Analytical gradients for molecular-orbital-based machine learning.

Molecular-orbital-based machine learning (MOB-ML) enables the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. Here, we present the derivation, implementation, and numerical demonstration of MOB-ML analytical nuclear gradients, which are formulated in a general Lagrangian framework to enforce orthogonality, localization, and Brillouin constraints on the molecular orbitals. The MOB-ML gradient framework is general with respect to the regression technique (e.

Improved accuracy and transferability of molecular-orbital-based machine learning: Organics, transition-metal complexes, non-covalent interactions, and transition states.

Molecular-orbital-based machine learning (MOB-ML) provides a general framework for the prediction of accurate correlation energies at the cost of obtaining molecular orbitals. The application of Nesbet's theorem makes it possible to recast a typical extrapolation task, training on correlation energies for small molecules and predicting correlation energies for large molecules, into an interpolation task based on the properties of orbital pairs. We demonstrate the importance of preserving physical constraints, including invariance conditions and size consistency, when generating the input for the machine learning model.

Molecular Recognition and Cocrystallization of Methylated and Halogenated Fragments of Danicalipin A by Enantiopure Alleno-Acetylenic Cage Receptors.

Enantiopure ()- and ()-configured alleno-acetylenic cage (AAC) receptors offer a highly defined interior for the complexation and structure elucidation of small molecule fragments of the stereochemically complex chlorosulfolipid danicalipin A. Solution (NMR), solid state (X-ray), and theoretical investigations of the formed host-guest complexes provide insight into the conformational preferences of 14 achiral and chiral derivatives of the danicalipin A chlorohydrin core in a confined, mostly hydrophobic environment, extending previously reported studies in polar solvents. The conserved binding mode of the guests permits deciphering the effect of functional group replacements on Gibbs binding energies Δ.

Hydrogen-Bonded Networks: Molecular Recognition of Cyclic Alcohols in Enantiopure Alleno-Acetylenic Cage Receptors.

Enantiopure (P) - and (M) -alleno-acetylenic cage (AAC) receptors form circular fourfold hydrogen-bonding networks in their closed cage conformation. Theoretical studies reveal a preferential clockwise (cw) orientation of the H-bonding array for (P) -configured and counterclockwise (ccw) for (M) -configured receptors (ΔE =-2.6 to -3.

Comprehensive Analysis of the Neglect of Diatomic Differential Overlap Approximation.

Many modern semiempirical molecular orbital models are built on the neglect of diatomic differential overlap (NDDO) approximation. An in-depth understanding of this approximation is therefore indispensable to rationalize the success of these semiempirical molecular orbital models and to develop further improvements on them. The NDDO approximation provides a recipe to approximate electron-electron repulsion integrals (ERIs) in a symmetrically orthogonalized basis based on a far smaller number of ERIs in a locally orthogonalized basis.

Calculation of Ligand Dissociation Energies in Large Transition-Metal Complexes.

The accurate calculation of ligand dissociation (or equivalently, ligand binding) energies is crucial for computational coordination chemistry. Despite its importance, obtaining accurate ab initio reference data is difficult, and density-functional methods of uncertain reliability are chosen for feasibility reasons. Here, we consider advanced coupled-cluster and multiconfigurational approaches to reinvestigate our WCCR10 set of 10 gas-phase ligand dissociation energies [ J.

Dispersion and Halogen-Bonding Interactions: Binding of the Axial Conformers of Monohalo- and (±)-trans-1,2-Dihalocyclohexanes in Enantiopure Alleno-Acetylenic Cages.

Enantiopure alleno-acetylenic cage (AAC) receptors with a resorcin[4]arene scaffold, from which four homochiral alleno-acetylenes converge to shape a cavity closed by a four-fold OH-hydrogen-bonding array, form a highly ordered porous network in the solid state. They enable the complexation and co-crystallization of otherwise non-crystalline small molecules. This paper analyzes the axial conformers of monohalo- and (±)-trans-1,2-dihalocyclohexanes, bound in the interior cavity of the AACs, on the atomic level in the solid state and in solution, accompanied by accurate calculations.

Uncertainty quantification for quantum chemical models of complex reaction networks.

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way.

Alternative Single-Solvent Electrolytes Based on Cyanoesters for Safer Lithium-Ion Batteries.

To identify alternative single-solvent-based electrolytes for application in lithium-ion batteries (LIBs), adequate computational methods were applied to screen specified physicochemical and electrochemical properties of new cyanoester-based compounds. Out of 2747 possible target compounds, two promising candidates and two structurally equivalent components were chosen. A constructive selection process including evaluation of basic physicochemical properties as well assessing the compatibility towards graphitic anodes was initiated to identify the most promising candidates.

How to estimate solid-electrolyte-interphase features when screening electrolyte materials.

Computational screening of battery electrolyte components is an extremely challenging task because very complex features like solid-electrolyte-interphase (SEI) formation and graphite exfoliation need to be taken into account at least in the final screening stage. We present estimators for both SEI formation and graphite exfoliation based on a combinatorial approach using quantum chemistry calculations on model system reactions, which can be applied automatically for a large number of compounds and thus allows for the systematic first assessment of the relevant properties using screening approaches. The thermodynamic effects are assessed using quantum mechanical calculations, while a more heuristic approach is used to estimate the kinetic effects.

Charting the known chemical space for non-aqueous lithium-air battery electrolyte solvents.

Li-air batteries are very promising candidates for powering future mobility, but finding a suitable electrolyte solvent for this technology turned out to be a major problem. We present a systematic computational investigation of the known chemical space for possible Li-air electrolyte solvents. It is shown that the problem of finding better Li-air electrolyte solvents is not only - as previously suggested - about maximizing Li(+) and O2(-) solubilities, but also about finding the optimal balance of these solubilities with the viscosity of the solvent.

Large-scale virtual high-throughput screening for the identification of new battery electrolyte solvents: computing infrastructure and collective properties.

A volunteer computing approach is presented for the purpose of screening a large number of molecular structures with respect to their suitability as new battery electrolyte solvents. Collective properties like melting, boiling and flash points are evaluated using COSMOtherm and quantitative structure-property relationship (QSPR) based methods, while electronic structure theory methods are used for the computation of electrochemical stability window estimators. Two application examples are presented: first, the results of a previous large-scale screening test (PCCP, 2014, 16, 7919) are re-evaluated with respect to the mentioned collective properties.