scholarly journals Electrosynthesis of 1,4-bis(diphenylphosphanyl) tetrasulfide via sulfur radical addition as cathode material for rechargeable lithium battery

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Dan-Yang Wang ◽  
Yubing Si ◽  
Wei Guo ◽  
Yongzhu Fu
Keyword(s):  
Density Functional ◽  
Chemical Space ◽  
Functional Materials ◽  
Radical Addition ◽  
Chemical Diversity ◽  
Energy Storage Materials ◽  
Intramolecular Rearrangement ◽  
Synthesis Methods ◽  
Dithiophosphinic Acid ◽  
Sulfur Radical

AbstractOrganic electrodes are promising as next generation energy storage materials originating from their enormous chemical diversity and electrochemical specificity. Although organic synthesis methods have been extended to a broad range, facile and selective methods are still needed to expose the corners of chemical space. Herein, we report the organopolysulfide, 1,4-bis(diphenylphosphanyl)tetrasulfide, which is synthesized by electrochemical oxidation of diphenyl dithiophosphinic acid featuring the cleavage of a P–S single bond and a sulfur radical addition reaction. Density functional theory proves that the external electric field triggers the intramolecular rearrangement of diphenyl dithiophosphinic acid through dehydrogenation and sulfur migration along the P–S bond axis. Impressively, the Li/bis(diphenylphosphanyl)tetrasulfide cell exhibits the high discharge voltage of 2.9 V and stable cycling performance of 500 cycles with the capacity retention of 74.8%. Detailed characterizations confirm the reversible lithiation/delithiation process. This work demonstrates that electrochemical synthesis offers the approach for the preparation of advanced functional materials.

scholarly journals Dataset’s chemical diversity limits the generalizability of machine learning predictions

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Marta Glavatskikh ◽  
Jules Leguy ◽  
Gilles Hunault ◽  
Thomas Cauchy ◽  
Benoit Da Mota
Keyword(s):  
Machine Learning ◽  
Density Functional ◽  
Chemical Space ◽  
Chemical Properties ◽  
Density Functional Theory Calculations ◽  
Real Data ◽  
Chemical Diversity ◽  
Energy Prediction ◽  
The Neural Network ◽  
Golden Standard

Abstract The QM9 dataset has become the golden standard for Machine Learning (ML) predictions of various chemical properties. QM9 is based on the GDB, which is a combinatorial exploration of the chemical space. ML molecular predictions have been recently published with an accuracy on par with Density Functional Theory calculations. Such ML models need to be tested and generalized on real data. PC9, a new QM9 equivalent dataset (only H, C, N, O and F and up to 9 “heavy” atoms) of the PubChemQC project is presented in this article. A statistical study of bonding distances and chemical functions shows that this new dataset encompasses more chemical diversity. Kernel Ridge Regression, Elastic Net and the Neural Network model provided by SchNet have been used on both datasets. The overall accuracy in energy prediction is higher for the QM9 subset. However, a model trained on PC9 shows a stronger ability to predict energies of the other dataset.

Towards a Design of Active Oxygen Evolution Catalysts: Insights from Automated Density Functional Theory Calculations and Machine Learning

2019 ◽  
Author(s):  
Seoin Back ◽  
Kevin Tran ◽  
Zachary Ulissi
Keyword(s):  
Machine Learning ◽  
Oxygen Evolution ◽  
Active Sites ◽  
Density Functional ◽  
Chemical Space ◽  
Density Functional Theory Calculations ◽  
Catalyst Design ◽  
Transition Metal Catalysts ◽  
Oxide Materials ◽  
Design Strategies

<div> <div> <div> <div><p>Developing active and stable oxygen evolution catalysts is a key to enabling various future energy technologies and the state-of-the-art catalyst is Ir-containing oxide materials. Understanding oxygen chemistry on oxide materials is significantly more complicated than studying transition metal catalysts for two reasons: the most stable surface coverage under reaction conditions is extremely important but difficult to understand without many detailed calculations, and there are many possible active sites and configurations on O* or OH* covered surfaces. We have developed an automated and high-throughput approach to solve this problem and predict OER overpotentials for arbitrary oxide surfaces. We demonstrate this for a number of previously-unstudied IrO2 and IrO3 polymorphs and their facets. We discovered that low index surfaces of IrO2 other than rutile (110) are more active than the most stable rutile (110), and we identified promising active sites of IrO2 and IrO3 that outperform rutile (110) by 0.2 V in theoretical overpotential. Based on findings from DFT calculations, we pro- vide catalyst design strategies to improve catalytic activity of Ir based catalysts and demonstrate a machine learning model capable of predicting surface coverages and site activity. This work highlights the importance of investigating unexplored chemical space to design promising catalysts.<br></p></div></div></div></div><div><div><div> </div> </div> </div>

scholarly journals r2SCAN-3c: An Efficient “Swiss Army Knife” Composite Electronic-Structure Method

2020 ◽  
Author(s):  
Stefan Grimme ◽  
Andreas Hansen ◽  
Sebastian Ehlert ◽  
Jan-Michael Mewes
Keyword(s):  
Electronic Structure ◽  
Density Functional ◽  
Chemical Space ◽  
Noncovalent Interactions ◽  
New Method ◽  
Basis Set ◽  
Basis Set Superposition Error ◽  
Benchmark Database ◽  
The Cost ◽  
Conformational Energies

The recently proposed second revision of the SCAN meta-GGA density-functional approximation (DFA) {Furness et al., J. Phys. Chem. Lett. 2020, 11, 8208-8215, termed r<sup>2</sup>SCAN} is used to construct an efficient composite electronic-structure method termed r<sup>2</sup>SCAN-3c, expanding the "3c'' series (hybrid: HSE/PBEh-3c, GGA: B97-3c, HF: HF-3c) to themGGA level. To this end, the unaltered r<sup>2</sup>SCAN functional is combined with a tailor-made <br>triple-zeta Gaussian AO-basis as well as with refitted D4 and gCP corrections for London-dispersion and basis-set superposition error. The performance of the new method is evaluated for the GMTKN55 thermochemical database covering large parts of chemical space with about 1500 <br>data points, as well as additional benchmarks for noncovalent interactions, organometallic reactions, lattice energies of organic molecules and ices, as well as for the adsorption on polar salt and non-polar coinage-metal surfaces. These comprehensive tests reveal a spectacular performance and robustness of r<sup>2</sup>SCAN-3c for reaction energies and noncovalent interactions in molecular and periodic systems, as well as outstanding conformational energies, and consistent structures. At just one tenth of the cost, r<sup>2</sup>SCAN-3c provides one of the best results of all semi-local DFT/QZ methods ever tested for the GMTKN55 benchmark database. Specifically for reaction and conformational energies as well as for noncovalent interactions, the new method outperforms hybrid-DFT/QZ approaches, compared to which the computational savings are even larger (factor 100-1000).<br>In relation to other "3c'' methods, r<sup>2</sup>SCAN-3c by far surpasses the accuracy of its predecessor B97-3c at only about twice the cost. The perhaps most relevant remaining systematic deviation of r<sup>2</sup>SCAN-3c is due to self-interaction-error, owing to its mGGA nature. However, SIE is notably reduced compared to other (m)GGAs, as is demonstrated for several examples. After all, this remarkably efficient and robust method is chosen as our new group default, replacing previous low-level DFT and partially even expensive high-level methods in most standard applications for systems with up to several hundreds of atoms.<br><br>

scholarly journals Analysis of a large food chemical database: chemical space, diversity, and complexity

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 993 ◽  
Author(s):  
J. Jesús Naveja ◽  
Mariel P. Rico-Hidalgo ◽  
José L. Medina-Franco
Keyword(s):  
Natural Products ◽  
Chemical Space ◽  
Structural Complexity ◽  
Chemical Diversity ◽  
Chemical Structures ◽  
Novel Approach ◽  
Chemical Database ◽  
Protein Interactome ◽  
Limited Basis ◽  
Future Direction

Background: Food chemicals are a cornerstone in the food industry. However, its chemical diversity has been explored on a limited basis, for instance, previous analysis of food-related databases were done up to 2,200 molecules. The goal of this work was to quantify the chemical diversity of chemical compounds stored in FooDB, a database with nearly 24,000 food chemicals. Methods: The visual representation of the chemical space of FooDB was done with ChemMaps, a novel approach based on the concept of chemical satellites. The large food chemical database was profiled based on physicochemical properties, molecular complexity and scaffold content. The global diversity of FooDB was characterized using Consensus Diversity Plots. Results: It was found that compounds in FooDB are very diverse in terms of properties and structure, with a large structural complexity. It was also found that one third of the food chemicals are acyclic molecules and ring-containing molecules are mostly monocyclic, with several scaffolds common to natural products in other databases. Conclusions: To the best of our knowledge, this is the first analysis of the chemical diversity and complexity of FooDB. This study represents a step further to the emerging field of “Food Informatics”. Future study should compare directly the chemical structures of the molecules in FooDB with other compound databases, for instance, drug-like databases and natural products collections. An additional future direction of this work is to use the list of 3,228 polyphenolic compounds identified in this work to enhance the on-going polyphenol-protein interactome studies.

scholarly journals r2SCAN-3c: An Efficient “Swiss Army Knife” Composite Electronic-Structure Method

2020 ◽  
Author(s):  
Stefan Grimme ◽  
Andreas Hansen ◽  
Sebastian Ehlert ◽  
Jan-Michael Mewes
Keyword(s):  
Electronic Structure ◽  
Density Functional ◽  
Chemical Space ◽  
Noncovalent Interactions ◽  
New Method ◽  
Basis Set ◽  
Basis Set Superposition Error ◽  
Benchmark Database ◽  
The Cost ◽  
Conformational Energies

The recently proposed second revision of the SCAN meta-GGA density-functional approximation (DFA) {Furness et al., J. Phys. Chem. Lett. 2020, 11, 8208-8215, termed r<sup>2</sup>SCAN} is used to construct an efficient composite electronic-structure method termed r<sup>2</sup>SCAN-3c, expanding the "3c'' series (hybrid: HSE/PBEh-3c, GGA: B97-3c, HF: HF-3c) to themGGA level. To this end, the unaltered r<sup>2</sup>SCAN functional is combined with a tailor-made <br>triple-zeta Gaussian AO-basis as well as with refitted D4 and gCP corrections for London-dispersion and basis-set superposition error. The performance of the new method is evaluated for the GMTKN55 thermochemical database covering large parts of chemical space with about 1500 <br>data points, as well as additional benchmarks for noncovalent interactions, organometallic reactions, lattice energies of organic molecules and ices, as well as for the adsorption on polar salt and non-polar coinage-metal surfaces. These comprehensive tests reveal a spectacular performance and robustness of r<sup>2</sup>SCAN-3c for reaction energies and noncovalent interactions in molecular and periodic systems, as well as outstanding conformational energies, and consistent structures. At just one tenth of the cost, r<sup>2</sup>SCAN-3c provides one of the best results of all semi-local DFT/QZ methods ever tested for the GMTKN55 benchmark database. Specifically for reaction and conformational energies as well as for noncovalent interactions, the new method outperforms hybrid-DFT/QZ approaches, compared to which the computational savings are even larger (factor 100-1000).<br>In relation to other "3c'' methods, r<sup>2</sup>SCAN-3c by far surpasses the accuracy of its predecessor B97-3c at only about twice the cost. The perhaps most relevant remaining systematic deviation of r<sup>2</sup>SCAN-3c is due to self-interaction-error, owing to its mGGA nature. However, SIE is notably reduced compared to other (m)GGAs, as is demonstrated for several examples. After all, this remarkably efficient and robust method is chosen as our new group default, replacing previous low-level DFT and partially even expensive high-level methods in most standard applications for systems with up to several hundreds of atoms.<br><br>

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 The Brazilian Compound Library (BraCoLi) Database: A Brazilian Repository of Chemical and Biological Information for Drug Design

2021 ◽  
Author(s):  
Gabriel Corrêa Veríssimo ◽  
Valtair Severino dos Santos Junior ◽  
Ingrid Ariela do Rosário de Almeida ◽  
Marina Sant'Anna Mitraud Ruas ◽  
Lukas Galuppo Coutinho ◽  
...  
Keyword(s):  
Drug Design ◽  
Chemical Space ◽  
Principal Component ◽  
Chemical Diversity ◽  
Biological Information ◽  
Virtual Library ◽  
Compound Library ◽  
Computer Aided Drug Design ◽  
Approved Drugs ◽  
Fda Approved Drugs

The Brazilian Compound Library (BraCoLi) is a novel virtual library of manually curated compounds developed by Brazilian research groups to support further computer-aided drug design works. Herein, the first version of the database is described comprising 1,176 compounds. Also, the chemical diversity and drug-like profile of BraCoLi were defined to analyze its chemical space. A significant amount of the compounds fitted Lipinski and Veber’s rules, alongside other drug-likeness properties. Principal component analysis showed that BraCoLi is similar to other databases (FDA-approved drugs and NuBBEDB) regarding structural and physicochemical patterns. Finally, a scaffold analysis showed that BraCoLi presents several privileged chemical skeletons with great diversity.

scholarly journals Rare earth based nanostructured materials: synthesis, functionalization, properties and bioimaging and biosensing applications

Nanophotonics ◽  
2017 ◽  
Vol 6 (5) ◽  
pp. 881-921 ◽  
Author(s):  
Alberto Escudero ◽  
Ana I. Becerro ◽  
Carolina Carrillo-Carrión ◽  
Nuria O. Núñez ◽  
Mikhail V. Zyuzin ◽  
...  
Keyword(s):  
Rare Earth ◽  
Functional Materials ◽  
Synthesis Methods ◽  
Special Focus ◽  
X Rays ◽  
Materials Synthesis ◽  
Comprehensive Overview ◽  
Current State ◽  
Metal Organic ◽  
X Ray Computed

AbstractRare earth based nanostructures constitute a type of functional materials widely used and studied in the recent literature. The purpose of this review is to provide a general and comprehensive overview of the current state of the art, with special focus on the commonly employed synthesis methods and functionalization strategies of rare earth based nanoparticles and on their different bioimaging and biosensing applications. The luminescent (including downconversion, upconversion and permanent luminescence) and magnetic properties of rare earth based nanoparticles, as well as their ability to absorb X-rays, will also be explained and connected with their luminescent, magnetic resonance and X-ray computed tomography bioimaging applications, respectively. This review is not only restricted to nanoparticles, and recent advances reported for in other nanostructures containing rare earths, such as metal organic frameworks and lanthanide complexes conjugated with biological structures, will also be commented on.

Calcium Carbonate and Cellulose/Calcium Carbonate Composites: Synthesis, Characterization, and Biomedical Applications

2016 ◽  
Vol 875 ◽  
pp. 24-44
Author(s):  
Ming Guo Ma ◽  
Shan Liu ◽  
Lian Hua Fu
Keyword(s):  
Calcium Carbonate ◽  
Biomedical Applications ◽  
Value Added ◽  
Functional Materials ◽  
Industrial Applications ◽  
Precipitation Method ◽  
Synthesis Methods ◽  
Water Pretreatment ◽  
Potential Applications ◽  
Co Precipitation

CaCO3 has six polymorphs such as vaterite, aragonite, calcite, amorphous, crystalline monohydrate, and hexahydrate CaCO3. CaCO3 is a typical biomineral that is abundant in both organisms and nature and has important industrial applications. Cellulose could be used as feedstocks for producing biofuels, bio-based chemicals, and high value-added bio-based materials. In the past, more attentions have been paid to the synthesis and applications of CaCO3 and cellulose/CaCO3 nanocomposites due to its relating properties such as mechanical strength, biocompatibility, and biodegradation, and bioactivity, and potential applications including biomedical, antibacterial, and water pretreatment fields as functional materials. A variety of synthesis methods such as the hydrothermal/solvothermal method, biomimetic mineralization method, microwave-assisted method, (co-) precipitation method, and sonochemistry method, were employed to the preparation of CaCO3 and cellulose/CaCO3 nanocomposites. In this chapter, the recent development of CaCO3 and cellulose/CaCO3 nanocomposites has been reviewed. The synthesis, characterization, and biomedical applications of CaCO3 and cellulose/CaCO3 nanocomposites are summarized. The future developments of CaCO3 and cellulose/CaCO3 nanocomposites are also suggested.

Computational analysis and identification of battery materials

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
F. Meutzner ◽  
T. Nestler ◽  
M. Zschornak ◽  
P. Canepa ◽  
G. S. Gautam ◽  
...  
Keyword(s):  
Energy Storage ◽  
Functional Materials ◽  
Theoretical Background ◽  
Energy Storage Materials ◽  
Storage Materials ◽  
Mobile Species ◽  
Very Large Databases ◽  
Future Data ◽  
Structure Databases ◽  
The One

AbstractCrystallography is a powerful descriptor of the atomic structure of solid-state matter and can be applied to analyse the phenomena present in functional materials. Especially for ion diffusion – one of the main processes found in electrochemical energy storage materials – crystallography can describe and evaluate the elementary steps for the hopping of mobile species from one crystallographic site to another. By translating this knowledge into parameters and search for similar numbers in other materials, promising compounds for future energy storage materials can be identified. Large crystal structure databases like the ICSD, CSD, and PCD have accumulated millions of measured crystal structures and thus represent valuable sources for future data mining and big-data approaches. In this work we want to present, on the one hand, crystallographic approaches based on geometric and crystal-chemical descriptors that can be easily applied to very large databases. On the other hand, we want to show methodologies based onab initioand electronic modelling which can simulate the structure features more realistically, incorporating also dynamic processes. Their theoretical background, applicability, and selected examples are presented.

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