| Chemprop: A machine learning package for chemical property prediction E Heid, KP Greenman, Y Chung, SC Li, DE Graff, FH Vermeire, H Wu, ... Journal of Chemical Information and Modeling 64 (1), 9-17, 2023 | 73 | 2023 |
| Learning matter: Materials design with machine learning and atomistic simulations S Axelrod, D Schwalbe-Koda, S Mohapatra, J Damewood, KP Greenman, ... Accounts of Materials Research 3 (3), 343-357, 2022 | 53 | 2022 |
| Multi-fidelity prediction of molecular optical peaks with deep learning KP Greenman, WH Green, R Gómez-Bombarelli Chemical Science, 2022 | 37 | 2022 |
| Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back BA Koscher, RB Canty, MA McDonald, KP Greenman, CJ McGill, ... Science 382 (6677), eadi1407, 2023 | 26 | 2023 |
| Benchmarking uncertainty quantification for protein engineering KP Greenman, AP Amini, KK Yang bioRxiv, 2023.04. 17.536962, 2023 | 11 | 2023 |
| Lattice-constant and band-gap tuning in wurtzite and zincblende BInGaN alloys K Greenman, L Williams, E Kioupakis Journal of Applied Physics 126 (5), 055702, 2019 | 8 | 2019 |
| Automated patent extraction powers generative modeling in focused chemical spaces A Subramanian, KP Greenman, A Gervaix, T Yang, R Gómez-Bombarelli Digital Discovery 2 (4), 1006-1015, 2023 | 6 | 2023 |
| An Undergraduate-Led, Research-Based Course That Complements a Traditional Chemical Engineering Curriculum S Butrus, K Greenman, E Khera, I Kopyeva, A Nishii Chemical Engineering Education 54 (2), 97-106, 2020 | 1 | 2020 |
| Optical Property Prediction and Molecular Discovery through Multi-Fidelity Deep Learning and Computational Chemistry KP Greenman Massachusetts Institute of Technology, 2024 | | 2024 |
| Multi-Fidelity Deep Learning for Data-Efficient Molecular Property Models from Experimental and Computational Data KP Greenman, T Orkhon, W Green, R Gomez-Bombarelli 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Multi-Fidelity Computer-Aided Molecular Design KP Greenman 2023 AIChE Annual Meeting, 2023 | | 2023 |
| Chemprop: Machine Learning for Molecular Property Prediction C McGill, E Heid, Y Chung, K Greenman, D Graff, M Liu, C Bilodeau, ... 2022 AIChE Annual Meeting, 2022 | | 2022 |
| Design of an Automatic Platform for Machine-Learning Model Based Molecular Property Optimization M McDonald, B Koscher, R Canty, SK Ha, C Bilodeau, KP Greenman, ... 2022 AIChE Annual Meeting, 2022 | | 2022 |
| MULTI-FIDELITY DEEP LEARNING AND ACTIVE LEARNING FOR MOLECULAR OPTICAL PROPERTIES KP Greenman International Symposium on Molecular Spectroscopy, 2022 | | 2022 |
| Transfer Learning for Prediction of Absorption and Emission Spectra from Multi-Fidelity Data KP Greenman, W Green, R Gomez-Bombarelli 2021 AIChE Annual Meeting, 2021 | | 2021 |
| Designing and Synthesizing Novel Dye Molecules Using Generative Modeling and Data-Driven Synthesis Planning C Bilodeau, B Koscher, KP Greenman, R Gómez-Bombarelli, KF Jensen 2021 AIChE Annual Meeting, 2021 | | 2021 |
| Lattice Constant and Band Gap Tuning in BInGaN Alloys for Next-Generation LEDs K Greenman, L Williams, E Kioupakis APS Meeting Abstracts, 2019 | | 2019 |
| Computational Catalysis: Creating a User-Friendly Tool for Research and Education KP Greenman, P Liao 2018 AIChE Annual Meeting, 2018 | | 2018 |
| Computational Catalysis with DFT K Greenman, P Liao nanoHUB, 2018 | | 2018 |
| BInGaN alloys lattice-matched to GaN for high-power high-efficiency visible LEDs L Williams, K Greenman, E Kioupakis APS Meeting Abstracts, 2018 | | 2018 |
William H. GreenHoyt C. Hottel Professor of Chemical Engineering, MIT在 mit.edu 的电子邮件经过验证
