scholarly journals Inverse Molecular Design of Alkoxides and Phenoxides for Aqueous Direct Air Capture of CO2

2021 ◽  
Author(s):  
Zisheng Zhang ◽  
Amanda L. Kummeth ◽  
Jenny Y. Yang ◽  
Anastassia N. Alexandrova
Keyword(s):  
High Performance ◽  
Density Functional ◽  
Molecular Design ◽  
Chemical Space ◽  
Binding Capacity ◽  
Poor Correlation ◽  
Co2 Absorption ◽  
Computational Screening ◽  
Direct Air Capture ◽  
Air Capture

Aqueous direct air capture (DAC) is a key technology toward a carbon negative infrastructure. Developing sorbent molecules with water- and oxygen-tolerance and high CO2 binding capacity is therefore highly desired. In this work, we analyze the CO2 absorption chemistries on amines, alkoxides, and phenoxides with density functional theory (DFT) calculations and search for the optimal sorbent using an inverse molecular design strategy. The alkoxides and phenoxides are found to be more suitable for aqueous DAC than amines thanks to their water-tolerance and capture stoichiometry of 1:1 (2:1 for amines). All three molecular systems are found to obey the same linear scaling relationship (LSR) between pK_(CO_2 ) and pK_a, since both CO2 and proton are bonded to the nucleophilic binding site through a majorly σ bonding orbital. Several high-performance alkoxides are proposed from the computational screening. In contrast, phenoxides have relatively poor correlation between pK_(CO_2 ) and pK_a, showing promise for optimization. We apply genetic algorithm (GA) to search the chemical space of substituted phenoxides for the optimal sorbent. Several promising candidates that break the LSR are discovered. The most promising off-LSR candidate phenoxides feature bulky ortho substituents forcing the CO2 adduct into a perpendicular configuration with respect to the aromatic ring. In this configuration, CO2 utilizes a different molecular orbital for binding than does the proton, and the pK_(CO_2 ) and pK_a are thus decoupled. The pK_(CO_2 )-pK_a trend and off-LSR behaviors are then confirmed by experiments, validating the inverse molecular design framework. This work not only extensively studies the chemistry of the aqueous DAC, but also presents a transferrable computational workflow for understanding and optimization of other functional molecules.

scholarly journals Multi-fidelity prediction of molecular optical peaks with deep learning

2021 ◽  
Author(s):  
Kevin Greenman ◽  
William Green ◽  
Rafael Gómez-Bombarelli
Keyword(s):  
Optical Properties ◽  
Statistical Methods ◽  
Message Passing ◽  
Density Functional ◽  
Molecular Design ◽  
Chemical Space ◽  
Epistemic Uncertainty ◽  
Computational Cost ◽  
Regression Tree ◽  
Td Dft

Optical properties are central to molecular design for many applications, including solar cells and biomedical imaging. A variety of ab initio and statistical methods have been developed for their prediction, each with a trade-off between accuracy, generality, and cost. Existing theoretical methods such as time-dependent density functional theory (TD-DFT) are generalizable across chemical space because of their robust physics-based foundations but still exhibit random and systematic errors with respect to experiment despite their high computational cost. Statistical methods can achieve high accuracy at a lower cost, but data sparsity and unoptimized molecule and solvent representations often limit their ability to generalize. Here, we utilize directed message passing neural networks (D-MPNNs) to represent both dye molecules and solvents for predictions of molecular absorption peaks in solution. Additionally, we demonstrate a multi-fidelity approach based on an auxiliary model trained on over 28,000 TD-DFT calculations that further improves accuracy and generalizability, as shown through rigorous splitting strategies. Combining several openly-available experimental datasets, we benchmark these methods against a state-of-the-art regression tree algorithm and compare the D-MPNN solvent representation to several alternatives. Finally, we explore the interpretability of the learned representations using dimensionality reduction and evaluate the use of ensemble variance as an estimator of the epistemic uncertainty in our predictions of molecular peak absorption in solution. The prediction methods proposed herein can be integrated with active learning, generative modeling, and experimental workflows to enable the more rapid design of molecules with targeted optical properties.

scholarly journals Pareto Optimization of Oligomer Polarizability and Dipole Moment using a Genetic Algorithm

2021 ◽  
Author(s):  
Danielle Hiener ◽  
Geoffrey Hutchison
Keyword(s):  
Genetic Algorithm ◽  
Dipole Moment ◽  
High Performance ◽  
Density Functional ◽  
Chemical Space ◽  
Dipole Moments ◽  
Tight Binding ◽  
Dielectric Materials ◽  
Underlying Structure ◽  
Large Set

High performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between polarizability and dipole moment indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polarizabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight binding method (GFN2-xTB) for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. We also explored the underlying structure-function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes.

Construction of novel molecular architectures from anthracene units and acetylene linkers

2012 ◽  
Vol 84 (4) ◽  
pp. 917-929 ◽  
Author(s):  
Shinji Toyota
Keyword(s):  
High Performance ◽  
Density Functional ◽  
Molecular Design ◽  
Spectroscopic Properties ◽  
Coupling Reactions ◽  
Thermal Reactions ◽  
Cyclic Oligomers ◽  
Molecular Architectures ◽  
Metal Catalyzed ◽  
Conjugated Compounds

To create novel π-conjugated compounds, we constructed various molecular architectures from anthracene units and acetylene linkers. Several cyclic oligomers ranging from dimers to dodecamers were synthesized by macrocyclization of acyclic precursors with metal-catalyzed coupling reactions. The structures, dynamic behavior, and spectroscopic features were greatly influenced by the number of anthracene units and the combination of building units and linkers. Optically active and circular dichroism (CD)-active enantiomers of some chiral cyclic oligomers were resolved by chiral high-performance liquid chromatography (HPLC). Conformational analysis of hexamers and higher oligomers was performed with the aid of density functional theory (DFT) calculations. Acyclic oligomers underwent reversible folding–unfolding processes via photochemical and thermal reactions. These results suggest that transannular π–π interactions between anthracene units are important factors in controlling the structural and spectroscopic properties and functions of π-conjugated compounds. The scope and perspectives of this molecular design are discussed on the basis of previous studies.

scholarly journals Direct Air Capture of CO2 with Aqueous Amino Acids and Solid Bis-Iminoguanidines (BIGs)

2019 ◽  
Author(s):  
Radu Custelcean ◽  
Neil J. Williams ◽  
Kathleen A. Garrabrant ◽  
Pierrick Agullo ◽  
Flavien M. Brethomé ◽  
...  
Keyword(s):  
Amino Acids ◽  
Co2 Absorption ◽  
Capture Process ◽  
Bench Scale ◽  
Direct Air Capture ◽  
Air Capture

A bench-scale direct air capture process is reported, based on CO2 absorption with aqueous amino acids and carbonate crystallization with a guanidine compound.

scholarly journals Pareto Optimization of Oligomer Polarizability and Dipole Moment using a Genetic Algorithm

2021 ◽  
Author(s):  
Danielle Hiener ◽  
Geoffrey Hutchison
Keyword(s):  
Genetic Algorithm ◽  
Dipole Moment ◽  
High Performance ◽  
Density Functional ◽  
Chemical Space ◽  
Dipole Moments ◽  
Tight Binding ◽  
Dielectric Materials ◽  
Underlying Structure ◽  
Large Set

High performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between polarizability and dipole moment indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polarizabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight binding method (GFN2-xTB) for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. We also explored the underlying structure-function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes.

scholarly journals Direct Air Capture of CO2 with Aqueous Amino Acids and Solid Bis-Iminoguanidines (BIGs)

2019 ◽  
Author(s):  
Radu Custelcean ◽  
Neil J. Williams ◽  
Kathleen A. Garrabrant ◽  
Pierrick Agullo ◽  
Flavien M. Brethomé ◽  
...  
Keyword(s):  
Amino Acids ◽  
Co2 Absorption ◽  
Capture Process ◽  
Bench Scale ◽  
Direct Air Capture ◽  
Air Capture

A bench-scale direct air capture process is reported, based on CO2 absorption with aqueous amino acids and carbonate crystallization with a guanidine compound.

Molecular Design of High Performance Poly-Fused Thiophene Radical Cations: A Density Functional Theory Study

2008 ◽  
Vol 47 (1) ◽  
pp. 420-424 ◽  
Author(s):  
Hiroshi Kawabata ◽  
Ken Tokunaga ◽  
Shigekazu Ohmori ◽  
Kazumi Matsushige ◽  
Hiroto Tachikawa
Keyword(s):  
Density Functional Theory ◽  
High Performance ◽  
Density Functional ◽  
Molecular Design ◽  
Radical Cations ◽  
Density Functional Theory Study ◽  
Functional Theory

Tuning the Redox Activity of Metal−Organic Frameworks for Enhanced, Selective O2 Binding

2019 ◽  
Author(s):  
Andrew Rosen ◽  
M. Rasel Mian ◽  
Timur Islamoglu ◽  
Haoyuan Chen ◽  
Omar Farha ◽  
...  
Keyword(s):  
Density Functional ◽  
Metal Organic Frameworks ◽  
Redox Activity ◽  
Screening Methods ◽  
Bridging Ligands ◽  
Metal Centers ◽  
Computational Screening ◽  
Open Metal Sites ◽  
Metal Organic ◽  
Unsaturated Metal Sites

<p>Metal−organic frameworks (MOFs) with coordinatively unsaturated metal sites are appealing as adsorbent materials due to their tunable functionality and ability to selectively bind small molecules. Through the use of computational screening methods based on periodic density functional theory, we investigate O<sub>2</sub> and N<sub>2</sub> adsorption at the coordinatively unsaturated metal sites of several MOF families. A variety of design handles are identified that can be used to modify the redox activity of the metal centers, including changing the functionalization of the linkers (replacing oxido donors with sulfido donors), anion exchange of bridging ligands (considering μ-Br<sup>-</sup>, μ-Cl<sup>-</sup>, μ-F<sup>-</sup>, μ-SH<sup>-</sup>, or μ-OH<sup>-</sup> groups), and altering the formal oxidation state of the metal. As a result, we show that it is possible to tune the O<sub>2</sub> affinity at the open metal sites of MOFs for applications involving the strong and/or selective binding of O<sub>2</sub>. In contrast with O<sub>2</sub> adsorption, N<sub>2</sub> adsorption at open metal sites is predicted to be relatively weak across the MOF dataset, with the exception of MOFs containing synthetically elusive V<sup>2+</sup> open metal sites. As one example from the screening study, we predict that exchanging the μ-Cl<sup>-</sup> ligands of M<sub>2</sub>Cl<sub>2</sub>(BBTA) (H<sub>2</sub>BBTA = 1<i>H</i>,5<i>H</i>-benzo(1,2-d:4,5-d′)bistriazole) with μ-OH<sup>-</sup> groups would significantly enhance the strength of O<sub>2</sub> adsorption at the open metal sites without a corresponding increase in the N<sub>2</sub> affinity. Experimental investigation of Co<sub>2</sub>Cl<sub>2</sub>(BBTA) and Co<sub>2</sub>(OH)<sub>2</sub>(BBTA) confirms that the former exhibits only weak physisorption, whereas the latter is capable of chemisorbing O<sub>2</sub> at room temperature. The chemisorption behavior is attributed to the greater electron-donating character of the μ-OH<sup>-</sup><sub> </sub>ligands and the presence of H-bonding interactions between the μ-OH<sup>-</sup> bridging ligands and the O<sub>2</sub> adsorbate.</p>

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning

2018 ◽  
Author(s):  
Sherif Tawfik ◽  
Olexandr Isayev ◽  
Catherine Stampfl ◽  
Joseph Shapter ◽  
David Winkler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Band Gap ◽  
Density Functional ◽  
2D Materials ◽  
Van Der Waals ◽  
Building Blocks ◽  
Machine Learning Techniques ◽  
Interlayer Distance ◽  
Computational Screening ◽  
Wide Range

Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.

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