Polymeric Chiral Catalyst Design and Chiral Polymer Synthesis

2011 ◽  
Keyword(s):  
Polymer Synthesis ◽  
Catalyst Design ◽  
Chiral Catalyst ◽  
Chiral Polymer

Iterative Supervised Principal Component Analysis-Driven Ligand Design for Regioselective Ti-Catalyzed Pyrrole Synthesis

2020 ◽  
Author(s):  
Xin Yi See ◽  
Benjamin Reiner ◽  
Xuelan Wen ◽  
T. Alexander Wheeler ◽  
Channing Klein ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
De Novo ◽  
Principal Component ◽  
Component Analysis ◽  
Catalyst Design ◽  
Data Driven ◽  
Initial Reaction ◽  
Training Set ◽  
Reaction Conditions ◽  
Component Loadings

<div> <div> <div> <p>Herein, we describe the use of iterative supervised principal component analysis (ISPCA) in de novo catalyst design. The regioselective synthesis of 2,5-dimethyl-1,3,4-triphenyl-1H- pyrrole (C) via Ti- catalyzed formal [2+2+1] cycloaddition of phenyl propyne and azobenzene was targeted as a proof of principle. The initial reaction conditions led to an unselective mixture of all possible pyrrole regioisomers. ISPCA was conducted on a training set of catalysts, and their performance was regressed against the scores from the top three principal components. Component loadings from this PCA space along with k-means clustering were used to inform the design of new test catalysts. The selectivity of a prospective test set was predicted in silico using the ISPCA model, and only optimal candidates were synthesized and tested experimentally. This data-driven predictive-modeling workflow was iterated, and after only three generations the catalytic selectivity was improved from 0.5 (statistical mixture of products) to over 11 (> 90% C) by incorporating 2,6-dimethyl- 4-(pyrrolidin-1-yl)pyridine as a ligand. The successful development of a highly selective catalyst without resorting to long, stochastic screening processes demonstrates the inherent power of ISPCA in de novo catalyst design and should motivate the general use of ISPCA in reaction development. </p> </div> </div> </div>

Metal-, and Organocatalyst-Free One-Pot Assembly of Chiral Aza-Tricyclic Molecules: Creating Six Contiguous Stereocenters from 2-D-Structures and an Amino Acid

2020 ◽  
Author(s):  
Dung Do
Keyword(s):  
Amino Acid ◽  
Chemical Space ◽  
Structural Diversity ◽  
Therapeutic Agents ◽  
Chiral Molecules ◽  
One Pot ◽  
Complex Molecules ◽  
Chiral Catalyst ◽  
Chiral Complex ◽  
Free Process

<p>Chiral molecules with their defined 3-D structures are of paramount importance for the study of chemical biology and drug discovery. Having rich structural diversity and unique stereoisomerism, chiral molecules offer a large chemical space that can be explored for the design of new therapeutic agents.<sup>1</sup> Practically, chiral architectures are usually prepared from organometallic and organocatalytic processes where a transition metal or an organocatalyst is tailor-made for desired reactions. As a result, developing a method that enables rapid assembly of chiral complex molecules under metal- and organocatalyst-free condition represents a daunting challenge. Here we developed a straightforward route to create a chiral 3-D structure from 2-D structures and an amino acid without any chiral catalyst. The center of this research is the design of a <a>special chiral spiroimidazolidinone cyclohexadienone intermediate</a>, a merger of a chiral reactive substrate with multiple nucleophillic/electrophillic sites and a transient organocatalyst. <a>This unique substrate-catalyst (“subcatalyst”) dual role of the intermediate enhances </a><a>the coordinational proximity of the chiral substrate and catalyst</a> in the key Aza-Michael/Michael cascade resulting in a substantial steric discrimination and an excellent overall diastereoselectivity. Whereas the “subcatalyst” (hidden catalyst) is not present in the reaction’s initial components, which renders a chiral catalyst-free process, it is strategically produced to promote sequential self-catalyzed reactions. The success of this methodology will pave the way for many efficient preparations of chiral complex molecules and aid for the quest to create next generation of therapeutic agents.</p>

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>

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