Helical Organic and Inorganic Polymers

Despite being a staple of synthetic plastics and biomolecules, helical polymers are scarcely studied with Gaussian-basis-set {\it ab initio} electron-correlated methods on an equal footing with molecules. This article introduces an {\it ab initio} second-order many-body Green's-function [MBGF(2)] method with nondiagonal, frequency-dependent Dyson self-energy for infinite helical polymers using screw-axis-symmetry-adapted Gaussian-spherical-harmonics basis functions. Together with the Gaussian-basis-set density-functional theory for energies, analytical atomic forces, translational-period force, and helical-angle force, it can compute correlated energy, quasiparticle energy bands, structures, and vibrational frequencies of an infinite helical polymer, which smoothly converge at the corresponding oligomer results. These methods can handle incommensurable structures, which have an infinite translational period and are hard to characterize by any other method, just as efficiently as commensurable structures. We apply these methods to polyethylene ($2/1$ helix), polyacetylene (Peierls' system), and polytetrafluoroethylene ($13/6$ helix) to establish the quantitative accuracy of MBGF(2)/cc-pVDZ in simulating their (angle-resolved) ultraviolet photoelectron spectra, and of B3LYP/cc-pVDZ or 6-31G** in reproducing their structures, infrared and Raman band positions, phonon dispersions, and (coherent and incoherent) inelastic neutron scattering spectra. We then predict the same properties for infinitely catenated chains of nitrogen or oxygen and discuss their possible metastable existence under ambient conditions. They include planar zigzag polyazene (N$_2$)$_x$ (Peierls' system), $11/3$-helical isotactic polyazane (NH)$_x$, $9/4$-helical isotactic polyfluoroazane (NF)$_x$, and $7/2$-helical polyoxane (O)$_x$ as potential high-energy-density materials

Ewald methods for polarizable surfaces with application to hydroxylation and hydrogen bonding on the (012) and (001) surfaces of alpha-Fe2O3

We present a clear and rigorous derivation of the Ewald-like method for calculation of the electrostatic energy of the systems infinitely periodic in two-dimensions and of finite size in the third dimension (slabs) which is significantly faster than existing methods. Molecular dynamics simulations using the transferable/polarizable model by Rustad et al. were applied to study the surface relaxation of the nonhydroxylated, hydroxylated, and solvated surfaces of alpha-Fe2O3 (hematite). We find that our nonhydroxylated structures and energies are in good agreement with previous LDA calculations on alpha-alumina by Manassidis et al. [Surf. Sci. Lett. 285, L517, 1993]. Using the results of molecular dynamics simulations of solvated interfaces, we define end-member hydroxylated-hydrated states for the surfaces which are used in energy minimization calculations. We find that hydration has a small effect on the surface structure, but that hydroxylation has a significant effect. Our calculations, both for gas-phase and solution-phase adsorption, predict a greater amount of hydroxylation for the (012) surface than for the (001) surface. Our simulations also indicate the presence of four-fold coordinated iron ions on the (001) surface.Comment: 23 pages, REVTeX (LaTeX), 8 figures not included, e-mail to [email protected], paper accepted in Surface Scienc

The Melting Temperature of Liquid Water with the Effective Fragment Potential

The direct simulation of the solid–liquid water interface with the effective fragment potential (EFP) via the constant enthalpy and pressure (NPH) ensemble was used to estimate the melting temperature (Tm) of ice-Ih. Initial configurations and velocities, taken from equilibrated constant pressure and temperature (NPT) simulations at P = 1 atm and T = 305 K, 325 K and 399 K, respectively, yielded corresponding Tm values of 378 ± 16 K, 382 ± 14 K and 384 ± 15 K. These estimates are consistently higher than experiment, albeit to the same degree as previously reported estimates using density functional theory (DFT)-based Born–Oppenheimer simulations with the Becke-Lee–Yang–Parr functional plus dispersion corrections (BLYP-D)

Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry

Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitively expensive, and motivate the development of machine-learning models that make the same predictions more efficiently. Training graph neural networks over large molecular databases introduces unique computational challenges such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs such as social networks. This paper demonstrates a novel hardware-software co-design approach to scale up the training of graph neural networks for molecular property prediction. We introduce an algorithm to coalesce the batches of molecular graphs into fixed size packs to eliminate redundant computation and memory associated with alternative padding techniques and improve throughput via minimizing communication. We demonstrate the effectiveness of our co-design approach by providing an implementation of a well-established molecular property prediction model on the Graphcore Intelligence Processing Units (IPU). We evaluate the training performance on multiple molecular graph databases with varying degrees of graph counts, sizes and sparsity. We demonstrate that such a co-design approach can reduce the training time of such molecular property prediction models from days to less than two hours, opening new possibilities for AI-driven scientific discovery

Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators

The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task. While such approaches have become the norm in fields such as natural language processing, implementation and evaluation of transfer learning approaches for chemistry are in the early stages. In this work, we demonstrate finetuning for downstream tasks on a graph neural network (GNN) trained over a molecular database containing 2.7 million water clusters. The use of Graphcore IPUs as an AI accelerator for training molecular GNNs reduces training time from a reported 2.7 days on 0.5M clusters to 1.2 hours on 2.7M clusters. Finetuning the pretrained model for downstream tasks of molecular dynamics and transfer to a different potential energy surface took only 8.3 hours and 28 minutes, respectively, on a single GPU.Comment: Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS

Anomalously Strong Effect of the Ion Sign on the Thermochemistry of Hydrogen Bonded Aqueous Clusters of Identical Chemical Composition

The sign preference of hydrogen bonded aqueous ionic clusters X±(H2O)i (n =1–5, X = F; Cl; Br) has been investigated using the Density Functional Theory and ab initio MP2 method. The present study indicates the anomalously large difference in formation free energies between cations and anions of identical chemical composition. The effect of vibrational anharmonicity on stepwise Gibbs free energy changes has been investigated, and possible uncertainties associated with the harmonic treatment of vibrational spectra have been discussed