Jensen, Klavs F.

(CdSe)ZnS Core−Shell Quantum Dots: Synthesis and Characterization of a Size Series of Highly Luminescent Nanocrystallites

Dabbousi, B. O., Rodriguez-Viejo, J., Mikulec, F. V., Heine, J. R., Mattoussi, H., Ober, R., Jensen, K. F., & Bawendi, M. G. (1997) · The Journal of Physical Chemistry B

Quantum Dots Synthesis And PropertiesSemiconductor Quantum Structures and Devices

Cited by 4,229

Analyzing Learned Molecular Representations for Property Prediction

Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 1,923

In vitro and ex vivo strategies for intracellular delivery

Stewart, M. P., Sharei, A., Ding, X., Sahay, G., Langer, R., & Jensen, K. F. (2016) · Nature

RNA Interference and Gene DeliveryNanoparticle-Based Drug Delivery

Cited by 885

Machine Learning in Computer-Aided Synthesis Planning

Coley, C. W., Green, W. H., & Jensen, K. F. (2018) · Accounts of Chemical Research

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 704

Next-generation in vivo optical imaging with short-wave infrared quantum dots

Bruns, O. T., Bischof, T. S., Harris, D. K., Franke, D., Shi, Y., Riedemann, L., Bartelt, A., Jaworski, F. B., Carr, J. A., Rowlands, C. J., et al. (2017) · Nature Biomedical Engineering

Quantum Dots Synthesis And PropertiesOptical Imaging and Spectroscopy Techniques

Cited by 686

A graph-convolutional neural network model for the prediction of chemical reactivity

Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 679

Microchemical systems for continuous-flow synthesis

Hartman, R. L., & Jensen, K. F. (2009) · Lab on a Chip

Innovative Microfluidic and Catalytic Techniques InnovationMicrofluidic and Capillary Electrophoresis Applications

Cited by 508

Convolutional embedding of attributed molecular graphs for physical property prediction

Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsGraph theory and applications

Cited by 470

Using Machine Learning To Predict Suitable Conditions for Organic Reactions

Gao, H., Struble, T. J., Coley, C. W., Wang, Y., Green, W. H., & Jensen, K. F. (2018) · ACS Central Science

Machine Learning in Materials ScienceComputational Drug Discovery Methods

Cited by 431

SCScore: Synthetic Complexity Learned from a Reaction Corpus

Coley, C. W., Rogers, L., Green, W. H., & Jensen, K. F. (2018) · Journal of Chemical Information and Modeling

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 381

The bifurcation behavior of tubular reactors

Jensen, K. F., & Ray, W. H. (1982) · Chemical Engineering Science

Heat and Mass Transfer in Porous MediaNonlinear Dynamics and Pattern Formation

Cited by 180

Rapid Flow‐Based Peptide Synthesis

Simon, M. D., Heider, P. L., Adamo, A., Vinogradov, A. A., Mong, S. K., Li, X., Berger, T., Policarpo, R. L., Zhang, C., Zou, Y., et al. (2014) · ChemBioChem

Chemical Synthesis and AnalysisInnovative Microfluidic and Catalytic Techniques Innovation

Cited by 4