Improving the IR spectra alignment algorithm with spectra deconvolution and combination with Raman or VCD spectroscopy

Quantum chemical package Jaguar: A survey of recent developments and unique features

This paper is dedicated to the quantum chemical package Jaguar, which is commercial software developed and distributed by Schrödinger, Inc. We discuss Jaguar's scientific features that are relevant to chemical research as well as describe those aspects of the program that are pertinent to the user interface, the organization of the computer code, and its maintenance and testing. Among the scientific topics that feature prominently in this paper are the quantum chemical methods grounded in the pseudospectral approach. A number of multistep workflows dependent on Jaguar are covered: prediction of protonation equilibria in aqueous solutions (particularly calculations of tautomeric stability and pKa), reactivity predictions based on automated transition state search, assembly of Boltzmann-averaged spectra such as vibrational and electronic circular dichroism, as well as nuclear magnetic resonance. Discussed also are quantum chemical calculations that are oriented toward materials science applications, in particular, prediction of properties of optoelectronic materials and organic semiconductors, and molecular catalyst design. The topic of treatment of conformations inevitably comes up in real world research projects and is considered as part of all the workflows mentioned above. In addition, we examine the role of machine learning methods in quantum chemical calculations performed by Jaguar, from auxiliary functions that return the approximate calculation runtime in a user interface, to prediction of actual molecular properties. The current work is second in a series of reviews of Jaguar, the first having been published more than ten years ago. Thus, this paper serves as a rare milestone on the path that is being traversed by Jaguar's development in more than thirty years of its existence.

Experimental and Theoretical Studies on the Reactions of Aliphatic Imines with Isocyanates

In the context of a project aiming at the replacement of the 3-substituted β-lactam ring in classical β-lactam antibiotics by an N(3)-acyl-1,3-diazetidinone moiety, we have investigated the reaction of isocyanates with imines derived from allyl glycinate and differently substituted propionaldehydes. Imines of aromatic aldehydes with anilines have been reported to react with acyl isocyanates to give 1,3-diazetidinones or 2,3-dihydro-4H-1,3,5-oxadiazin-4-ones, via [2+2] or [4+2] cycloaddition, respectively. However, neither of these products was formed with imines derived from allyl glycinate and 2-(mono)methyl propionaldehydes. α,α-Dimethylation of the imine enabled the [4+2] cycloaddition pathway, but the desired 1,3-diazetidinone products were not observed. Surprisingly, the imines obtained from thioesters of 2,2-dimethyl 3-oxo propionic acid reacted with aryl isocyanates or with benzyl isocyanate to give 5,5-dimethyl-2,4-dioxo-6-(aryl-/alkylthio)tetrahydropyrimidines, via thiol displacement and re-addition to a putative six-membered iminium intermediate. These experimental results obtained for the reactions could be rationalized by DFT calculations. In addition, we have shown that N(3)-acyl-1,3-diazetidinone and 2,3-dihydro-4H-1,3,5-oxadiazin-4-one products can be distinguished based on experimental IR data in combination with theoretical reference spectra employing the IR spectra alignment (IRSA) algorithm. This discrimination was not possible by means of 1 H, 13 C, or 15 N NMR spectroscopy.

Catalytic Structure Design by AI Generating with Spectroscopic Descriptors

Generative artificial intelligence has depicted a beautiful blueprint for on-demand design in chemical research. However, the few successful chemical generations have only been able to implement a few special property values because most chemical descriptors are mathematically discrete or discontinuously adjustable. Herein, we use spectroscopic descriptors with machine learning to establish a quantitative spectral structure-property relationship for adsorbed molecules on metal monatomic catalysts. Besides catalytic properties such as adsorption energy and charge transfer, the complete spatial relative coordinates of the adsorbed molecule were successfully inverted. The spectroscopic descriptors and prediction models are generalized, allowing them to be transferred to several different systems. Due to the continuous tunability of the spectroscopic descriptors, the design of catalytic structures with continuous adsorption states generated by AI in the catalytic process has been achieved. This work paves the way for using spectroscopy to enable real-time monitoring of the catalytic process and continuous customization of catalytic performance, which will lead to profound changes in catalytic research.