A5051973614- Machine Learning in Materials Science
- Asymmetric Hydrogenation and Catalysis
- Ammonia Synthesis and Nitrogen Reduction
- Synthesis and Catalytic Reactions
- Computational Drug Discovery Methods
- Chemical Synthesis and Analysis
- Caching and Content Delivery
- Catalytic Processes in Materials Science
- Nanomaterials for catalytic reactions
- Scientific Computing and Data Management
- Innovative Microfluidic and Catalytic Techniques Innovation
- Electrocatalysts for Energy Conversion
- Crystallography and molecular interactions
- CO2 Reduction Techniques and Catalysts
- Catalysis and Oxidation Reactions
- Asymmetric Synthesis and Catalysis
- DNA and Biological Computing
- Innovation Policy and R&D
- Nanocluster Synthesis and Applications
- Topic Modeling
University of Copenhagen
2022-2025
Robert Bosch (Germany)
2023
We present a de novo discovery of an efficient catalyst the Morita-Baylis-Hillman (MBH) reaction by searching chemical space for molecules that lower estimated barrier rate-determining step using genetic algorithm (GA) starting from randomly selected tertiary amines. identify 435 candidates, virtually all which contain azetidine N as catalytically active site, is discovered GA. Two are further study based on their predicted synthetic accessibility and have barriers than known catalyst....
The mild and selective functionalization of carbon-hydrogen (C-H) bonds remains a pivotal challenge in organic synthesis, crucial for developing complex molecular architectures pharmaceuticals, polymers, agrochemicals. Despite advancements directing group (DG) methodologies computational approaches, predicting accurate regioselectivity C-H activation poses significant difficulties due to the diversity complexity compounds. This study introduces novel quantum mechanics-based workflow tailored...
This study introduces a novel approach for the unrestricted de novo design of transition metal catalysts, leveraging power genetic algorithms (GAs) and density functional theory (DFT) calculations. By focusing on Suzuki reaction, known its significance in forming carbon-carbon bonds, we demonstrate effectiveness fragment-based graph-based identifying ligands palladium-based catalytic systems. Our research highlights capability these to generate with desired thermodynamic properties, moving...
Using genetic algorithms and semiempirical quantum mechanical methods for discovery of nitrogen fixation catalysts.
This study introduces a novel approach for the de novo design of transition metal catalysts, leveraging power genetic algorithms and density functional theory calculations. By focusing on Suzuki reaction, known its significance in forming carbon-carbon bonds, we demonstrate effectiveness fragment-based graph-based identifying ligands palladium-based catalytic systems. Our research highlights capability these to generate with desired thermodynamic properties, moving beyond restriction...
This study leverages a graph-based genetic algorithm (GB-GA) for the design of efficient nitrogen-fixing catalysts as alternatives to Schrock catalyst, with aim improve energetics key reaction steps. Despite abundance nitrogen in atmosphere, it remains largely inaccessible due its inert nature. The molybdenum-based complex, offered breakthrough but practical application is limited low turnover numbers and energetic bottlenecks. our explores chemical space viable modifications evaluating each...
This study leverages a graph-based genetic algorithm (GB-GA) for the design of efficient nitrogen-fixing catalysts as alternatives to Schrock catalyst, with aim improve energetics key reaction steps. Despite abundance nitrogen in atmosphere, it remains largely inaccessible due its inert nature. The molybdenum-based complex, offered breakthrough but practical application is limited low turnover numbers and energetic bottlenecks. our explores chemical space viable modifications evaluating each...
This study introduces a novel approach for the unrestricted de novo design of transition metal catalysts, leveraging power genetic algorithms (GAs) and density functional theory (DFT) calculations. By focusing on Suzuki reaction, known its significance in forming carbon-carbon bonds, we demonstrate effectiveness fragment-based graph-based identifying ligands palladium-based catalytic systems. Our research highlights capability these to generate with desired thermodynamic properties, moving...
Computational discovery of organometallic catalysts that effectively catalyze nitrogen fixation is a difficult task. The complexity the chemical reactions involved and lack understanding natures enzyme raises need for intricate computational models. In this study, use dataset 91 experimentally verified ligands starting population Genetic Algorithm (GA) to discover molybdenum based catalyst in trigonal bipyramidal octahedral configurations. Through evolutionary with semi-empirical quantum...
We present a method for creating RDKit parsable SMILES transition metal complexes (TMCs) based on xyz-coordinates and overall charge of the complex. This can be viewed as an extension to program xyz2mol that does same organic molecules. The only dependency is RDKit, which makes it widely applicable. One thing has been lacking when comes generating from structure TMCs existing dataset compare with. Therefore, sanity-checking required manual work. we also generate two other ways; one where...
Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover new and efficient catalyst for the alcohol-mediated Morita-Baylis-Hillman (MBH) reaction. In particular, discovered was shown experimentally eight times more active than DABCO, commonly catalyze MBH This represents breakthrough in using generative models optimization. However, GA procedure, hence discovery, relied on two important pieces of information; 1) knowledge that...
Abstract We present a de novo discovery of an efficient catalyst the Morita–Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower estimated barrier rate‐determining step using genetic algorithm (GA) starting from randomly selected tertiary amines. identify 435 candidates, virtually all which contain azetidine N as catalytically active site, is discovered GA. Two are further study based on their predicted synthetic accessibility and have barriers than known...
Very recently our group has demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover new and efficient catalyst for the alcohol-mediated Morita- Baylis-Hillman (MBH) reaction. In particular, discovered was shown experimentally eight times more active than 1,4-diazabicyclo[2.2.2]octane (DABCO), which is commonly catalyze MBH This represents breakthrough in using generative models optimization. However, GA-procedure, hence discovery, relied on two...
Abstract Recently we have demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover new and efficient catalyst for the alcohol‐mediated Morita–Baylis–Hillman (MBH) reaction. In particular, discovered was shown experimentally eight times more active than DABCO, commonly catalyze MBH This represents breakthrough in using generative models optimization. However, GA procedure, hence discovery, relied on two important pieces of information; 1)...
Computational discovery of organometallic catalysts that effectively catalyze nitrogen fixation is a difficult task. The complexity the chemical reactions involved and lack understanding natures enzyme raises need for intricate computational models. In this study, use dataset 91 experimentally verified ligands starting population Genetic Algorithm (GA) to discover molybdenum based catalyst in trigonal bipyramidal octahedral configurations. Through evolutionary with semi-empirical quantum...
We present a de novo discovery of an efficient catalyst the Morita–Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower estimated barrier rate determining step using genetic algorithm (GA) starting from randomly selected tertiary amines. performed five independent GA searches resulted in 448 unique molecules, which we were able to locate 435 true transitions states at semiempirical level theory. The predicted activation energies all where than DABCO, is popular...
…fluorescence labeling opens exciting possibilities for advanced and superresolution microscopy techniques.I ntheir Research Article (e202219050) Michael Holtmannspçtter,J acob Piehler et al. have engineered HaloTag variants with restored dehalogenase activity,which enzymatically exchange fluorogenic substrates at the second-to-minutes time scale.Thus,long-term time-lapse imaging utmost spatial temporal resolution is achieved particularly striking performance in single molecule tracking...
We present a de novo discovery of an efficient catalyst the methanol-mediated Morita– Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower es- timated barrier rate-determining step using genetic algorithm (GA) starting from randomly selected tertiary amines. performed five independent GA searches resulted in 448 unique molecules, which we were able to locate 435 true transition states at semiem- pirical level theory. The predicted activation energies all than...
We present a de novo discovery of an efficient catalyst the methanol-mediated Morita– Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower es- timated barrier rate-determining step using genetic algorithm (GA) starting from randomly selected tertiary amines. performed five independent GA searches resulted in 448 unique molecules, which we were able to locate 435 true transition states at semiem- pirical level theory. The predicted activation energies all than...