Gao, H., Pauphilet J., Struble, T. J., Coley, C. W., Jensen, K. F. (2020). Direct optimization across computer generated reaction networks balances materials use and feasibility of synthesis plans for molecule libraries. Journal of Chemical Information and Modeling, Accepted.

Wang, X., Qian, Y., Gao, H., Coley, C. W., Mo, Y., Barzilay R., Jensen, K. F. (2020). Towards Efficient Discovery of Green Synthesis Pathways with Monte Carlo Tree Search and Reinforcement Learning. Chemical Science, 2020, 11, 10959-10972. DOI: 10.1039/D0SC04184J

Plehiers, P. P., Coley, C. W., Gao H., Vermeire F. H., Dobbelaere M. R., Stevens C. V., Van Geem K. M., Green W. H. (2020). Artificial Intelligence for Computer-Aided Synthesis In Flow: Analysis and Selection of Reaction Components. Frontiers in Chemical Engineering, 2020, 5. DOI: 10.3389/fceng.2020.00005

Gao, H., Coley, C. W., Struble, T. J., Li, L., Qian, Y., Green, W. H., Jensen, K. F. (2020). Combining Retrosynthesis and Mixed-Integer Optimization for Predicting the Minimum Chemical Inventory Needed to Realize a WHO Essential Medicines List. React. Chem. Eng., 5, 367-376. DOI: 10.1039/C9RE00348G

Coley, C. W., Thomas D., Lummiss, J., Jaworski, J. Rogers, L. Schultz, V., Fishmann J., Breen, C., Hart, T., Gao, H., Hicklin, R., Plehiers, P., Piotti, J., Green, W. H., Hart, J., Jamison, T. F., Jensen, K. F. (2019). A Robotic Platform for Flow Synthesis of Organic Compounds Informed by AI Planning. Science, vol. 365(6453), eaax1566. DOI: 10.1126/science.aax1566

Gao, H., Struble, T. J., Coley, C. W., Green, W. H., Jensen, K. F. (2018). Using Machine Learning to Predict Suitable Conditions for Organic Reactions. ACS Central Science, 4.11, 1465-1476. DOI: 10.1021/acscentsci.8b00357 (Highlighted by Science, https://science.sciencemag.org/content/362/6420/1260.2)

Gao, H., Waechter, A., Konstantinov, I. A., Arturo, S. G., Broadbelt, L. J. (2018). Application and Comparison of Derivative-free Optimization Algorithms to Control and Optimize Free Radical Polymerization Simulated Using the Kinetic Monte Carlo Method. Computers & Chemical Engineering, vol. 108, 268-275. DOI: 10.1016/j.compchemeng.2017.09.015

Gao, H., Konstantinov, I. A., Arturo, S. G., Broadbelt, L. J. (2017). On the Modeling of Number and Weight Average Molecular Weight of Polymers. Chemical Engineering Journal, vol. 327, 906-913. DOI: 10.1016/j.cej.2017.06.131

Gao, H., Konstantinov, I. A., Arturo, S. G., Broadbelt, L. J. (2016). Acceleration of Kinetic Monte Carlo Simulations for Free Radical Copolymerization: a Hybrid Approach with Scaling of Kinetic Parameters. AIChE Journal, vol. 63(9), 4013-4021. DOI: 10.1002/aic.15751

Zhang, G., Zhang, L., Gao, H.; Konstantinov, I. A., Arturo, S. G., Yu, D., Torkelson, J. M., Broadbelt, L. J. (2016). A Combined Computational and Experimental Study of Copolymerization Propagation Kinetics for 1-Ethylcyclopentyl Methacrylate and Methyl Methacrylate. Macromol. Theory Simul., vol. 25(3), 263-273. DOI: 10.1002/mats201500072

Gao, H., Oakley, L. H., Konstantinov, I. A., Arturo, S. G., Broadbelt, L. J. (2015). Acceleration of Kinetic Monte Carlo Method for the Simulations of Free Radical Copolymerization through Scaling. Industrial & Engineering Chemistry Research, vol. 54 (48), 11975-11985.  DOI: 10.1021/acs.iecr.5b03198 (Selected as the ACS Editor’s Choice for October 26th, 2015)

Regatte, V. R., Gao, H., Konstantinov, I. A., Arturo, S. G., Broadbelt, L. J. (2014). Design of Copolymers Based on Sequence Distribution for a Targeted Molecular Weight and Conversion. Macromol. Theory Simul., vol. 23 (9), 564-574. DOI: 10.1002/mats.201400037

Leperi, K., Gao, H., Snurr, R.Q., You, F. (2014). Modeling and Optimization of a Two-stage MOF-based Pressure/vacuum Swing Adsorption Process Coupled with Material Selection. Proceedings of the 17th Conference on Process Integration, Modelling and Optimization for Energy Saving and Pollution Reduction (PRES). Chemical Engineering Transactions, 39, 277-282. DOI: 10.3303/CET1439047