Three-body interactions in clusters CO–(pH2)n

A neural network-based four-body potential energy surface for parahydrogen

We present an isotropic ab initio (para-H2)4 four-body interaction potential energy surface (PES). The electronic structure calculations are performed at the correlated coupled-cluster theory level, with single, double, and perturbative triple excitations. They use an atom-centered augmented correlation-consistent double zeta basis set, supplemented by a (3s3p2d) midbond function. We use a multilayer perceptron to construct the PES. We apply a rescaling transformation to the output energies during training to improve the prediction of weaker energies in the sample data. At long distances, the interaction energies are adjusted to match the empirically derived four-body dispersion interaction. The four-body interaction energy at short intermolecular separations is net repulsive. The use of this four-body PES, in combination with a first principles pair potential for para-H2 [J. Chem. Phys. 119, 12551 (2015)] and an isotropic ab initio three-body potential for para-H2 [J. Chem. Phys. 156, 044301 (2022)], is expected to provide closer agreement with experimental results.

Neural Network Potential Energy Surfaces for Small Molecules and Reactions

We review progress in neural network (NN)-based methods for the construction of interatomic potentials from discrete samples (such as ab initio energies) for applications in classical and quantum dynamics including reaction dynamics and computational spectroscopy. The main focus is on methods for building molecular potential energy surfaces (PES) in internal coordinates that explicitly include all many-body contributions, even though some of the methods we review limit the degree of coupling, due either to a desire to limit computational cost or to limited data. Explicit and direct treatment of all many-body contributions is only practical for sufficiently small molecules, which are therefore our primary focus. This includes small molecules on surfaces. We consider direct, single NN PES fitting as well as more complex methods that impose structure (such as a multibody representation) on the PES function, either through the architecture of one NN or by using multiple NNs. We show how NNs are effective in building representations with low-dimensional functions including dimensionality reduction. We consider NN-based approaches to build PESs in the sums-of-product form important for quantum dynamics, ways to treat symmetry, and issues related to sampling data distributions and the relation between PES errors and errors in observables. We highlight combinations of NNs with other ideas such as permutationally invariant polynomials or sums of environment-dependent atomic contributions, which have recently emerged as powerful tools for building highly accurate PESs for relatively large molecular and reactive systems.

Comparative density functional theory and density functional tight binding study of arginine and arginine-rich cell penetrating peptide TAT adsorption on anatase TiO2

A comparative DFT-DFTB study of geometries and electronic structures of arginine, arginine dipeptide, and arginine-rich cell penetrating peptide TAT on the surface of TiO₂.

Microwave spectroscopy of the seeded binary and ternary clusters CO-(pH2)2, CO-pH2-He, CO-HD, and CO-(oD2)(N=1,2)

We report the Fourier transform microwave spectra of the a-type J = 1-0 transitions of the binary and ternary CO-(pH2)2, CO-pH2-He, CO-HD, and CO-(oD2)N=1,2 clusters. In addition to the normal isotopologue of CO for all clusters, we observed the transitions of the minor isotopologues, (13)C(16)O, (12)C(18)O, and (13)C(18)O, for CO-(pH2)2 and CO-pH2-He. All transitions lie within 335 MHz of the experimentally or theoretically predicted values. In comparison to previously reported infrared spectra [Moroni et al., J. Chem. Phys. 122, 094314 (2005)], we are able to tentatively determine the vibrational shift for CO-pH2-He, in addition to its b-type J = 1-0 transition frequency. The a-type frequency of CO-pH2-He is similar to that of CO-He2 [Surin et al., Phys. Rev. Lett. 101, 233401 (2008)], suggesting that the pH2 molecule has a strong localizing effect on the He density. Perturbation theory analysis of CO-oD2 reveals that it is approximately T-shaped, with an anisotropy of the intermolecular potential amounting to ∼9 cm(-1).

Representing potential energy surfaces by high-dimensional neural network potentials

The development of interatomic potentials employing artificial neural networks has seen tremendous progress in recent years. While until recently the applicability of neural network potentials (NNPs) has been restricted to low-dimensional systems, this limitation has now been overcome and high-dimensional NNPs can be used in large-scale molecular dynamics simulations of thousands of atoms. NNPs are constructed by adjusting a set of parameters using data from electronic structure calculations, and in many cases energies and forces can be obtained with very high accuracy. Therefore, NNP-based simulation results are often very close to those gained by a direct application of first-principles methods. In this review, the basic methodology of high-dimensional NNPs will be presented with a special focus on the scope and the remaining limitations of this approach. The development of NNPs requires substantial computational effort as typically thousands of reference calculations are required. Still, if the problem to be studied involves very large systems or long simulation times this overhead is regained quickly. Further, the method is still limited to systems containing about three or four chemical elements due to the rapidly increasing complexity of the configuration space, although many atoms of each species can be present. Due to the ability of NNPs to describe even extremely complex atomic configurations with excellent accuracy irrespective of the nature of the atomic interactions, they represent a general and therefore widely applicable technique, e.g. for addressing problems in materials science, for investigating properties of interfaces, and for studying solvation processes.