Predicting electronic structure properties of transition metal complexes with neural networks

Abstract

Our neural network predicts spin-state ordering of transition metal complexes to near-chemical accuracy with respect to DFT reference.

Quantum Machine Learning in Chemical Compound Space

“…While still recent overall, the evidence for the applicability of ML models to atomistic simulation and electronic property predictions has become overwhelming by now.A n incomplete list of studies which infer QM observables,rather than solving the conventional quantum chemistry approximations to Schrçdingerse quation (SE), [29] includes ML models of enthalpies of formation, [30,31] density functionals, [32] basis-set effects, [33] reorganization energies, [34] chemical reactivity, [35] atomization energies, [11] kinetic density functionals, [36] electronic ground-state properties, [20,37] transition state dividing surfaces, [38] polymer properties, [39] electron transmission coefficients, [40] crystal properties, [41][42][43][44] Anderson impurity models, [45,46] NMR nuclear shifts, [47] frontier orbital eigenvalues, [13] atomic charges,d ipole and quadrupole-moments, [48] electronic excitation energies, [49] Møller-Plesset theory corrections, [50] interatomic many-body expansions, [51] transition metals, [52] bonds in molecules, [53] and electron density functionals for use in ab initio molecular dynamics. [54] But also fitting the potential energy surface is awell-known problem in quantum chemistry,and has triggered substantial research on how to interpolate best with the least number of single point calculations.W agner, Schatz and Bowman introduced the modern computing perspective in the 80s, [55,56] while neural network fits to PES started to appear with Sumpter and Noid in 1992, [57] followed by Lorenz, Gross and Scheffler in 2004, [58] Manzhos and Carrington in 2006, [59] Behler and Parrinello in 2007,…”

Chemical bonding mechanism in SERS effect of pyridine by CuO nanoparticles

“…The other is the nonresonant effects (CHEM), due to the interaction between the molecule and the surface as consequence of the chemical bond (CB) established. Recent studies report the SERS effect for interaction between metallic oxides and Rhodamine 6G (R6G), as well as the study of the effects caused by the modification of CB in transition metals complexes . In this matter, copper oxide (CuO) has been used in the study of SERS in molecules such as 4‐mercaptopyridine (4‐MPy) .…”

“…Recent studies report the SERS effect for interaction between metallic oxides and Rhodamine 6G (R6G), [21] as well as the study of the effects caused by the modification of CB in transition metals complexes. [22,23] In this matter, copper oxide (CuO) has been used in the study of SERS in molecules such as 4-mercaptopyridine (4-MPy). [24,25] In these reports, the overall enhancement was studied from the mechanism CT. Additionally, the molecule-surface interaction is reported as responsible of new electronic states as a result of the chemical absorption.…”

A program for automatically predicting supramolecular aggregates and its application to urea and porphin

“…For complicated aggregate structures, evolutionary algorithms and, possibly, neural networks might be applied for further refining the aggregate candidate set. Periodic boundary conditions might be used at any growth step to check whether experimental properties of extended supramolecular systems are sufficiently well reproduced at that growth step .…”