Accelerating the discovery of materials for clean energy in the era of smart automation DP Tabor, LM Roch, SK Saikin, C Kreisbeck, D Sheberla, JH Montoya, ... Nat. Rev. Mater. 3, 5-20, 2018 | 673 | 2018 |
Self-driving laboratory for accelerated discovery of thin-film materials BP MacLeod, FGL Parlane, TD Morrissey, F Häse, LM Roch, ... Science Advances 6 (20), eaaz8867, 2020 | 467 | 2020 |
Phoenics: A Bayesian optimizer for chemistry F Häse, LM Roch, C Kreisbeck, A Aspuru-Guzik ACS central science 4 (9), 1134-1145, 2018 | 312 | 2018 |
Next-generation experimentation with self-driving laboratories F Häse, LM Roch, A Aspuru-Guzik Trends in Chemistry 1 (3), 282-291, 2019 | 258 | 2019 |
Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems S Langner, F Häse, J Darío Perea, T Stubhan, J Hauch, LM Roch, ... Advanced Materials, 1907801, 2020 | 220 | 2020 |
ChemOS: orchestrating autonomous experimentation LM Roch, F Häse, C Kreisbeck, T Tamayo-Mendoza, LPE Yunker, ... Science Robotics 3 (19), eaat5559, 2018 | 154 | 2018 |
Data-science driven autonomous process optimization M Christensen, LPE Yunker, F Adedeji, F Häse, LM Roch, T Gensch, ... Communications Chemistry 4 (1), 112, 2021 | 153 | 2021 |
ChemOS: An orchestration software to democratize autonomous discovery LM Roch, F Häse, C Kreisbeck, T Tamayo-Mendoza, LPE Yunker, ... PLoS ONE 15 (4), e0229862, 2020 | 143 | 2020 |
ChemOS: An Orchestration Software to Democratize Autonomous Discovery LM Roch, F Häse, A Aspuru-Guzik Artificial Intelligence in Drug Discovery 75, 351, 2020 | 141 | 2020 |
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories F Häse, LM Roch, A Aspuru-Guzik Chemical science 9 (39), 7642-7655, 2018 | 138 | 2018 |
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge F Häse, M Aldeghi, RJ Hickman, LM Roch, A Aspuru-Guzik Applied Physics Reviews 8 (3), 2021 | 114 | 2021 |
Materials Acceleration Platform: Accelerating Advanced Energy Materials Discovery by Integrating High-Throughput Methods with Artificial Intelligence. A Aspuru-Guzik, K Persson, A Alexander-Katz, C Amador, D Solis-Ibarra, ... http://mission-innovation.net/wp-content/uploads/2018/01/Mission-Innovation …, 2018 | 101 | 2018 |
A Bayesian approach to predict solubility parameters B Sanchez‐Lengeling, LM Roch, JD Perea, S Langner, CJ Brabec, ... Advanced Theory and Simulations 2 (1), 1800069, 2019 | 83 | 2019 |
Olympus: a benchmarking framework for noisy optimization and experiment planning F Häse, M Aldeghi, RJ Hickman, LM Roch, M Christensen, E Liles, ... Machine Learning: Science and Technology 2 (3), 035021, 2021 | 79 | 2021 |
π-Depletion as criterion to predict π-stacking ability J Gonthier, S Steinmann, L Roch, A Ruggi, N Luisier, K Severin, ... Chemical Communications (London) 48 (74), 9239 - 9241, 2012 | 77 | 2012 |
Designing and understanding light-harvesting devices with machine learning F Häse, LM Roch, P Friederich, A Aspuru-Guzik Nature Communications 11 (1), 4587, 2020 | 73 | 2020 |
Interface molecular engineering for laminated monolithic perovskite/silicon tandem solar cells with 80.4% fill factor CO Ramírez Quiroz, GD Spyropoulos, M Salvador, LM Roch, M Berlinghof, ... Advanced Functional Materials 29 (40), 1901476, 2019 | 53 | 2019 |
Pentaindenocorannulene: Properties, Assemblies and C60 Complex S Lampart, LM Roch, AK Dutta, Y Wang, R Warshamanage, AD Finke, ... Angewandte Chemie International Edition 128 (47), 14868 - 14872, 2016 | 50 | 2016 |
Kinetics of the Regeneration by Iodide of Dye Sensitizers Adsorbed on Mesoporous Titania J Teuscher, A Marchioro, J Andrès, LM Roch, M Xu, SM Zakeeruddin, ... Journal of Physical Chemistry C 118 (30), 17108 - 17115, 2014 | 36 | 2014 |
Toward Accurate Adsorption Energetics on Clay Surfaces A Zen, LM Roch, SJ Cox, X Hu, S Sorella, D Alfè, A Michaelides Journal of Physical Chemistry C 120 (46), 26402 - 26413, 2016 | 34 | 2016 |