Polymorphism of 3‐(5‐phenyl‐1,3,4‐oxadiazol‐2‐yl)‐ and 3‐[5‐(pyridin‐4‐yl)‐1,3,4‐oxadiazol‐2‐yl]‐2H‐chromen‐2‐ones

This study of 3‐(5‐phenyl‐1,3,4‐oxadiazol‐2‐yl)‐2H‐chromen‐2‐one, C₁₇H₁₀N₂O₃, 1 , and 3‐[5‐(pyridin‐4‐yl)‐1,3,4‐oxadiazol‐2‐yl]‐2H‐chromen‐2‐one, C₁₆H₉N₃O₃, 2 , was performed on the assumption of the potential anticancer activity of the compounds. Three polymorphic structures for 1 and two polymorphic structures for 2 have been studied thoroughly. The strongest intermolecular interaction is stacking of the `head‐to‐head' type in all the studied crystals. The polymorphic structures of 1 differ with respect to the intermolecular interactions between stacked columns. Two of the polymorphs have a columnar or double columnar type of crystal organization, while the third polymorphic structure can be classified as columnar‐layered. The difference between the two structures of 2 is less pronounced. Both crystals can be considered as having very similar arrangements of neighbouring columns. The formation of polymorphic modifications is caused by a subtle balance of very weak intermolecular interactions and packing differences can be identified only using an analysis based on a study of the pairwise interaction energies.     

Choosing the right molecular machine learning potential

Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential''s main features, and judge what they could expect from each one.

This article provides a lifeline for those lost in the sea of the molecular machine learning potentials by providing a balanced overview and evaluation of popular potentials.     

Synthesis of substituted 3‐(5‐amino‐[1,3,4]thiadiazol‐2‐yl)‐2H‐pyrano[2,3‐c]pyridin‐2‐ones

An efficient two‐step synthesis of novel 3‐(5‐amino‐[1,3,4]thiadiazol‐2‐yl)‐2H‐pyrano[2,3‐c]pyridine‐2‐ones was developed. In the first step, a new 2H‐pyrano[2,3‐c]pyridine‐3‐carboxamide 5 was prepared by Knoevenagel condensation of pyridoxal hydrochloride with cyanoacetamide. In the second step, the reaction of carboxamide 5 with a series of N⁴‐substituted thiosemicarbazides yielded a library of 35 dis crete compounds 8 {1–35} in high yields. The intermolecular recyclization mechanism leading to these products is discussed.     

Facile access to a series of large polycondensed pyridazines and their utility for the supramolecular synthesis of coordination polymers

Domino cyclization of ketoenols and hydrazine leads to a series of polycondensed pyridazines, which reveal potential as rigid N-donor multidentate ligands for supramolecular synthesis of open coordination polymers.     

Asymptotic behavior of the expected optimal value of the multidimensional assignment problem

The Multidimensional Assignment Problem (MAP) is a higher-dimensional version of the Linear Assignment Problem that arises in the areas of data association, target tracking, resource allocation, etc. This paper elucidates the question of asymptotical behavior of the expected optimal value of the large-scale MAP whose assignment costs are independent identically distributed random variables with a prescribed probability distribution. We demonstrate that for a broad class of continuous distributions the limiting value of the expected optimal cost of the MAP is determined by the location of the left endpoint of the support set of the distribution, and construct asymptotical bounds for the expected optimal cost.     

Semiempirical UNO-CAS and UNO-CI: method and applications in nanoelectronics

Unrestricted Natural Orbital-Complete Active Space Configuration Interaction, abbreviated as UNO-CAS, has been implemented for NDDO-based semiempirical molecular-orbital (MO) theory. A computationally more economic technique, UNO-CIS, in which we use a configuration interaction (CI) calculation with only single excitations (CIS) to calculate excited states, has also been implemented and tested. The class of techniques in which unrestricted natural orbitals (UNOs) are used as the reference for CI calculations is denoted UNO-CI. Semiempirical UNO-CI gives good results for the optical band gaps of organic semiconductors such as polyynes and polyacenes, which are promising materials for nanoelectronics. The results of these semiempirical UNO-CI techniques are generally in better agreement with experiment than those obtained with the corresponding conventional semiempirical CI methods and comparable to or better than those obtained with far more computationally expensive methods such as time-dependent density-functional theory. We also show that symmetry breaking in semiempirical UHF calculations is very useful for predicting the diradical character of organic compounds in the singlet spin state.     

The Impact of Aggregation on the Photophysics of Spiro‐Bridged Heterotriangulenes

We report on the impact of the central heteroatom on structural, electronic, and spectroscopic properties of a series of spirofluorene‐bridged heterotriangulenes and provide a detailed study on their aggregates. The in‐depth analysis of their molecular structure by NMR spectroscopy and X‐ray crystallography was further complemented by density functional theory calculations. With the aid of extensive photophysical analysis the complex fluorescence spectra were deconvoluted showing contributions from the peripheral fluorenes and the heteroaromatic cores. Beyond the molecular scale, we examined the aggregation behavior of these heterotriangulenes in THF/H₂O mixtures and analyzed the aggregates by static and dynamic light scattering. The excited‐state interactions within the aggregates were found to be similar to those found in the solid state. A plethora of morphologies and superstructures were observed by scanning electron microscopy of drop‐casted dispersions.     

On optimality of a polynomial algorithm for random linear multidimensional assignment problem

We demonstrate that the linear multidimensional assignment problem with iid random costs is polynomially e{\varepsilon} -approximable almost surely (a.s.) via a simple greedy heuristic, for a broad range of probability distributions of the assignment costs. Specifically, conditions on discrete and continuous distributions of the cost coefficients, including distributions with unbounded support, have been established that guarantee convergence to unity in the a.s. sense of the cost ratio between the greedy solution and optimal solution. The corresponding convergence rates have been determined.