Exhumed sections of the middle and lower crust in western New Zealand reveal how deformation was partitioned within a thermally and rheologically evolving crustal column during Cretaceous continental extension. Structural data, P‐T determinations, and U‐Pb geochronology from central Fiordland and the Paparoa Range in Westland show that extension initiated in the lower crust by ∼114 Ma as a period of arc‐related magmatism waned. Initially, deformation was localized into areas that were weakened by heat and magma. However, these hot, weak zones were ephemeral. During the period 114–111 Ma, lower crustal fabrics record a rapid progression from magmatic flow to high‐temperature deformation at the garnet‐granulite facies (T > 700°C, P = 12 kbar) to cooler deformation at the upper amphibolite facies (T = 550–650°C, P = 7–9 kbar). Lower crustal cooling and compositional contrasts between mafic granulites and hydrous metasedimentary material resulted in a middle crust that was weak relative to the lower crust. Between circa 111 and circa 90 Ma, focused subhorizontal flow and vertical thinning in a weak middle crust led to the collapse of the upper crust and the unroofing of midcrustal material. During this period, arrays of conjugate‐style shear zones transferred displacements vertically and horizontally through the crust, resulting in a structural style that resembles crustal‐scale boudinage. The New Zealand example of continental extension shows that a weak middle crust and a relatively cool, highly viscous lower crust can result in a localized style of extension, including the formation of metamorphic core complexes that exhume the middle crust but not the lower crust.

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We have calculated state-averaged complete-active-space self-consistent-field (SA-CASSCF), multiconfiguration pair-density functional theory (MC-PDFT), hybrid MC-PDFT (HMC-PDFT), and n-electron valence state second-order perturbation theory (NEVPT2) excitation energies with the approximate pair coefficient (APC) automated active-space selection scheme for the QUESTDB benchmark database of 542 vertical excitation energies. We eliminated poor active spaces (20–40% of calculations) by applying a threshold to the SA-CASSCF absolute error. With the remaining calculations, we find that NEVPT2 performance is significantly impacted by the size of the basis set the wave functions are converged in, regardless of the quality of their description, which is a problem absent in MC-PDFT. Additionally, we find that HMC-PDFT is a significant improvement over MC-PDFT with the translated PBE (tPBE) density functional and that it performs about as well as NEVPT2 and second-order coupled cluster on a set of 373 excitations in the QUESTDB database. We optimized the percentage of SA-CASSCF energy to include in HMC-PDFT when using the tPBE on-top functional, and we find the 25% value used in tPBE0 to be optimal. This work is by far the largest benchmarking of MC-PDFT and HMC-PDFT to date, and the data produced in this work are useful as a validation of HMC-PDFT and of the APC active-space selection scheme. We have made all the wave functions produced in this work (orbitals and CI vectors) available to the public and encourage the community to utilize this data as a tool in the development of further multireference model chemistries.

The developments of the open-source chemistry software environment since spring 2020 are described, with a focus on novel functionalities accessible in the stable branch of the package or via interfaces with other packages. These developments span a wide range of topics in computational chemistry and are presented in thematic sections: electronic structure theory, electronic spectroscopy simulations, analytic gradients and molecular structure optimizations, ab initio molecular dynamics, and other new features. This report offers an overview of the chemical phenomena and processes can address, while showing that is an attractive platform for state-of-the-art atomistic computer simulations.

Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn−Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structure and chemical bond of such systems. In the complete-active-space self-consistent field (CASSCF) method one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black-box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a "black-box" mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.