An Intuitively Understandable Quality Measure for Theoretical Vibrational Spectra

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

A sub-ppbv-level Acetone and Ethanol Quantum Cascade Laser Based Photoacoustic Sensor - Characterization and Multi-Component Spectra Recording in Synthetic Breath

Trace gas analysis in breath is challenging due to the vast number of different components. We present a highly sensitive quantum cascade laser based photoacoustic setup for breath analysis. Scanning the range between 8263 and 8270 nm with a spectral resolution of 48 pm, we are able to quantify acetone and ethanol within a typical breath matrix containing water and CO2. We photoacoustically acquired spectra within this region of mid-infra-red light and prove that those spectra do not suffer from non-spectral interferences. The purely additive behavior of a breath sample spectrum was verified by comparing it with the independently acquired single component spectra using Pearson and Spearman correlation coefficients. A previously presented simulation approach is improved and an error attribution study is presented. With a 3σ detection limit of 6.5 ppbv in terms of ethanol and 250 pptv regarding acetone, our system is among the best performing presented so far.

An Imbalance in the Force: The Need for Standardized Benchmarks for Molecular Simulation

Force fields (FFs) for molecular simulation have been under development for more than half a century. As with any predictive model, rigorous testing and comparisons of models critically depends on the availability of standardized data sets and benchmarks. While such benchmarks are rather common in the fields of quantum chemistry, this is not the case for empirical FFs. That is, few benchmarks are reused to evaluate FFs, and development teams rather use their own training and test sets. Here we present an overview of currently available tests and benchmarks for computational chemistry, focusing on organic compounds, including halogens and common ions, as FFs for these are the most common ones. We argue that many of the benchmark data sets from quantum chemistry can in fact be reused for evaluating FFs, but new gas phase data is still needed for compounds containing phosphorus and sulfur in different valence states. In addition, more nonequilibrium interaction energies and forces, as well as molecular properties such as electrostatic potentials around compounds, would be beneficial. For the condensed phases there is a large body of experimental data available, and tools to utilize these data in an automated fashion are under development. If FF developers, as well as researchers in artificial intelligence, would adopt a number of these data sets, it would become easier to compare the relative strengths and weaknesses of different models and to, eventually, restore the balance in the force.

Examining unsupervised ensemble learning using spectroscopy data of organic compounds

One solution to the challenge of choosing an appropriate clustering algorithm is to combine different clusterings into a single consensus clustering result, known as cluster ensemble (CE). This ensemble learning strategy can provide more robust and stable solutions across different domains and datasets. Unfortunately, not all clusterings in the ensemble contribute to the final data partition. Cluster ensemble selection (CES) aims at selecting a subset from a large library of clustering solutions to form a smaller cluster ensemble that performs as well as or better than the set of all available clustering solutions. In this paper, we investigate four CES methods for the categorization of structurally distinct organic compounds using high-dimensional IR and Raman spectroscopy data. Single quality selection (SQI) forms a subset of the ensemble by selecting the highest quality ensemble members. The Single Quality Selection (SQI) method is used with various quality indices to select subsets by including the highest quality ensemble members. The Bagging method, usually applied in supervised learning, ranks ensemble members by calculating the normalized mutual information (NMI) between ensemble members and consensus solutions generated from a randomly sampled subset of the full ensemble. The hierarchical cluster and select method (HCAS-SQI) uses the diversity matrix of ensemble members to select a diverse set of ensemble members with the highest quality. Furthermore, a combining strategy can be used to combine subsets selected using multiple quality indices (HCAS-MQI) for the refinement of clustering solutions in the ensemble. The IR + Raman hybrid ensemble library is created by merging two complementary "views" of the organic compounds. This inherently more diverse library gives the best full ensemble consensus results. Overall, the Bagging method is recommended because it provides the most robust results that are better than or comparable to the full ensemble consensus solutions.

Stacking Is Favored over Hydrogen Bonding in Azaphenanthrene Dimers

N-Doped polycyclic aromatic hydrocarbons have recently emerged as potential organic electronic materials. The function of such materials is determined not only by the intrinsic electronic properties of individual molecules but also by their supramolecular interactions in the solid state. Therefore, a proper characterization of the interactions between the individual units is of interest to materials science since they ultimately govern properties such as excitons and charge transfer. Here, we report a joint experimental and computational study of two azaphenanthrene dimers to determine the structure and the nature of supramolecular interactions in the aggregates. IR/UV double-resonance experiments were carried out using far- and mid-infrared free-electron laser radiation. The experimental spectra are compared with quantum chemical calculations for the lowest-energy π-stacked and hydrogen-bonded structures. The data reveal a preference of the π-stacked structure for the benzo[f]quinoline and the phenanthridine dimer.

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra

The high-throughput identification of unknown metabolites in biological samples remains challenging. Most current non-targeted metabolomics studies rely on mass spectrometry, followed by computational methods that rank thousands of candidate structures based on how closely their predicted mass spectra match the experimental mass spectrum of an unknown. We reasoned that the infrared (IR) spectra could be used in an analogous manner and could add orthologous structure discrimination; however, this has never been evaluated on large data sets. Here, we present results of a high-throughput computational method for predicting IR spectra of candidate compounds obtained from the PubChem database. Predicted spectra were ranked based on their similarity to gas-phase experimental IR spectra of test compounds obtained from the NIST. Our computational workflow (IRdentify) consists of a fast semiempirical quantum mechanical method for initial IR spectra prediction, ranking, and triaging, followed by a final IR spectra prediction and ranking using density functional theory. This approach resulted in the correct identification of 47% of 258 test compounds. On average, there were 2152 candidate structures evaluated for each test compound, giving a total of approximately 555,200 candidate structures evaluated. We discuss several variables that influenced the identification accuracy and then demonstrate the potential application of this approach in three areas: (1) combining IR and mass spectra rankings into a single composite rank score, (2) identifying the precursor and fragment ions using cryogenic ion vibrational spectroscopy, and (3) the incorporation of a trimethylsilyl derivatization step to extend the method compatibility to less-volatile compounds. Overall, our results suggest that matching computational with experimental IR spectra is a potentially powerful orthogonal option for adding significant high-throughput chemical structure discrimination when used with other non-targeted chemical structure identification methods.

Quantitative Comparison of Experimental and Computed IR-Spectra Extracted from Ab Initio Molecular Dynamics

Experimentally measured infrared spectra are often compared to their computed equivalents. However, the accordance is typically characterized by visual inspection, which is prone to subjective judgment. The primary challenge for a similarity-based analysis is that the artifacts introduced by each approach are very different and, therefore, may require preprocessing steps to determine and correct impeding irregularities. To allow for automated objective assessment, we propose a practical and comprehensive workflow involving scaling factors, a novel baseline correction scheme, and peak smoothing. The resulting spectra can then easily be compared quantitatively using similarity measures, for which we found the Pearson correlation coefficient to be the most suitable. The proposed procedure is then applied to compare the agreement of the experimental infrared spectra from the NIST Chemistry Web book with the calculated spectra using standard harmonic frequency analysis and spectra extracted from ab initio molecular dynamics simulations at different levels of theory. We conclude that the direct, quantitative comparison of calculated and measured IR spectra might become a novel, sophisticated approach to benchmark quantum-chemical methods. In the present benchmark, simulated spectra based on ab initio molecular dynamics show in general better agreement with the experiment than static calculations.

Comprehensive Assessment of GFN Tight-Binding and Composite Density Functional Theory Methods for Calculating Gas-Phase Infrared Spectra

Vibrational spectroscopy is a valuable and widely used analytical tool for the characterization of chemical substances. We investigate the performance of semiempirical quantum mechanical GFN tight-binding and force-field methods for the calculation of gas-phase infrared spectra in comparison to experiment and low-cost (B3LYP-3c) density functional theory. A data set of 7247 experimental references was used to evaluate method performance based on automatic spectra comparison. Various quantitative spectral similarity measures were employed for the comparison between theory and experiment and for determining empirical scaling factors. It is shown that the scaling of atomic masses provides an accurate yet simple alternative to standard global frequency scaling in density functional theory (DFT) and semiempirical calculations. Furthermore, the method performance for 58 exemplary transition metal complexes was investigated. The efficient DFT composite method B3LYP-3c, that was introduced in the course of this work, was found to be excellently suited for general IR spectra calculations. The GFN1- and GFN2-xTB tight-binding methods clearly outperformed the PMx competitors. Conformational changes were investigated for a subset of the data and are found to have a mediocre strong influence on the simulated spectra suggesting that the corresponding elaborate sampling steps may be neglected in automated compound identification workflows.

Systematic design of biomolecular force fields

Force fields for the study of biomolecules have been developed in a predominantly organic manner by regular updates over half a century. Together with better algorithms and advances in computer technology, force fields have improved to yield more robust predictions. However, there are also indications to suggest that intramolecular energy functions have not become better and that there still is room for improvement. In this review, systematic efforts toward development of novel force fields from scratch are described. This includes an estimate of the complexity of the problem and the prerequisites in the form of data and algorithms. It is suggested that in order to make progress, an effort is needed to standardize reference data and force field validation benchmarks.

A potential for molecular simulation of compounds with linear moieties

The harmonic angle bending potential is used in many force fields for (bio)molecular simulation. The force associated with this potential is discontinuous at angles close to 180°, which can lead to numeric instabilities. Angle bending of linear groups, such as alkynes or nitriles, or linear molecules, such as carbon dioxide, can be treated by a simple harmonic potential if we describe the fluctuations as a deviation from a reference position of the central atom, the position of which is determined by the flanking atoms. The force constant for the linear angle potential can be derived analytically from the corresponding force constant in the traditional potential. The new potential is tested on the properties of alkynes, nitriles, and carbon dioxide. We find that the angles of the linear groups remain about 2° closer to 180° using the new potential. The bond and angle force constants for carbon dioxide were tuned to reproduce the experimentally determined frequencies. An interesting finding was that simulations of liquid carbon dioxide under pressure with the new flexible model were stable only when explicitly modeling the long-range Lennard-Jones (LJ) interactions due to the very long-range nature of the LJ interactions (>1.7 nm). In the other tested liquids, we find that a Lennard-Jones cutoff of 1.1 nm yields similar results as the particle mesh Ewald algorithm for LJ interactions. Algorithmic factors influencing the stability of liquid simulations are discussed as well. Finally, we demonstrate that the linear angle potential can be used in free energy perturbation calculations.