \% variations of $T_{1/2}$. Lastly, we present results from the high-level, all-electron coupled cluster method for eight of the smaller molecules in the reference data set, and discuss the influence of the truncation of the excitation series upon the spin state energies.

cond-mat.mtrl-sci

Discovery of Spin-Crossover Candidates with Equivariant Graph Neural Networks and Relevance-Based Classification

Swift discovery of spin-crossover materials for their potential application in quantum information devices requires techniques which enable efficient identification of suitably bistable candidates. To this end, we screened the Cambridge Structural Database to develop a specialized database of 1,439 materials and computed spin-switching energies from density functional theory for each material. The database was used to train an equivariant graph convolutional neural network to predict the magnitude of the spin-conversion energy. A test mean absolute error was 360 meV. For candidate identification, we equipped the system with a relevance-based classifier. This approach leads to a nearly four-fold improvement in identifying potential spin-crossover systems of interest as compared to conventional high-throughput screening.

cond-mat.dis-nn

Some Problems in Density Functional Theory

Though calculations based on density functional theory (DFT) are used remarkably widely in chemistry, physics, materials science, and biomolecular research and though the modern form of DFT has been studied for almost 60 years, some mathematical problems remain. For context, we provide an outline of the basic structure of DFT, then pose several questions regarding both its time-independent and time-dependent forms. Progress on any of these would aid in development of better approximate functionals and in interpretation.

physics.chem-ph