High throughput virtual screening of 230 billion molecular solar heat battery candidates

Abstract

The dihydroazulene/vinylheptafulvene (DHA/VHF) thermocouple is a promising candidate for thermal heat batteries that absorb and store solar energy as chemical energy without the need for insulation. However, in order to be viable the energy storage capacity and lifetime of the high energy form (i.e., the free energy barrier to the back reaction) of the canonical parent compound must be increased significantly to be of practical use. We use semiempirical quantum chemical methods, machine learning, and density functional theory to virtually screen over 230 billion substituted DHA molecules to identify promising candidates. We identify a molecule with a predicted energy density of 0.38 kJ/g, which is significantly larger than the 0.14 kJ/g computed for the parent compound. The free energy barrier to the back reaction is 11 kJ/mol higher than the parent compound, which should correspond to a half-life of about 10 days—4 months. This is considerably longer than the 3–39 h (depending on solvent) observed for the parent compound and sufficiently long for many practical applications. Our paper makes two main important contributions: (1) a novel and generally applicable methodological approach that makes screening of huge libraries for properties involving chemical reactivity with modest computational resources, and (2) a clear demonstration that the storage capacity of the DHA/VHF thermocouple cannot be increased to >0.5 kJ/g by combining simple substituents.

Virtual screening of norbornadiene-based molecular solar thermal energy storage systems using a genetic algorithm

“…In a previous study 32 we have shown that chemical space of MOST candidates can be searched efficiently by genetic algorithms and we apply a similar approach here. The chemical space shown in Figure 2 is encoded by a gene like that shown in Figure 3.…”

A Neural Network Approach for Property Determination of Molecular Solar Cell Candidates

“…Previous work has been done on a similar database, focusing directly on the thermochemical properties. 14 Our focus here is predicting the dipole moments and how they relate to the thermochemical properties.…”

Molecular Design of Dispersed Nickel Phthalocyanine@Nanocarbon Hybrid Catalyst for Active and Stable Electroreduction of CO2

“…22−24 In the recent decades, with the development of low-cost quantum mechanical methods, researchers are able to simulate over-large molecular libraries by high-throughput virtual screening. 25 The low-cost quantum mechanical methods or predictive machine learning (ML) models, when coupled with a proper molecular representation and evolutionary algorithms, make extremely efficient possible optimization of a certain molecular property in any predefined chemical subspace, with minimal human interference. 26 Although there has been exciting progress in rational or inverse design of molecular electrocatalysts, due to sluggish charge transfer kinetics with the electrode, efforts have been made to immobilize molecular electrocatalysts onto an electric conducting substrate to achieve heterogenization.…”

“…In terms of an atomic-level understanding of the active site and manipulatable local environment, molecular systems, especially transition metal phthalocyanines and porphins, could outperform SACs since their geometric and electronic structures are more well-defined; , hence, one can make modifications to it by substituent or through-space effects based on an understanding of the electronic structure–activity relationship to improve its property toward a certain optimum, i.e., molecular engineering. − The concept has been widely applied to various energy applications including solar cells, redox flow cells, molecular photocatalysis, and homogeneous electrocatalysis. − In the recent decades, with the development of low-cost quantum mechanical methods, researchers are able to simulate over-large molecular libraries by high-throughput virtual screening . The low-cost quantum mechanical methods or predictive machine learning (ML) models, when coupled with a proper molecular representation and evolutionary algorithms, make extremely efficient possible optimization of a certain molecular property in any predefined chemical subspace, with minimal human interference …”