Study of Positron Impact Scattering from Methane and Silane Using an Analytically Obtained Static Potential with Correlation Polarization

A detailed study of positron impact elastic scattering from methane and silane is carried out using a model potential consisting of static and polarization potentials. The static potential for the molecular target is obtained analytically by using accurate Gaussian molecular wavefunctions. The molecular orbitals are expressed as a linear combination of Gaussian atomic orbitals. Along with the analytically obtained static potential, a correlation polarization potential is also added to construct the model potential. Utilizing the model potential, the Schrödinger equation is solved using the partial wave phase shift analysis method, and the scattering amplitude is obtained in terms of the phase shifts. Thereafter, the differential, integrated and total cross sections are calculated. These cross-section results are compared with the previously reported measurements and theoretical calculations.

Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

Rationalising molecular crystal structures using Hirshfeld surfaces

Hirshfeld surface analysis [1] has very quickly become a routine tool for rationalising and visualising intermolecular interactions in crystals. The serendipitous discovery of an intriguing and novel way to identify the space `belonging' to a molecule in a crystal has led to the development of a suite of computational tools that facilitate a deeper understanding of how molecules pack in crystals and why it makes sense that a particular crystal packing occurs [2]. We have previously used the Hirshfeld surface as a vehicle for mapping inherent shape and curvature, surface-mediated distances between closest atoms, as well as quantum mechanical properties such as molecular orbital density, electron density and electrostatic potential. Combining visualisation tools like these with quantum mechanical wavefunctions – and hence properties derived from these wavefunctions – offers a powerful and unique opportunity to investigate intuitive concepts like `electrostatic complementarity' [3]. With this in mind we have been investigating ways to subdivide Hirshfeld surfaces into discrete patches that can be identified with specific pairs of molecules in close contact in crystals, and testing different expressions to quantify our ideas on electrostatic complementarity. Coupled with this appealing visual approach we also compute the electrostatic energy of interaction between the respective molecular wavefunctions. This combination of approaches within an easy to use software package will be powerful enough to not only routinely explore and visualise the patterns of interaction exhibited by molecules in crystals, but also provide meaningful energies of interaction between relevant pairs of molecules. In this way we can readily attach some real significance – energetics – to what are more usually classified as close contacts of various kinds.