Exploring the Photophysical Properties of Molecular Systems Using Excited State Accelerated ab Initio Molecular Dynamics.

In the present work, we employ excited state accelerated ab initio molecular dynamics (A-AIMD) to efficiently study the excited state energy landscape and photophysical topology of a variety of molecular systems. In particular, we focus on two important challenges for the modeling of excited electronic states: (i) the identification and characterization of conical intersections and crossing seams, in order to predict different and often competing radiationless decay mechanisms, and (ii) the description of the solvent effect on the absorption and emission spectra of chemical species in solution. In particular, using as examples the Schiff bases formaldimine and salicylidenaniline, we show that A-AIMD can be readily employed to explore the conformational space around crossing seams in molecular systems with very different photochemistry. Using acetone in water as an example, we demonstrate that the enhanced configurational space sampling may be used to accurately and efficiently describe both the prominent features and line-shapes of absorption and emission spectra

Coarse-Graining Auto-Encoders for Molecular Dynamics

Molecular dynamics simulations provide theoretical insight into the microscopic behavior of materials in condensed phase and, as a predictive tool, enable computational design of new compounds. However, because of the large temporal and spatial scales involved in thermodynamic and kinetic phenomena in materials, atomistic simulations are often computationally unfeasible. Coarse-graining methods allow simulating larger systems, by reducing the dimensionality of the simulation, and propagating longer timesteps, by averaging out fast motions. Coarse-graining involves two coupled learning problems; defining the mapping from an all-atom to a reduced representation, and the parametrization of a Hamiltonian over coarse-grained coordinates. Multiple statistical mechanics approaches have addressed the latter, but the former is generally a hand-tuned process based on chemical intuition. Here we present Autograin, an optimization framework based on auto-encoders to learn both tasks simultaneously. Autograin is trained to learn the optimal mapping between all-atom and reduced representation, using the reconstruction loss to facilitate the learning of coarse-grained variables. In addition, a force-matching method is applied to variationally determine the coarse-grained potential energy function. This procedure is tested on a number of model systems including single-molecule and bulk-phase periodic simulations.Comment: 8 pages, 6 figure

Computational characterization and prediction of metal-organic framework properties

In this introductory review, we give an overview of the computational chemistry methods commonly used in the field of metal-organic frameworks (MOFs), to describe or predict the structures themselves and characterize their various properties, either at the quantum chemical level or through classical molecular simulation. We discuss the methods for the prediction of crystal structures, geometrical properties and large-scale screening of hypothetical MOFs, as well as their thermal and mechanical properties. A separate section deals with the simulation of adsorption of fluids and fluid mixtures in MOFs

Analytical models and system topologies for remote multispectral data acquisition and classification

Simple analytical models are presented of the radiometric and statistical processes that are involved in multispectral data acquisition and classification. Also presented are basic system topologies which combine remote sensing with data classification. These models and topologies offer a preliminary but systematic step towards the use of computer simulations to analyze remote multispectral data acquisition and classification systems

Adsorption Mechanism and Uptake of Methane in Covalent Organic Frameworks: Theory and Experiment

We determined the methane (CH_4) uptake (at 298 K and 1 to 100 bar pressure) for a variety of covalent organic frameworks (COFs), including both two-dimensional (COF-1, COF-5, COF-6, COF-8, and COF-10) and three-dimensional (COF-102, COF-103, COF-105, and COF-108) systems. For all COFs, the CH_4 uptake was predicted from grand canonical Monte Carlo (GCMC) simulations based on force fields (FF) developed to fit accurate quantum mechanics (QM) [second order Møller−Plesset (MP2) perturbation theory using doubly polarized quadruple-ζ (QZVPP) basis sets]. This FF was validated by comparison with the equation of state for CH_4 and by comparison with the experimental uptake isotherms at 298 K (reported here for COF-5 and COF-8), which agrees well (within 2% for 1−100 bar) with the GCMC simulations. From our simulations we have been able to observe, for the first time, multilayer formation coexisting with a pore filling mechanism. The best COF in terms of total volume of CH_4 per unit volume COF absorbent is COF-1, which can store 195 v/v at 298 K and 30 bar, exceeding the U.S. Department of Energy target for CH_4 storage of 180 v/v at 298 K and 35 bar. The best COFs on a delivery amount basis (volume adsorbed from 5 to 100 bar) are COF-102 and COF-103 with values of 230 and 234 v(STP: 298 K, 1.01 bar)/v, respectively, making these promising materials for practical methane storage