Here is an overview of the current and upcoming research projects in the group. If you are interested in teaming up with us, please feel free to get in touch<\/a>. <\/p>\n \n <\/p>\n <\/a><\/p>\n This project investigates the utility of state-of-the-art machine learning, statistical learning, and informatics techniques in the context of molecular as well as condensed matter systems and processes. Our goal is to use this approach to uncover structure-property relationships for the rational design of new compounds, materials, and reactions. We plan to develop a general purpose software package, which will make the tools of the computer science and engineering community more accessible to the chemical disciplines. <\/p>\n Timeframe:<\/span> since 2014. <\/p>\n <\/a><\/p>\n In this project, we develop improved and generalized models (based on the Shockley-Queisser and Scharber models) to predict the power conversion efficiency and other performance related quantities of organic photovoltaic materials. We further investigate the implications that can be derived from these models for the design of new materials.<\/p>\n Timeframe:<\/span> since 2014. <\/p>\n <\/a><\/p>\n This project addresses questions concerning the utility of simple semiempirical methods for the rapid assessment of organic electronic compounds. A focus is on the PPP approximation and other π-electron models that can be used to readily generate big data sets and for the virtual high-throughput screening of the associated molecular space.\n<\/p>\n Timeframe:<\/span> since 2014. <\/p>\n <\/a><\/p>\n This project is concerned with the computational modeling and rational design of novel high-performance molecular materials for optical devices.<\/p>\n Timeframe:<\/span> since 2014. <\/p>\n <\/a><\/p>\n This project explores the design and utility of empirical calibration schemes for results from computational chemistry to compensate for some of the systematic discrepancies with respect to experimental data.\n<\/p>\n Timeframe:<\/span> since 2014. <\/p>\n <\/a><\/p>\n This project addresses technical issues of Big Data in the context of the chemical and materials sciences, such as databases and data handling.\n<\/p>\n Timeframe:<\/span> since 2014. <\/p>\n <\/a><\/p>\n The Clean Energy Project is a joint venture concerning the discovery of new materials for organic photovoltaics. It is led by the Aspuru-Guzik Research Group at Harvard University, supported by the IBM World Community Grid, and it involves a number of experimental collaborators.<\/p>\n Timeframe:<\/span> since 2009 (at UB since 2014). <\/p>\n <\/a><\/p>\n The UB Solar Fuel Project will be a virtual high-throughput discovery and design effort for solar water splitting catalysts.<\/p>\n Timeframe:<\/span> starting 2014 (tentatively). <\/p>\n <\/a><\/p>\n \n Timeframe:<\/span> TBD. <\/p>\n <\/a><\/p>\n \nOverview<\/h4>\n
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\nMachine Learning Toolbox for Data Analysis, Mining, and Modeling in the Chemical and Materials Sciences<\/h4>\n
\nTeam:<\/span> Mojtaba, Gaurav, Arti, Shawn.
\nPartners:<\/span> Govindaraju Group (UB).
\nFunding:<\/span> UB SEAS Startup.
\nPublications:<\/span> N\/A.<\/p>\n
\nAdvanced Models for the Prediction of the Photovoltaic Performance of Organic Semiconductors<\/h4>\n
\nTeam:<\/span> Bryan, Andrew, Sai, Zach.
\nPartners:<\/span> N\/A.
\nFunding:<\/span> UB SMURI Summer Research Award, UB SEAS Startup.
\nPublications:<\/span> N\/A.<\/p>\n
\nSemiempirical Approaches for the Virtual High-Throughput Characterization of Organic Semiconductors<\/h4>\n
\nTeam:<\/span> Ching-Yen, Jun, Andrew.
\nPartners:<\/span> N\/A.
\nFunding:<\/span> UB SEAS Startup.
\nPublications:<\/span> N\/A.<\/p>\n
\nNew Molecular and Polymeric Materials for Optical Applications<\/h4>\n
\nTeam:<\/span> Atif.
\nPartners:<\/span> Cheng Group (UB).
\nFunding:<\/span> UB SEAS Startup.
\nPublications:<\/span> N\/A.<\/p>\n
\nEmpirical Calibration Schemes for Computational Chemistry Data<\/h4>\n
\nTeam:<\/span> Bryan.
\nPartners:<\/span> Aspuru-Guzik Group (Harvard University)<\/a>.
\nFunding:<\/span> UB SMURI Summer Research Award.
\nPublications:<\/span> N\/A.<\/p>\n
\nBig Data Technology in the Chemical and Materials Sciences<\/h4>\n
\nTeam:<\/span> Bryan, Arti.
\nPartners:<\/span> Govindaraju Group (UB).
\nFunding:<\/span> N\/A.
\nPublications:<\/span> N\/A.<\/p>\n
\nThe Clean Energy Project<\/h4>\n
\nTeam (at UB):<\/span> Bryan, Andrew, Sai, Zach.
\nPartners:<\/span> Aspuru-Guzik Group (Harvard University)<\/a>, Bao Group (Stanford University), White Group (University of Linz), Mueller Group (Johns Hopkins University), Campos Group (Columbia University), Riede Group (Oxford University), the IBM World Community Grid team, Kitware Inc.
\nFunding (at UB):<\/span> UB SMURI Summer Research Award.
\nPublications:<\/span><\/p>\n\n
\nThe UB Solar Fuel Project<\/h4>\n
\nTeam:<\/span> TBD.
\nPartners:<\/span> TBD.
\nFunding:<\/span> UB SEAS Startup.
\nPublications:<\/span> N\/A.<\/p>\n
\nBiocatalytic Activation of Small Molecules<\/h4>\n
\nTeam:<\/span> TBD.
\nPartners:<\/span> TBD.
\nFunding:<\/span> TBD.
\nPublications:<\/span> N\/A.<\/p>\n
\nMachine Learning Route to New Electronic Structure Methods<\/h4>\n