BSE49, a diverse, high-quality benchmark dataset of separation energies of chemical bonds

Research on correlation between DNA typing and trace characteristics of blood after thermal exposure

The identification of biological evidence is particularly important for criminal detection, and the deoxyribonucleic acid (DNA) analysis plays a significant role in reconstructing events. However, bloodstains after thermal exposure in fires are quite unique compared to those in other scenes. Previous results regarding DNA recovery in bloodstains after heating are inconsistent with each other, which limits guidance for forensic science. In order to confirm the influence of heat on DNA recovery, the important physical evidence, bloodstain, was selected for its common occurrence, and a standard heat source, the Cone Calorimeter, was used to simulate high temperatures in fire scenes. A series of bloodstains were prepared under different heating conditions, and the results of short tandem repeat (STR) typing were systematically correlated with the trace characteristics of bloodstains. The findings indicate that heating bloodstains below 150 °C for less than 10 mins has minimal effect on DNA testing. After heating bloodstains at 150 °C for 20 mins or at 180 °C for 5 mins, partial DNA profiles were obtained, accompanied by blackening and cracking of the bloodstains. After exposure to 180 °C for 20 mins or 200 °C for 10 mins, no DNA profiles were obtained with bloodstains exhibiting metallic lusters and black bulges. Furthermore, from the perspective of chemical bond energy, the C-N, C-O, C-C, and P-O bonds in DNA molecules are prone to breaking during heating. The C-N bond serves as the primary connection between the four bases and the strand, while the C-O, C-C, and P-O bonds are significant connections within the DNA strand. It is thus hypothesized that the breakage of any bond aforementioned during heating results in the failure of DNA typing. Understanding the correlation between trace characteristics of bloodstains and DNA typing results after thermal exposure is crucial for comprehending DNA recovery from physical evidence collected from fire scenes.

The influence of substituents in governing the strength of the P-X bonds of substituted halophosphines R1R2P-X (X = F and Cl)

In this study, the gas-phase homolytic P-F and P-Cl bond dissociation energies (BDEs) of a set of thirty fluorophosphine (R1R2P-F) and thirty chlorophosphine-type (R1R2P-Cl) molecules have been obtained using the high-level W2 thermochemical protocol. For the R1R2P-F species, the P-F BDEs (at 298 K) differ by up to 117.0 kJ mol-1, with (H3Si)2P-F having the lowest BDE (439.5 kJ mol-1) and F2P-F having the largest BDE (556.5 kJ mol-1). In the case of the chlorophosphine-type molecules, the difference in BDEs is considerably smaller (i.e., 72.6 kJ mol-1), with (NC)2P-Cl having the lowest P-Cl BDE (299.8 kJ mol-1) and (HO)2P-Cl having the largest (372.4 kJ mol-1). We have further analyzed the effect of substituents in governing the P-F and P-Cl BDEs by considering the effect of substituents in the parent halogenated precursors (using molecule stabilization enthalpies) and the effect of substituents in the product radicals (using radical stabilization enthalpies). Finally, we have also assessed the performance of a wide range of DFT methods for their ability to compute the gas-phase P-F and P-Cl BDEs contained in this dataset. We find that, overall, the double hybrid functional DSD-PBEB95 offers the best performance for both bond types, with mean absolute deviations of just 2.1 (P-F BDEs) and 2.2 (P-Cl BDEs) kJ mol-1.

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.

Small-Basis Set Density-Functional Theory Methods Corrected with Atom-Centered Potentials

Density functional theory (DFT) is currently the most popular method for modeling noncovalent interactions and thermochemistry. The accurate calculation of noncovalent interaction energies, reaction energies, and barrier heights requires choosing an appropriate functional and, typically, a relatively large basis set. Deficiencies of the density-functional approximation and the use of a limited basis set are the leading sources of error in the calculation of noncovalent and thermochemical properties in molecular systems. In this article, we present three new DFT methods based on the BLYP, M06-2X, and CAM-B3LYP functionals in combination with the 6-31G* basis set and corrected with atom-centered potentials (ACPs). ACPs are one-electron potentials that have the same form as effective-core potentials, except they do not replace any electrons. The ACPs developed in this work are used to generate energy corrections to the underlying DFT/basis-set method such that the errors in predicted chemical properties are minimized while maintaining the low computational cost of the parent methods. ACPs were developed for the elements H, B, C, N, O, F, Si, P, S, and Cl. The ACP parameters were determined using an extensive training set of 118655 data points, mostly of complete basis set coupled-cluster level quality. The target molecular properties for the ACP-corrected methods include noncovalent interaction energies, molecular conformational energies, reaction energies, barrier heights, and bond separation energies. The ACPs were tested first on the training set and then on a validation set of 42567 additional data points. We show that the ACP-corrected methods can predict the target molecular properties with accuracy close to complete basis set wavefunction theory methods, but at a computational cost of double-ζ DFT methods. This makes the new BLYP/6-31G*-ACP, M06-2X/6-31G*-ACP, and CAM-B3LYP/6-31G*-ACP methods uniquely suited to the calculation of noncovalent, thermochemical, and kinetic properties in large molecular systems.

Improving Density Functional Prediction of Molecular Thermochemical Properties with a Machine-Learning-Corrected Generalized Gradient Approximation

The past decade has seen an increasing interest in designing sophisticated density functional approximations (DFAs) by integrating the power of machine learning (ML) techniques. However, application of the ML-based DFAs is often confined to simple model systems. In this work, we construct an ML correction to the widely used Perdew-Burke-Ernzerhof (PBE) functional by establishing a semilocal mapping from the electron density and reduced gradient to the exchange-correlation energy density. The resulting ML-corrected PBE is immediately applicable to any real molecule and yields significantly improved heats of formation while preserving the accuracy for other thermochemical and kinetic properties. This work highlights the prospect of combining the power of data-driven ML methods with physics-inspired derivations for reaching the heaven of chemical accuracy.

BH9, a New Comprehensive Benchmark Data Set for Barrier Heights and Reaction Energies: Assessment of Density Functional Approximations and Basis Set Incompleteness Potentials

The calculation of accurate reaction energies and barrier heights is essential in computational studies of reaction mechanisms and thermochemistry. To assess methods regarding their ability to predict these two properties, high-quality benchmark sets are required that comprise a reasonably large and diverse set of organic reactions. Due to the time-consuming nature of both locating transition states and computing accurate reference energies for reactions involving large molecules, previous benchmark sets have been limited in scope, the number of reactions considered, and the size of the reactant and product molecules. Recent advances in coupled-cluster theory, in particular local correlation methods like DLPNO-CCSD(T), now allow the calculation of reaction energies and barrier heights for relatively large systems. In this work, we present a comprehensive and diverse benchmark set of barrier heights and reaction energies based on DLPNO-CCSD(T)/CBS called BH9. BH9 comprises 449 chemical reactions belonging to nine types common in organic chemistry and biochemistry. We examine the accuracy of DLPNO-CCSD(T) vis-a-vis canonical CCSD(T) for a subset of BH9 and conclude that, although there is a penalty in using the DLPNO approximation, the reference data are accurate enough to serve as a benchmark for density functional theory (DFT) methods. We then present two applications of the BH9 set. First, we examine the performance of several density functional approximations commonly used in thermochemical and mechanistic studies. Second, we assess our basis set incompleteness potentials regarding their ability to mitigate basis set incompleteness errors. The number of data points, the diversity of the reactions considered, and the relatively large size of the reactant molecules make BH9 the most comprehensive thermochemical benchmark set to date and a useful tool for the development and assessment of computational methods.