Wang, Andrew, Alán Aspuru-Guzik, and Geoffrey Ozin. “Challenges and Opportunities for AI in Synthetic Solid-State Inorganic Chemistry.” Matter 7, no. 1 (January 3, 2024): 5–8. https://doi.org/10.1016/j.matt.2023.11.016.
Angello, Nicholas H., Vandana Rathore, Wiktor Beker, Agnieszka Wołos, Edward R. Jira, Rafał Roszak, Tony C. Wu, et al. “Closed-Loop Optimization of General Reaction Conditions for Heteroaryl Suzuki-Miyaura Coupling.” Science 378, no. 6618 (October 28, 2022): 399–405. https://doi.org/10.1126/science.adc8743.
Flam-Shepherd, D., Zhu, K., & Aspuru-Guzik, A. (2022). Language models can learn complex molecular distributions. Nature Communications, 13(1), 3293. https://doi.org/10.1038/s41467-022-30839-x
Pauric Bannigan, , Matteo Aldeghi, Zeqing Bao, Florian Häse, Alán Aspuru-Guzik, and Christine Allen. "Machine learning directed drug formulation development".Advanced Drug Delivery Reviews 175 (2021): 113806.
M. Seifrid, R. J. Hickman, A. Aguilar-Granda, C. Lavigne, J. Vestfrid, T. C. Wu, T. Gaudin, E. J. Hopkins, A. Aspuru-Guzik, Routescore: Punching the Ticket to More Efficient Materials Development. ACS Cent. Sci. 8, 122–131 (2022).
Tao, Huachen, Tianyi Wu, Matteo Aldeghi, Tony C. Wu, Alán Aspuru-Guzik, and Eugenia Kumacheva. “Nanoparticle Synthesis Assisted by Machine Learning.” Nature Reviews Materials, July 13, 2021, 1–16. https://doi.org/10.1038/s41578-021-00337-5.
Cynthia Shen and Mario Krenn and Sagi Eppel and Alán Aspuru-Guzik, . "Deep Molecular Dreaming: Inverse machine learning for de-novo molecular design and interpretability with surjective representations".CoRR abs/2012.09712 (2020).
Eppel, Sagi, Haoping Xu, and Alan Aspuru-Guzik. “Computer Vision for Liquid Samples in Hospitals and Medical Labs Using Hierarchical Image Segmentation and Relations Prediction.” ArXiv:2105.01456 [Cs], May 4, 2021. http://arxiv.org/abs/2105.01456.
Chermoshentsev, Dmitry A., Aleksei O. Malyshev, Egor S. Tiunov, Douglas Mendoza, Alán Aspuru-Guzik, Aleksey K. Fedorov, and Alexander I. Lvovsky. “Polynomial Unconstrained Binary Optimisation Inspired by Optical Simulation.” ArXiv:2106.13167 [Nlin, Physics:Quant-Ph], June 24, 2021. http://arxiv.org/abs/2106.13167.
Friederich, Pascal, Florian Häse, Jonny Proppe, and Alán Aspuru-Guzik. “Machine-Learned Potentials for next-Generation Matter Simulations.” Nature Materials 20, no. 6 (June 2021): 750–61. https://doi.org/10.1038/s41563-020-0777-6.
Cervera-Lierta, Alba, Jakob S. Kottmann, and Alán Aspuru-Guzik. “Meta-Variational Quantum Eigensolver: Learning Energy Profiles of Parameterized Hamiltonians for Quantum Simulation.” PRX Quantum 2, no. 2 (May 28, 2021): 020329. https://doi.org/10.1103/PRXQuantum.2.020329.
Friederich, Pascal, Mario Krenn, Isaac Tamblyn, and Alán Aspuru-Guzik. “Scientific Intuition Inspired by Machine Learning-Generated Hypotheses.” Machine Learning: Science and Technology 2, no. 2 (June 1, 2021): 025027. https://doi.org/10.1088/2632-2153/abda08.
Kottmann, Jakob S., and Alán Aspuru-Guzik. “Optimized Low-Depth Quantum Circuits for Molecular Electronic Structure Using a Separable Pair Approximation.” ArXiv:2105.03836 [Physics, Physics:Quant-Ph], May 9, 2021. http://arxiv.org/abs/2105.03836.
Hamza Jnane, , Nicolas P. D. Sawaya, Borja Peropadre, Alan Aspuru-Guzik, Raul Garcia-Patron, and Joonsuk Huh. "Analog quantum simulation of non-Condon effects in molecular spectroscopy." ACS Photonics (2020).
Gensch, Tobias, Gabriel dos Passos Gomes, Pascal Friederich, Ellyn Peters, Theophile Gaudin, Robert Pollice, Kjell Jorner, et al. “A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis,” April 29, 2021. https://doi.org/10.26434/chemrxiv.12996665.v1.
Alba Cervera-Lierta, , Mario Krenn, Alán Aspuru-Guzik, and Alexey Galda. "Experimental high-dimensional Greenberger-Horne-Zeilinger entanglement with superconducting transmon qutrits." (2021).
Daniel Flam-Shepherd, , Tony C Wu, and Alan Aspuru-Guzik. "MPGVAE : Improved Generation of Small Organic Molecules using Message Passing Neural Nets".Machine Learning: Science and Technology (2021).
Anand, Abhinav, Jonathan Romero, Matthias Degroote, and Alán Aspuru-Guzik. “Noise Robustness and Experimental Demonstration of a Quantum Generative Adversarial Network for Continuous Distributions.” Advanced Quantum Technologies 4, no. 5 (2021): 2000069. https://doi.org/10.1002/qute.202000069.
Menke, T., Häse, F., Gustavsson, S. et al. Automated design of superconducting circuits and its application to 4-local couplers. npj Quantum Inf7, 49 (2021).
Riley J. Hickman, , Florian Häse, Loïc M. Roch, and Alán Aspuru-Guzik. "Gemini: Dynamic Bias Correction for Autonomous Experimentation and Molecular Simulation." (2021).
Matteo Aldeghi, , Florian Häse, Riley J. Hickman, Isaac Tamblyn, and Alán Aspuru-Guzik. "Golem: An algorithm for robust experiment and process optimization." (2021).
AkshatKumar Nigam, , Robert Pollice, Matthew F. D. Hurley, Riley J. Hickman, Matteo Aldeghi, Naruki Yoshikawa, Seyone Chithrananda, Vincent A. Voelz, and Alán Aspuru-Guzik. "Assigning Confidence to Molecular Property Prediction." (2021).
Pollice, Robert, Gabriel, Passos Gomes, Matteo, Aldeghi, Riley J., Hickman, Mario, Krenn, Cyrille, Lavigne, Michael, Lindner-D’Addario, AkshatKumar, Nigam, Cher Tian, Ser, Zhenpeng, Yao, and Alán, Aspuru-Guzik. "Data-Driven Strategies for Accelerated Materials Design".Accounts of Chemical Research
M. Seifrid, A. Aspuru-Guzik, You Wouldn’t Download a Molecule! Now, ChemSCAD Makes It Possible. ACS Cent. Sci. 7, 228–230 (2021).
Seifrid, Martin, and Alán, Aspuru-Guzik. "You Wouldn’t Download a Molecule! Now, ChemSCAD Makes It Possible".ACS Central Science
Kishor Bharti, , Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, and Alán Aspuru-Guzik. "Noisy intermediate-scale quantum (NISQ) algorithms." (2021).
Kottmann, Jakob S., Philipp, Schleich, Teresa, Tamayo-Mendoza, and Alán, Aspuru-Guzik. "Reducing Qubit Requirements while Maintaining Numerical Precision for the Variational Quantum Eigensolver: A Basis-Set-Free Approach".The Journal of Physical Chemistry Letters 12, no.1 (2021): 663-673.
Nigam, AkshatKumar, Robert, Pollice, Mario, Krenn, Gabriel dos Passos, Gomes, and Alán, Aspuru-Guzik. "Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES".Chem. Sci. 12 (2021): 7079-7090.
Luca A. Thiede, , Mario Krenn, AkshatKumar Nigam, and Alan Aspuru-Guzik. "Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning." (2020).
Sorli, Jeni C., Pascal, Friederich, Benjamin, Sanchez-Lengeling, Nicholas C., Davy, Guy Olivier, Ngongang Ndjawa, Hannah L., Smith, Xin, Lin, Steven A., Lopez, Melissa L., Ball, Antoine, Kahn, Alán, Aspuru-Guzik, and Yueh-Lin, Loo. "Coronene derivatives for transparent organic photovoltaics through inverse materials design".J. Mater. Chem. C 9 (2021): 1310-1317.
Pascal Friederich, , Salvador Leon, José Dario Perea, Loïc M Roch, and Alán Aspuru-Guzik. "The influence of sorbitol doping on aggregation and electronic properties of PEDOT:PSS: a theoretical study".Machine Learning: Science and Technology 2, no.1 (2020): 01LT01.
Abhinav Anand, , Matthias Degroote, and Alán Aspuru-Guzik. "Natural Evolutionary Strategies for Variational Quantum Computation." (2020).
Griffen, Ed, et al. “Chapter 12. AI via Matched Molecular Pair Analysis.” Drug Discovery, edited by Nathan Brown, Royal Society of Chemistry, 2020, pp. 250–71. DOI.org (Crossref), doi:10.1039/9781788016841-00250.
Jakob S. Kottmann, , Sumner Alperin-Lea, Teresa Tamayo-Mendoza, Alba Cervera-Lierta, Cyrille Lavigne, Tzu-Ching Yen, Vladyslav Verteletskyi, Philipp Schleich, Abhinav Anand, Matthias Degroote, Skylar Chaney, Maha Kesibi, Artur F. Izmaylov, and Alán Aspuru-Guzik. "Tequila: A platform for rapid development of quantum algorithms." (2020).
Kottmann, Jakob S., Abhinav, Anand, and Alán, Aspuru-Guzik. "A feasible approach for automatically differentiable unitary coupled-cluster on quantum computers".Chem. Sci. (2021): -.
Zhao, Chenglong, Qidi, Wang, Zhenpeng, Yao, Jianlin, Wang, Benjam\'\in, Sánchez-Lengeling, Feixiang, Ding, Xingguo, Qi, Yaxiang, Lu, Xuedong, Bai, Baohua, Li, Hong, Li, Alán, Aspuru-Guzik, Xuejie, Huang, Claude, Delmas, Marnix, Wagemaker, Liquan, Chen, and Yong-Sheng, Hu. "Rational design of layered oxide materials for sodium-ion batteries".Science 370, no.6517 (2020): 708–711.
Christensen, Melodie; Yunker, Lars; Adedeji, Folarin; Häse, Florian; Roch, Loic; Gensch, Tobias; et al. (2020): Data-science driven autonomous process optimization. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.13146404.v1
Robert Pollice, , Pascal Friederich, Cyrille Lavigne, Gabriel dos Passos Gomes, and Alán Aspuru-Guzik. "Organic molecules with inverted gaps between first excited singlet and triplet states and appreciable fluorescence rates".Matter (2021).
Rosen, Andrew; Iyer, Shaelyn; Ray, Debmalya; Yao, Zhenpeng; Aspuru-Guzik, Alan; Gagliardi, Laura; et al. (2020): Machine Learning the Quantum-Chemical Properties of Metal–Organic Frameworks for Accelerated Materials Discovery with a New Electronic Structure Database. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.13147616.v1
Pascal Friederich, , Mario Krenn, Isaac Tamblyn, and Alan Aspuru-Guzik. "Scientific intuition inspired by machine learning generated hypotheses." (2020).
Lavigne, Cyrille; dos Passos Gomes, Gabriel; Pollice, Robert; Aspuru-Guzik, Alan (2020): Automatic discovery of chemical reactions using imposed activation. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.13008500.v1
Florian Häse, , Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein, and Alán Aspuru-Guzik. "Olympus: a benchmarking framework for noisy optimization and experiment planning." (2020).
Häse, F., Roch, L.M., Friederich, P. et al. Designing and understanding light-harvesting devices with machine learning. Nat Commun11, 4587 (2020).
Eppel, Sagi, Haoping, Xu, Mor, Bismuth, and Alan, Aspuru-Guzik. "Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set".ACS Central Science.
Zhong, Junjie, Jason, Riordon, Tony C., Wu, Harrison, Edwards, Aaron R., Wheeler, Keith, Pardee, Alán, Aspuru-Guzik, and David, Sinton. "When robotics met fluidics".Lab Chip 20 (2020): 709-716.
Zi-Jian Zhang, , Thi Ha Kyaw, Jakob S. Kottmann, Matthias Degroote, and Alan Aspuru-Guzik. "Mutual information-assisted Adaptive Variational Quantum Eigensolver Ansatz Construction." (2020).
Yao, Zhenpeng; Sanchez-Lengeling, Benjamin; Bobbitt, N. Scott; Bucior, Benjamin J.; Kumar, Sai Govind Hari; Collins, Sean P.; et al. (2020): Inverse Design of Nanoporous Crystalline Reticular Materials with Deep Generative Models. Nature Machine Intelligence (2020) In press. https://doi.org/10.26434/chemrxiv.12186681.v2
dos Passos Gomes, Gabriel; Pollice, Robert; Aspuru-Guzik, Alan (2020): Navigating through the Maze of Homogeneous Catalyst Design with Machine Learning. Trends in Chemistry. https://doi.org/10.26434/chemrxiv.12786722.v1
Proppe, A.H., Li, Y.C., Aspuru-Guzik, A. et al. Bioinspiration in light harvesting and catalysis. Nat Rev Mater (2020).
Mills, L. Reginald, John J., Monteith, Gabriel, Passos Gomes, Alán, Aspuru-Guzik, and Sophie A. L., Rousseaux. "The Cyclopropane Ring as a Reporter of Radical Leaving-Group Reactivity for Ni-Catalyzed C(sp3)–O Arylation".Journal of the American Chemical Society
Yury N. Lebedev, Chirag Apte, Susan Cheng, Cyrille Lavigne, Alan Lough, Dwight S. Seferos, Alán Aspuru-Guzik, and Andrei K Yudin
Journal of the American Chemical Society Just Accepted Manuscript
DOI: 10.1021/jacs.0c05410
Florian Häse, , Ignacio Fdez. Galván, Alán Aspuru-Guzik, Roland Lindh, and Morgane Vacher. "Machine learning for analysing ab initio molecular dynamics simulations".Journal of Physics: Conference Series 1412 (2020): 042003.
Martha M. Flores-Leonar, , Luis M. Mejía-Mendoza, Andrés Aguilar-Granda, Benjamin Sanchez-Lengeling, Hermann Tribukait, Carlos Amador-Bedolla, and Alán Aspuru-Guzik. "Materials Acceleration Platforms: on the way to autonomous experimentation".Current Opinion in Green and Sustainable Chemistry (2020): 100370.
Jakob S. Kottmann, Mario Krenn, Thi Ha Kyaw, Sumner Alperin-Lea, & Alán Aspuru-Guzik. (2020). Quantum Computer-Aided design of Quantum Optics Hardware.
Thi Ha Kyaw, Tim Menke, Sukin Sim, Nicolas P. D. Sawaya, William D. Oliver, Gian Giacomo Guerreschi, & Alán Aspuru-Guzik. (2020). Quantum computer-aided design: digital quantum simulation of quantum processors.
Abhinav Anand, Jonathan Romero, Matthias Degroote, & Alán Aspuru-Guzik. (2020). Experimental demonstration of a quantum generative adversarial network for continuous distributions.
Phillip Wagner Kastberg Jensen, , Lasse Bjørn Kristensen, Jakob S. Kottmann, and Alan Aspuru-Guzik. "Quantum computation of Eigenvalues within target intervals".Quantum Science and Technology (2020).
Mario Krenn, , Jakob Kottmann, Nora Tischler, and Alán Aspuru-Guzik. "Conceptual understanding through efficient inverse-design of quantum optical experiments." (2020).
E. N. Muratov, J. Bajorath, R. P. Sheridan, I. V. Tetko, D. Filimonov, V. Poroikov, T. I. Oprea, I. I. Baskin, A. Varnek, A. Roitberg, O. Isayev, S. Curtalolo, D. Fourches, Y. Cohen, A. Aspuru-Guzik, D. A. Winkler, D. Agrafiotis, A. Cherkasov and A. Tropsha, QSAR without borders, Chemical Society Reviews, Advance Article (2020)
L. M. Roch, S. K. Saikin, F. Häse, P. Friederich, R. H. Goldsmith, S. León and A. Aspuru-Guzik, From Absorption Spectra to Charge Transfer in Nanoaggregates of Oligomers with Machine Learning, ACS Nano, (2020), In Press
G. Sivaraman, N. Jackson, B. Sanchez-Lengeling, A. Vasquez-Mayagoitia, A. Aspuru-Guzik, V. Vishwanath and J. de Pablo, A machine learning workflow for molecular analysis: application to melting points, Machine Learning: Science and Technology, (2020), In Press
F. Häse, T. Tamayo-Mendoza, C. Boixo, J. Romero, L. Roch and A. Aspuru-Guzik, Autonomous Titration for Chemistry Classrooms: Preparing Students for Digitized Chemistry Laboratories, ChemRxiv: 12097908 (2020)
P. Friederich, G. dos Passos Gomes, R. De Bin, A. Aspuru-Guzik and D. Balcells, Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex, Chemical Science, Advance Article, (2020), In Press
S. Kim, J. Noh, G. Ho Gu, A. Aspuru-Guzik and Y. Jung, Generative Adversarial Networks for Crystal Structure Prediction, arXiv: 2004.01396 (2020)
S. McArdle, S. Endo, A. Aspuru-Guzik, S. C. Benjamin and X. Yuan, Quantum computational chemistry, Reviews of Modern Physics 92, 015003 (2020)
F. Häse, L. M. Roch and A. Aspuru-Guzik, Gryffin: An algorithm for Bayesian optimization for categorical variables informed by physical intuition with applications to chemistry, arXiv: 2003.12127 (2020)
Daniel Flam-Shepherd, , Tony Wu, Pascal Friederich, and Alan Aspuru-Guzik. "Neural Message Passing on High Order Paths." (2020).
S. Langner, F. Häse, J. Darío Perea, T. Stubhan, J. Hauch, L. M. Roch, T. Heumueller, A. Aspuru‐Guzik and C. J. Brabec, Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems, Advanced Materials 32, 1907801 (2020)
S. Vargas, S. Zamirpour, S. Menon, A. Rothman, F. Häse, T. Tamayo-Mendoza, J. Romero, S. Sim, T. Menke and A. Aspuru-Guzik, Team-Based Learning for Scientific Computing and Automated Experimentation: Visualization of Colored Reactions, Journal of Chemical Education 97, 689-694 (2020)
G. C. Sedenho, D. De Porcellinis, Y. Jing, E. Kerr, L. M. Mejia-Mendoza, Á. Vazquez-Mayagoitia, A. Aspuru-Guzik, R. G. Gordon, F. N. Crespilho and M. J. Aziz, Effect of Molecular Structure of Quinones and Carbon Electrode Surfaces on the Interfacial Electron Transfer Process, ACS Applied Energy Materials 3, 1933-1943 (2020)
N. Sawaya, T. Menke, T. H. Kyaw, S. Johri, A. Aspuru-Guzik and G. Giacomo Guerreschi, Resource-efficient digital quantum simulation of d-level systems for photonic, vibrational, and spin-s Hamiltonians, npj Quantum Information, (2020), In Press
C. Chuang, D. I.G. Bennett, J. R. Caram, A. Aspuru-Guzik, M. G. Bawendi, and J. Cao, Generalized Kasha’s Model: T-Dependent Spectroscopy Reveals Short-Range Structures of 2D Excitonic Systems, Chem 5, 3135–3150, (2019)
Tim Menke, , Florian Häse, Simon Gustavsson, Andrew J. Kerman, William D. Oliver, and Alán Aspuru-Guzik. "Automated discovery of superconducting circuits and its application to 4-local coupler design." (2019).
P. Friederich, G. dos Passos Gomes, R. De Bin, A. Aspuru-Guzik and D. Balcells, Machine Learning Reactivity in the Chemical Space Surrounding Vaska's Complex, ChemRxiv: 10347566, (2019)
P. S. Emani, J. Warrell, A. Anticevic, S. Bekiranov, M. Gandal, M. J. McConnell, G. Sapiro, A. Aspuru-Guzik, J. Baker, M. Bastiani, P. McClure, J. Murray, S. N Sotiropoulos, J. Taylor, G. Senthil, T. Lehner, M. B. Gerstein and A. W. Harrow, Quantum Computing at the Frontiers of Biological Sciences, arXiv: 1911.07127, (2019)
C. Zhao, Z. Yao, J. Wang, Y. Lu, X. Bai, A. Aspuru-Guzik, L. Chen and Y. Hu, Ti Substitution Facilitating Oxygen Oxidation in Na2/3 Mg1/3 Ti1/6 Mn1/2 O2 Cathode, Chem 5, 2913−2925, (2019)
J. Noh, J. Kim, H. S. Stein, B. Sanchez-Lengeling, J. M. Gregoire, A. Aspuru-Guzik and Y. Jung, Inverse Design of Solid-State Materials via a Continuous Representation, Matter 1, 1−15, (2019)
R. Gómez-Bombarelli, and A. Aspuru-Guzik, Computational Discovery of Organic LED Materials. In Computational Materials Discovery, edited by Artem Oganov, Gabriele Saleh, and Alexander Kvashnin. Ch. 13, Royal Chemistry Society, 423,(2018)
B. Sanchez-Lengeling, J. N. Wei, B. K. Lee, R. C. Gerkin, A. Aspuru-Guzik and A. B. Wiltschko, Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules, arXiv: 1910.10685, (2019)
S. Sim, P. D. Johnson and A. Aspuru‐Guzik, Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms, Advanced Quantum Technologies, 1900070, (2019)
Pagano, Philip, Qi, Guo, Chethya, Ranasinghe, Evan, Schroeder, Kevin, Robben, Florian, Häse, Hepeng, Ye, Kyle, Wickersham, Alán, Aspuru-Guzik, Dan T., Major, Lokesh, Gakhar, Amnon, Kohen, and Christopher M., Cheatum. "Oscillatory Active-Site Motions Correlate with Kinetic Isotope Effects in Formate Dehydrogenase".ACS Catalysis 9, no.12 (2019): 11199-11206.
A. Nigam, P. Friederich, M. Krenn and A. Aspuru-Guzik, Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space, ICLR Conference, (2020)
C. F. Perkinson, D. P. Tabor, M. Einzinger, D. Sheberla, H. Utzat, T. Lin, D. N. Congreve, M. G. Bawendi, A. Aspuru-Guzik, and M. A. Baldo, Discovery of blue singlet exciton fission molecules via a high-throughput virtual screening and experimental approach, The Journal of Chemical Physics 151, 121102, (2019)
A. Zhavoronkov, Y. A. Ivanenkov, A. Aliper, M. S. Veselov, V. A. Aladinskiy, A. V. Aladinskaya, V. A. Terentiev, D. A. Polykovskiy, M. D. Kuznetsov, A. Asadulaev, Y. Volkov, A. Zholus, R. R. Shayakhmetov, A. Zhebrak, L. I. Minaeva, B. A. Zagribelnyy, L. H. Lee, R. Soll, D. Madge, L. Xing, T. Guo and A. Aspuru-Guzik, Deep learning enables rapid identification of potent DDR1 kinase inhibitors, Nature Biotechnology 37, 1038−1040, (2019)
Y. Cao, J. Romero, J. P. Olson, M. Degroote, P. D. Johnson, M. Kieferová, I. D. Kivlichan, T. Menke, B. Peropadre, N. P. D. Sawaya, S. Sim, L. Veis and A. Aspuru-Guzik, Quantum Chemistry in the Age of Quantum Computing, Chemical Reviews 119, 10856−10915, (2019)
P. Dallaire-Demers, J. Romero, L. Veis, S. Sim, and A. Aspuru-Guzik, Low-depth circuit ansatz for preparing correlated fermionic states on a quantum computer, Quantum Science and Technology 4, 045005, (2019)
B. Bucior, A. Rosen, M. Haranczyk, Z. Yao, M. Ziebel, O. Farha, J. Hupp, J. Siepmann, A. Aspuru-Guzik, and R. Snurr, Identification Schemes for Metal–Organic Frameworks to Enable Rapid Search and Cheminformatics Analysis, Crystal Growth & Design 19, 6682−6697, (2019)
C. O. R. Quiroz, G. D. Spyropoulos, M. Salvador, L. M. Roch, M. Berlinghof , J. D. Perea, K. Forberich, L. I. Dion-Bertrand, N. Schrenker, A. Classen, N. Gasparini, G. Chistiakova, M. Mews, L. Korte, B. Rech, N. Li, F. Hauke, E. Spiecker, T. Ameri, S. Albrecht, G. Abellán, S. León, T. Unruh, A. Hirsch, A. Aspuru-Guzik, and C. J. Brabec, Interface Molecular Engineering for Laminated Monolithic Perovskite/Silicon Tandem Solar Cells with 80.4% Fill Factor, Advanced Functional Materials 29, 1901476 (2019)
X. Wang, Z. Yao, S. Hwang, Y. Pan, H. Dong, M. Fu, N. Li, K. Sun, H. Gan, Y. Yao, A. Aspuru-Guzik, Q. Xu, and D. Su, In Situ Electron Microscopy Investigation of Sodiation of Titanium Disulfide Nanoflakes, ACS Nano 13, 9421−9430, (2019)
D. P. Tabor, V. Chiykowski, P. Friederich, Y. Cao, D. Dvorak, C. P. Berlinguette and A. Aspuru Guzik, Design rules for high mobility xanthene-based hole transport materials, Chemical Science 10, 8360-8366 (2019)
L. Bjørn Kristensen, M. Degroote, P. Wittek, A. Aspuru-Guzik and N. T. Zinner, An Artificial Spiking Quantum Neuron, arXiv: 1907.06269, (2019)
B. P. MacLeod, F. G. L. Parlane, T. D. Morrissey, F. Häse, L. M. Roch, K. E. Dettelbach, R. Moreira, L. P. E. Yunker, M. B. Rooney, J. R. Deeth, V. Lai, G. J. Ng, H. Situ, R. H. Zhang, M. S. Elliott, T. H. Haley, D. J. Dvorak, A. Aspuru-Guzik, J. E. Hein and C. P. Berlinguette, Self-driving laboratory for accelerated discovery of thin-film materials, Science Advances 6. 20(2020)
A. Jinich, B. Sanchez-Lengeling, H. Ren, R. Harman, and A. Aspuru-Guzik, A mixed quantum chemistry/machine learning approach for the fast and accurate prediction of biochemical redox potentials and its large-scale application to 315,000 redox reactions, ACS Central Science 5, 1199−1210, (2019)
https://pubs.acs.org/doi/10.1021/acs.jctc.9b00126
D. Rappoport and A. Aspuru-Guzik, Predicting Feasible Organic Reaction Pathways Using Heuristically Aided Quantum Chemistry, Journal of Chemical Theory and Computation 15, 4099-4112 (2019)
M. Krenn, F. Häse, A. Nigam, P. Friederich and A. Aspuru-Guzik, Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation, Mach. Learn.: Sci. Technol.1 045024
A. Jinich, B. Sanchez-Lengeling, H. Ren, J. E. Goldford, E. Noor, J. N. Sanders, D. Segrè and A. Aspuru-Guzik, A thermodynamic atlas of carbon redox chemical space, bioRxiv: 245811, (2019)
D. Tabor, R. Gómez-Bombarelli, L. Tong, R. G. Gordon, M. J. Aziz and A. Aspuru-Guzik, Mapping the frontiers of quinone stability in aqueous media: implications for organic aqueous redox flow batteries, Journal of Materials Chemistry A 7, 12833-12841 (2019)
S. Y. Guo, P. Friederich, Y. Cao, T. Wu, C. Forman, D. Mendoza, M. Degroote, A. Cavell, V. Krasecki, R. Hickman, A. Sharma, L. Cronin, N. Gianneschi, R. Goldsmith and A. Aspuru-Guzik, A Molecular Computing Approach to Solving Optimization Problems via Programmable Microdroplet Arrays, ChemRxiv: 10250897, (2019)
M. Goulet, L. Tong, D. A. Pollack, D. P. Tabor, S. A. Odom, A. Aspuru-Guzik, E. E. Kwan, R. G. Gordon and M. J. Aziz, Extending the Lifetime of Organic Flow Batteries via Redox State Management, Extending the Lifetime of Organic Flow Batteries via Redox State Management, Journal of the American Chemical Society 141, 8014-8019 (2019)
A. Aspuru-Guzik, M. Baik, S. Balasubramanian, R. Banerjee, S. Bart, N. Borduas-Dedekind, S. Chang, P. Chen, C. Corminboeuf, F. Coudert, L. Cronin, C. Crudden, T. Cuk, A. G. Doyle, C. Fan, X. Feng, D. Freedman, S. Furukawa, S. Ghosh, F. Glorius, M. Jeffries-EL, N. Katsonis, A. Li, S. S. Linse, S. Marchesan, N. Maulide, A. Milo, A. R. H. Narayan, P. Naumov, C. Nevado, T. Nyokong, R. Palacin, M. Reid, C. Robinson, G. Robinson, R. Sarpong, C. Schindler, G. S. Schlau-Cohen, T. W. Schmidt, R. Sessoli, Y. Shao-Horn, H. Sleiman, J. Sutherland, A. Taylor, A. Tezcan, M. Tortosa, A. Walsh, A. J. B. Watson, B. M. Weckhuysen, E. Weiss, D. Wilson, V. W.-W. Yam, X. Yang, J. Y. Ying, T. Yoon, S. You, A. J. G. Zarbin and H. Zhang, Charting a course for chemistry, Nature Chemistry 11, 286–294 (2019)
F. Häse, L. M. Roch and A. Aspuru-Guzik, Next-Generation Experimentation with Self-Driving Laboratories, Trends in Chemistry 1, 282-291 (2019)
Kivlichan, Ian D., Craig, Gidney, Dominic W., Berry, Nathan, Wiebe, Jarrod, McClean, Wei, Sun, Zhang, Jiang, Nicholas, Rubin, Austin, Fowler, Alán, Aspuru-Guzik, Hartmut, Neven, and Ryan, Babbush. "Improved Fault-Tolerant Quantum Simulation of Condensed-Phase Correlated Electrons via Trotterization".Quantum 4 (2020): 296.
Z. Yao, V. I. Hegde, A. Aspuru‐Guzik and C. Wolverton, Discovery of Calcium‐Metal Alloy Anodes for Reversible Ca‐Ion Batteries, Advanced Energy Materials 9, 1802994 (2019)
P. W.K. Jensen, C. Jin, P. Dallaire-Demers, A. Aspuru-Guzik, and G. C. Solomon, Molecular Realization of a Quantum NAND Tree, Quantum Science Technology 4, 015013, (2018)
J. Romero and A. Aspuru-Guzik, Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions, arXiv: 1901.00848, (2019)
E. R. Anschuetz, J. P. Olson, A. Aspuru-Guzik and Y. Cao, Variational Quantum Factoring, Lecture Notes in Computer Science Ch. 7, 11413 (2019)
Y. Cao, J. Romero, and A. Aspuru-Guzik, Potential of quantum computing for drug discovery, IBM Journal of Research and Development, 62, 1–6, (2018)
F. Häse, I. Fdez. Galván, A. Aspuru-Guzik, R. Lindh and M. Vacher, How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry, Chemical Science 10, 2298-2307 (2019)
Y. Cao, , J. Romero, and A. Aspuru-Guzik. "Potential of quantum computing for drug discovery".IBM Journal of Research and Development 62, no.6 (2018): 6:1-6:20.
E. S. Fried, N. P. D. Sawaya, Y. Cao, I. D. Kivlichan, J. Romero, and A. Aspuru-Guzik, qTorch: The Quantum Tensor Contraction Handler, PLoS One 13, e0208510, (2018)
D. Polykovskiy, A. Zhebrak, B. Sanchez-Lengeling, S. Golovanov, O. Tatanov, S. Belyaev, R. Kurbanov, A. Artamonov, V. Aladinskiy, M. Veselov, A. Kadurin, S. Johansson, H. Chen, S. Nikolenko, A. Aspuru-Guzik and A. Zhavoronkov, Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models, arXiv: 1811.12823, (2018)
S. K. Saikin, C. Kreisbeck, D. Sheberla, J. S. Becker and A. Aspuru-Guzik, Closed-loop discovery platform integration is needed for artificial intelligence to make an impact in drug discovery, Expert Opinion on Drug Discovery 14, 1–4 (2019)
D. Hinton, J. Ng, J. Sun, S. Lee, S. Saikin, J. Logsdon, D. White, A. Marquard, A. Cavell, K. Knapper, K. Lupo, M. Wasielewski, A. Aspuru-Guzik, J. Biteen, P. Gopalan, and R. Goldsmith, Mapping Forbidden Emission to Structure in Self-Assembled Organic Nanoparticles, Journal of the American Chemical Society 140, 15827–15841, (2018)
A. Jinich, A. Flamholz, H. Ren, S. Kim, B. Sanchez-Lengeling, C. A. R. Cotton, E. Noor, A. Aspuru-Guzik, and A. Bar-Even, Quantum chemistry reveals thermodynamic principles of redox biochemistry, PLoS Computational Biology 14, e1006471, (2018)
S. Sim, Y. Cao, J. Romero, P. D. Johnson and A. Aspuru-Guzik, A framework for algorithm deployment on cloud-based quantum computers, arXiv: 1810.10576, (2018)
K. Alberi, M. B. Nardelli, A. Zakutayev, L. Mitas, S. Curtarolo, A. Jain, M. Fornari, N. Marzari, I. Takeuchi, M. L Green, M. Kanatzidis, M. F Toney, S. Butenko, B. Meredig, S. Lany, U. Kattner, A. Davydov, E. S Toberer, V. Stevanovic, A. Walsh, N. Park, A. Aspuru-Guzik, D. P Tabor, J. Nelson, J. Murphy, A. Setlur, J. Gregoire, H. Li, R. Xiao, A. Ludwig, L. W Martin, A. M Rappe, S. Wei and J. Perkins, The 2019 materials by design roadmap, Journal of Physics D: Applied Physics 52, 013001 (2019)
F. Häse, L. Roch, and A. Aspuru-Guzik, Chimera: Enabling Hierarchy Based Multi-Objective Optimization for Self-Driving Laboratories, Chemical Science 9, 7642–7655, (2018)
J. Romero, R. Babbush, J. R. McClean, C. Hempel, P. Love, and A. Aspuru-Guzik, Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz, Quantum Science and Technology 4, 014008, (2018)
L. A. Pachon, A. Relaño, B. Peropadre, and A. Aspuru-Guzik, Origin of the 1/fα-Spectral-Noise in Chaotic and Regular Quantum Systems, Physical Review E 98, 042213, (2018)
V. A. Chiykowski, Y. Cao, H. Tan, D. P. Tabor, E. H. Sargent, A. Aspuru-Guzik, and C. Berlinguette, Precise Control of Thermal and Redox Properties of Organic Hole-Transport Materials, Angewandte Chemie 130, 15755–15759, (2018)
David G. Kwabi, Kaixiang Lin, Yunlong Ji, Emily F. Kerr, Marc-Antoni Goulet, Diana D. Porcellinis, Daniel P. Tabor, Daniel A. Pollack, Alan Aspuru-Guzik, Roy G. Gordon, and Michael J. Aziz, Alkaline Quinone Flow Battery with Long Lifetime at pH 12, Joule 2, 1894–1906, (2018)
B. Sanchez-Lengeling, L. Roch, J. D. Perea, S. Langner, C. J. Brabec, and A. Aspuru-Guzik, A Bayesian Approach to Predict Solubility Parameters, Advanced Theory and Simulations 2, 1800069, (2018)
F. Häse, L. M. Roch, C. Kreisbeck, and A. Aspuru-Guzik, Phoenics: A Bayesian Optimizer for Chemistry, ACS Central Science 4, 1134–1145, (2018)
B. Sanchez-Lengeling, and A. Aspuru-Guzik, Inverse molecular design using machine learning: Generative models for matter engineering, Science 361, 360, (2018)
C. Hempel, C. Maier, J. Romero, J. McClean, T. Monz, H. Shen, P. Jurcevic, B. Lanyon, P. Love, R. Babbush, A. Aspuru-Guzik, R. Blatt, and C. Roos, Quantum chemistry calculations on a trapped-ion quantum simulator, Physical Review X 8, 031022, (2018)
R. Gómez-Bombarelli, and A. Aspuru-Guzik, Machine Learning and Big-Data in Computational Chemistr,. In: Andreoni W., Yip S. (eds) Handbook of Materials Modeling, Springer, Cham. (2018)
L. M. Roch, F. Häse, C. Kreisbeck, T. Tamayo-Mendoza, L. P. E. Yunker, J. E. Hein, and A. Aspuru-Guzik, ChemOS: Orchestrating autonomous experimentation, Science Robotics 3, aat5559, (2018)
N. P. D. Sawaya, D. Rappoport, D. Tabor, and A. Aspuru-Guzik, Excitonics: A Set of Gates for Molecular Exciton Processing and Signaling, Nano Letters. 12, 6410, (2018)
T. Tamayo-Mendoza, C. Kreisbeck, R. Lindh, and A. Aspuru-Guzik. Automatic differentiation in quantum chemistry with an application to fully variational Hartree-Fock. ACS Cent. Sci. 4 (2018) 559
D. P. Tabor, L. M. Roch, S. K. Saikin, C. Kreisbeck, D. Sheberla, J. H. Montoya, S. Dwaraknath, M. Aykol, C. Ortiz, H. Tribukait, C. Amador-Bedolla, C. J. Brabec, B. Maruyama, K. A. Persson, and A. Aspuru-Guzik, Accelerating the discovery of materials for clean energy in the era of smart automation, Nature Materials 3, 5, (2018)
D. Gelbwaser-Klimovsky, A. Bylinskii, D. Gangloff, R. Islam, A. Aspuru-Guzik, and V. Vuletic, Single-atom heat machines enabled by energy quantization, Physical Review Letters 120, 170601, (2018)
D. I. G. Bennett, P. Malý, C. Kreisbeck, R. van Grondelle, and A. Aspuru-Guzik, Mechanistic Regimes of Vibronic Transport in a Heterodimer and the Design Principle of Incoherent Vibronic Transport in Phycobiliproteins, The Journal of Physical Chemistry Letters 9, 2665, (2018)
E. Putin, A. Asadulaev, Y. Ivanenkov, V. Aladinsky, B. Sánchez-Lengeling, A. Aspuru-Guzik, A. Zhavoronkov. Reinforced Adversarial Neural Computer for De Novo Molecular Design. Journal of Chemical Information and Modelling 58, 1194-1204, (2018)
S. M. Blau, D. I. G. Bennett, C. Kreisbeck, G. D. Scholes, and A. Aspuru-Guzik, Local protein solvation drives direct down-conversion in phycobiliprotein PC645 via incoherent vibronic transport, Proceedings of the National Academy of Sciences 115, E3342–E3350, (2018)
I. D. Kivlichan, J. McClean, N. Wiebe, C. Gidney, A. Aspuru-Guzik, G. K. Chan, and R. Babbush, Quantum Simulation of Electronic Structure with Linear Depth and Connectivity, Physical Review Letters 120, 11501, (2018)
V. Sundar, D. Gelbwaser-Klimovsky, and A. Aspuru-Guzik, Reproducing Quantum Probability Distributions at the Speed of Classical Dynamics: A New Approach for Developing Force-Field Functors, The Journal of Physical Chemistry Letters 9, 1721, (2018)
L. M. Roch, F. Häse, C. Kreisbeck, T. Tamayo-Mendoza, L. P. E. Yunker, J. E. Hein and A. Aspuru-Guzik, ChemOS: An Orchestration Software to Democratize Autonomous Discovery, ChemRxiv: 5953606, (2018)
L. Kringle, N. P. D. Sawaya, J. Widom, C. Adams, M. G. Raymer, A. Aspuru-Guzik, and A. H. Marcus, Temperature-dependent conformations of exciton-coupled Cy3 dimers in double-stranded DNA, The Journal of Chemical Physics 148, 085101, (2018)
S. Sim, J. Romero, P. D. Johnson, and A. Aspuru-Guizik, Viewpoint: Quantum Computer Simulates Excited States of Molecule, Physics 11, 14, (2018)
A. Aspuru-Guzik, R. Lindh, and M. Reiher, The Matter Simulation (R)evolution, ACS Central Science, 4, 144, (2018)
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. Miguel Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, A. Aspuru-Guzik, Automatic chemical design using a data-driven continuous representation of molecules, ACS Central Science 4, 268, (2018)
J. Ly, S. Lopez, J. Lin, L. Zhang, A. Aspuru-Guzik, K. Houk, and A. Briseno, The Oxidation of Rubrene Revisited: Unexpected Intramolecular Rearrangement, Journal of Materials Chemistry C, 6, 3757, (2018)
P. Pagano, Qi Guo, F. Häse, H. Ye, K. Wickersham, A. Aspuru-Guzik, D. T. Major, L. Gakhar, A. Kohen, and C. M. Cheatum, Oscillatory Active-site Motions Correlate with Kinetic Isotope Effects in Enzymes, Submitted, (2018)
R. Babbush, D. W Berry, Y. R Sanders, I. D Kivlichan, A. Scherer, A. Y Wei, P. J Love and A. Aspuru-Guzik, Exponentially more precise quantum simulation of fermions in the configuration interaction representation, Quantum Science and Technology 3, 015006 (2017)
P. De Luna, J. Wei, Y. Bengio, A. Aspuru-Guzik and E. Sargent, Use machine learning to find energy materials, Nature 552, 23 (2017)
Z. Yang, L. Tong, D. P. Tabor, E. S. Beh, M. Goulet, D. De Porcellinis, A. Aspuru-Guzik, R. G. Gordon, and M. J. Aziz, Alkaline benzoquinone aqueous flow battery for large-scale storage of electrical energy, Advanced Energy Materials 8, 1870034, (2018)
Y. Cao, G. G. Guerreschi and A. Aspuru-Guzik, Quantum Neuron: an elementary building block for machine learning on quantum computers, arXiv: 1711.11240, (2017)
L. Tong, Q. Chen, A. A. Wong, R. Gómez-Bombarelli, A. Aspuru-Guzik, R. G. Gordon and M. J. Aziz, UV-Vis spectrophotometry of quinone flow battery electrolyte for in situ monitoring and improved electrochemical modeling of potential and quinhydrone formation, Physical Chemistry Chemical Physics 19, 31684-31691 (2017)
E. Boulais, N. P.D. Sawaya, R. Veneziano, A. Andreoni, S. Lin, N. Woodbury, H. Yan, A. Aspuru-Guzik, and M. Bathe, Programmed coherent coupling in a DNA-based excitonic circuit, Nature Materials 17, 159, (2018)
P. D. Johnson, J. Romero, J. Olson, Y. Cao and A. Aspuru-Guzik, QVECTOR: an algorithm for device-tailored quantum error correction, arXiv: 1711.02249, (2017)
S. A. Lopez, B. Sanchez-Lengeling, J. de Goes Soares and A. Aspuru-Guzik, Design Principles and Top Non-Fullerene Acceptor Candidates for Organic Photovoltaics, Joule 1, 857-870 (2017)
S. K. Saikin, M. A. Shakirov, C. Kreisbeck, U. Peskin, Y. N. Proshin and A. Aspuru-Guzik, On the Long-Range Exciton Transport in Molecular Systems: The Application to H-Aggregated Heterotriangulene Chains, The Journal of Physical Chemistry C 121, 24994-25002 (2017)
F. Häse, C. Kreisbeck and A. Aspuru-Guzik, Machine learning for quantum dynamics: deep learning of excitation energy transfer properties, Chemical Science 8, 8419-8426 (2017)
S. Valleau, R. A. Studer, F. Häse, C. Kreisbeck, R. G. Saer, R. E. Blankenship, E. I. Shakhnovich and A. Aspuru-Guzik, Absence of Selection for Quantum Coherence in the Fenna–Matthews–Olson Complex: A Combined Evolutionary and Excitonic Study, ACS Central Science 3, 1086-1095 (2017)
J. Romero, J. P Olson and A. Aspuru-Guzik, Quantum autoencoders for efficient compression of quantum data, Quantum Science and Technology 2, 045001 (2017)
B. Sanchez-Lengeling, C. Outeiral, G. L. Guimaraes and A. Aspuru-Guzik, Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC), ChemRxiv: 5309668, (2017)
D. Coles, L. C. Flatten, T. Sydney, E. Hounslow, S. K. Saikin, A. Aspuru‐Guzik, V. Vedral, J. Kuo‐Hsiang Tang, R. A. Taylor, J. M. Smith and D. G. Lidzey, A Nanophotonic Structure Containing Living Photosynthetic Bacteria, Small nano micro 13, 1701777 (2017)
J. D. Perea, S. Langner, M. Salvador, B. Sanchez-Lengeling, N. Li, C. Zhang, G. Jarvas, J. Kontos, A. Dallos, A. Aspuru-Guzik and C. J. Brabec, Introducing a New Potential Figure of Merit for Evaluating Microstructure Stability in Photovoltaic Polymer-Fullerene Blends, The Journal of Physical Chemistry C 121, 18153-18161 (2017)
J. Goodknight and A. Aspuru-Guzik, Taking six-dimensional spectra in finite time, Science 356, 1333 (2017)
I. D Kivlichan, N. Wiebe, R. Babbush and A. Aspuru-Guzik, Bounding the costs of quantum simulation of many-body physics in real space, Journal of Physics A: Mathematical and Theoretical 50, 305301 (2017)
J. Olson, Y. Cao, J. Romero, P. Johnson, P. Dallaire-Demers, N. Sawaya, P. Narang, I. Kivlichan, M. Wasielewski and A. Aspuru-Guzik, Quantum Information and Computation for Chemistry, arXiv: 1706.05413, (2017)
G. L. Guimaraes, B. Sanchez-Lengeling, C. Outeiral, P. L. C. Farias and A. Aspuru-Guzik, Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models, arXiv: 1705.10843, (2017)
M. Baghbanzadeh, C. M. Bowers, D. Rappoport, T. Żaba, L. Yuan, K. Kang, K. Liao, M. Gonidec, P. Rothemund, P. Cyganik, A. Aspuru-Guzik and G. M. Whitesides, Anomalously Rapid Tunneling: Charge Transport across Self-Assembled Monolayers of Oligo(ethylene glycol), Journal of the American Chemical Society 139, 7624-7631 (2017)
B. Sánchez-Lengeling and A. Aspuru-Guzik, Learning More, with Less, ACS Central Science 3, 275-277 (2017)
B. Peropadre, A. Aspuru-Guzik and J. José García-Ripoll, Equivalence between spin Hamiltonians and boson sampling, Physical Review A 95, 032327 (2017)
S. Kim, A. Jinich and A. Aspuru-Guzik, MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes, Journal of Chemical Information and Modeling 57, 657-668 (2017)
A. J. Wagner, D. Yu. Zubarev, A. Aspuru-Guzik and D. G. Blackmond, Chiral Sugars Drive Enantioenrichment in Prebiotic Amino Acid Synthesis, ACS Central Science 3, 322-328 (2017)
S. Mostame, J. Huh, C. Kreisbeck, A. J. Kerman, T. Fujita, A. Eisfeld and A. Aspuru-Guzik, Emulation of complex open quantum systems using superconducting qubits, Quantum Information Processing 16, 44 (2017)
G. D. Scholes, G. R. Fleming, L. X. Chen, A. Aspuru-Guzik, A. Buchleitner, D. F. Coker, G. S. Engel, R. van Grondelle, A. Ishizaki, D. M. Jonas, J. S. Lundeen, J. K. McCusker, S. Mukamel, J. P. Ogilvie, A. Olaya-Castro, M. A. Ratner, F. C. Spano, K. B. Whaley and X. Zhu, Using coherence to enhance function in chemical and biophysical systems, Nature 543, 647–656 (2017)
A. Aspuru-Guzik, L’aube des robots chimistes autonomes, La Recherche 529, (2017).
J. M. Hernández-Lobato, J. Requeima, E. O. Pyzer-Knapp and A. Aspuru-Guzik, Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space, Proceedings of the 34th International Conference on Machine Learning, PMLR 70, 1470-1479 (2017)
J. T. Blaskovits, T. Bura, S. Beaupré, S. A. Lopez, C. Roy, J. de Goes Soares, A. Oh, J. Quinn, Y. Li, A. Aspuru-Guzik and M. Leclerc, A Study of the Degree of Fluorination in Regioregular Poly(3-hexylthiophene), Macromolecules 50, 162-174 (2017)
M. R. Gerhardt, L. Tong, R. Gómez‐Bombarelli, Q. Chen, M. P. Marshak, C. J. Galvin, A. Aspuru‐Guzik, R. G. Gordon and M. J. Aziz, Anthraquinone Derivatives in Aqueous Flow Batteries, Advanced Energy Materials 7, 1601488 (2017)
D. Gelbwaser-Klimovsky, S. K. Saikin, R. H. Goldsmith and A. Aspuru-Guzik, Optical Spectra of p-Doped PEDOT Nanoaggregates Provide Insight into the Material Disorder, ACS Energy Letters 1, 1100-1105 (2016)
D. Gelbwaser-Klimovsky and A. Aspuru-Guzik, On thermodynamic inconsistencies in several photosynthetic and solar cell models and how to fix them, Chemical Science 8, 1008-1014 (2017)
J. N. Wei, D. Duvenaud and A. Aspuru-Guzik, Neural Networks for the Prediction of Organic Chemistry Reactions, ACS Central Science 2, 725-732 (2016)
A. Bernstein, E. H. Sargent, A. Aspuru-Guzik, R. Cogdell, G. R. Fleming, R. Van Grondelle and M. Molina, Renewables need a grand-challenge strategy, Nature 538, 30 (2016)
B. Peropadre, G. G. Guerreschi, J. Huh and A. Aspuru-Guzik, Proposal for Microwave Boson Sampling, Physical Review Letters 117, 140505 (2016)
H. J. Park, M. C. So, D. Gosztola, G. P. Wiederrecht, J. D. Emery, A. B. F. Martinson, S. Er, C. E. Wilmer, N. A. Vermeulen, A. Aspuru-Guzik, J. F. Stoddart, O. K. Farha and J. T. Hupp, Layer-by-Layer Assembled Films of Perylene Diimide- and Squaraine-Containing Metal–Organic Framework-like Materials: Solar Energy Capture and Directional Energy Transfer, ACS Applied Materials and Interfaces 8, 24983-24988 (2016)
D. Jasrasaria, E. O. Pyzer-Knapp, D. Rappoport and A. Aspuru-Guzik, Space-Filling Curves as a Novel Crystal Structure Representation for Machine Learning Models, arXiv: 1608.05747, (2016)
D. González Olivares, B. Peropadre, A. Aspuru-Guzik and J. J. García-Ripoll, Quantum simulation with a boson sampling circuit, Physical Review A 94, 022319 (2016)
P.J.J. O’Malley, R. Babbush, I.D. Kivlichan, J. Romero, J.R. McClean, R. Barends, J. Kelly, P. Roushan, A. Tranter, N. Ding, B. Campbell, Y. Chen, Z. Chen, B. Chiaro, A. Dunsworth, A.G. Fowler, E. Jeffrey, E. Lucero, A. Megrant, J.Y. Mutus, M. Neeley, C. Neill, C. Quintana, D. Sank, A. Vainsencher, J. Wenner, T.C. White, P.V. Coveney, P.J. Love, H. Neven, A. Aspuru-Guzik and J.M. Martinis, Scalable Quantum Simulation of Molecular Energies, Physical Review X 6, 031007 (2016)
S. Mandrà, G. G. Guerreschi and A. Aspuru-Guzik, Faster than classical quantum algorithm for dense formulas of exact satisfiability and occupation problems, New Journal of Physics 18, 073003 (2016)
N. P. D. Sawaya, M.l Smelyanskiy,J. R. McClean and A. Aspuru-Guzik, Error Sensitivity to Environmental Noise in Quantum Circuits for Chemical State Preparation, Journal of Chemical Theory and Computation 12, 3097-3108 (2016)
T. Markovich, M. A. Blood-Forsythe, D. Rappoport, D. Kim and A. Aspuru-Guzik, Calibration of the Many-Body Dispersion Range-Separation Parameter, arXiv: 1605.04987 (2016)
T. Markovich, S. M. Blau, J. N. Sanders and A. Aspuru‐Guzik, Benchmarking compressed sensing, super‐resolution, and filter diagonalization, International Journal of Quantum Chemistry 116, 1097-1106 (2016)
C. M. Bowers, D. Rappoport, M. Baghbanzadeh, F. C. Simeone, K. Liao, S. N. Semenov, T. Żaba, P. Cyganik, A. Aspuru-Guzik and G. M. Whitesides, Tunneling across SAMs Containing Oligophenyl Groups, The Journal of Physical Chemistry C 120, 11331-11337 (2016)
R. Olivares‐Amaya, A. Jinich, M. A Watson and A. Aspuru-Guzik, GPU Acceleration of Second‐Order Møller–Plesset Perturbation Theory with Resolution of Identity, In Electronic Structure Calculations on Graphics Processing Units: From Quantum Chemistry to Condensed: edited by Ross C. Walker, and Andreas W. Goetz, John Wiley & Sons, 259-278 (2016)
F. Häse, S. Valleau, E. Pyzer-Knappa and A. Aspuru-Guzik, Machine learning exciton dynamics, Chemical Science 7, 5139-5147 (2016)
R. Babbush, D. W Berry, I. D Kivlichan, A. Y Wei, P. J Love and A. Aspuru-Guzik, Exponentially more precise quantum simulation of fermions in second quantization, New Journal of Physics 18, 033032 (2016)
T. Fujita, S. Atahan-Evrenk, N. P. D. Sawaya and A. Aspuru-Guzik, Coherent Dynamics of Mixed Frenkel and Charge-Transfer Excitons in Dinaphtho[2,3-b:2′3′-f]thieno[3,2-b]-thiophene Thin Films: The Importance of Hole Delocalization, Journal of Physical Chemistry Letters 7, 1374-1380 (2016)
C. Kreisbeck and A. Aspuru-Guzik, Efficiency of energy funneling in the photosystem II supercomplex of higher plants, Chemical Science 7, 4174-4183 (2016)
E. O. Pyzer-Knapp, G. N. Simmb and A. Aspuru Guzik, A Bayesian approach to calibrating high-throughput virtual screening results and application to organic photovoltaic materials, Materials Horizons 3, 226-233 (2016)
J. R McClean, J. Romero, R. Babbush and A. Aspuru-Guzik, The theory of variational hybrid quantum-classical algorithms, New Journal of Physics 18, 023023 (2016)
M. Smelyanskiy, N. P. D. Sawaya and A. Aspuru-Guzik, qHiPSTER: The Quantum High Performance Software Testing Environment, arXiv: 1601.07195 (2016)
X. Andrade and A. Aspuru-Guzik, Application of Graphics Processing Units to Accelerate Real‐Space Density Functional Theory and Time‐Dependent Density Functional Theory Calculations, In Electronic Structure Calculations on Graphics Processing Units: From Quantum Chemistry to Condensed: edited by Ross C. Walker, and Andreas W. Goetz, John Wiley & Sons, 211-238 (2016)
M. G. Moebius, F. Herrera, S. Griesse-Nascimento, O. Reshef, C. C. Evans, G. G. Guerreschi, A. Aspuru-Guzik and E. Mazur, Efficient photon triplet generation in integrated nanophotonic waveguides, Optics express 24, 9932-9954 (2016)
K. Lin, R. Gómez-Bombarelli, E. S. Beh, L. Tong, Q. Chen, A. Valle, A. Aspuru-Guzik, M. J. Aziz and R. G. Gordon, A redox-flow battery with an alloxazine-based organic electrolyte, Nature Energy 1, 16102 (2016)
T. Markovich, S. M. Blau, J. Parkhill, C. Kreisbeck, J. N. Sanders, X. Andrade and A. Aspuru-Guzik, Accelerating the computation of bath spectral densities with super-resolution, Theoretical Chemistry Accounts 135, 215 (2016)
S. A. Lopez, E. O. Pyzer-Knapp, G. N. Simm, T. Lutzow, K. Li, L. R. Seress, J. Hachmann and A. Aspuru-Guzik, The Harvard organic photovoltaic dataset, Scientific Data 3, 160086 (2016)
R.l Gómez-Bombarelli, J. Aguilera-Iparraguirre, T. D. Hirzel, D. Duvenaud, D. Maclaurin, M. A. Blood-Forsythe, H. S. Chae, M. Einzinger, D. Ha, T. Wu, G. Markopoulos, S. Jeon, H. Kang, H. Miyazaki, M. Numata, S. Kim, W. Huang, S. I. Hong, M. Baldo, R. P. Adams and A. Aspuru-Guzik, Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach, Nature Materials 15, 1120–1127 (2016)
J. Yuen-Zhou, J. J. Krich, I. Kassal and A. Aspuru-Guzik, Ultrafast Spectroscopy: Quantum Information and Wave Packets, In Ultrafast Dynamics at the Nanoscale: Biomolecules and Supramolecular Assemblies: edited by Stefan Haacke, and Irene Burghardt, Pan Stanford, 437-465 (2016)
R. Gómez-Bombarelli and A. Aspuru-Guzik, Computers design next-generation OLED molecules, SPIE Newsroom, (2016)
S. Mandrà, G. G. Guerreschi, and A. Aspuru-Guzik, Adiabatic quantum optimization in the presence of discrete noise: Reducing the problem dimensionality, Physical Review A 92 , (2015)
H. G. Laguna, R. P. Sagar, D. G. Tempeld and A. Aspuru-Guzik, The role of interparticle interaction and environmental coupling in a two-particle open quantum system, Physical Chemistry Chemical Physics 18, 436-447 (2016)
J. R. McClean and A. Aspuru-Guzik, Compact wavefunctions from compressed imaginary time evolution, RSC Advances 5, 102277-102283 (2015)
E. O. Pyzer-Knapp, K. Li, and A. Aspuru-Guzik, Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery, Advanced Functional Materials 25, 6495-6502 (2015)
M. A. Blood-Forsythe, T. Markovich, R. A. DiStasio, Jr., R. Car and A. Aspuru-Guzik, Analytical nuclear gradients for the range-separated many-body dispersion model of noncovalent interactions, Chemical Science 7, 1712-1728 (2016)
M. Baghbanzadeh, C. M. Bowers, D. Rappoport, T. Żaba, M. Gonidec, M. H. Al-Sayah, P. Cyganik, A. Aspuru-Guzik, and G. M. Whitesides, Charge Tunneling along Short Oligoglycine Chains, Angewandte Chemie International Edition 54, 14743–14747(2015)
J. Huh, G. G. Guerreschi, B. Peropadre, J. R. McClean, and A. Aspuru-Guzik, Boson sampling for molecular vibronic spectra, Nature Photonics 9, 615-620(2015)
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D. Gelbwaser-Klimovsky and A. Aspuru-Guzik, Strongly Coupled Quantum Heat Machines, Journal of Physical Chemistry Letters 6, 3477-3482 (2015)
S. Chandrasekaran, M. Aghtar, S. Valleau, A. Aspuru-Guzik, and U. Kleinekathoefer, Influence of Force Fields and Quantum Chemistry Approach on Spectral Densities of BChl a in Solution and in FMO Proteins, Journal of Physical Chemistry B 119, 9995-10004 (2015)
L. A. Pachon, A. H. Marcus, and A. Aspuru-Guzik, Quantum process tomography by 2D fluorescence spectroscopy, Journal of Chemical Physics 142, 212442(2015)
Y. Wang, F. Dolde, J. Biamonte, R. Babbush, V. Bergholm, S. Yang, I. Jakobi, P. Neumann, A. Aspuru-Guzik, J. D. Whitfield, and J. Wrachtrup, Quantum Simulation of Helium Hydride Cation in a Solid-State Spin Register, ACS Nano 9, 7769-7774 (2015)
S. Losilla, M. A. Watson, A. Aspuru-Guzik, and D. Sundholm, Construction of the Fock Matrix on a Grid-Based Molecular Orbital Basis Using GPGPUs, Journal of Chemical Theory and Computation 11, 2053-2062 (2015)
J. N. Sanders, X. Andrade, and A. Aspuru-Guzik, Compressed Sensing for the Fast Computation of Matrices: Application to Molecular Vibrations, ACS Central Science 1, 24-32 (2015)
E. O. Pyzer-Knapp, C. Suh, R. Gómez-Bombarelli, J. Aguilera-Iparraguirre, and A. Aspuru-Guzik, What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery, Annual Reviews of Materials Science 45, 196-216 (2015)
X. Andrade, D. Strubbe, U. De Giovannini, A. H. Larsen, M. J. T. Oliveira, J. Alberdi-Rodriguez, A. Varas, I. Theophilou, N. Helbig, M. Verstraete, L. Stella, F. Nogueira, A. Aspuru-Guzik, A. Castro, M. A. L. Marques, and A. Rubio, Real-space grids and the Octopus code as tools for the development of new simulation approaches for electronic systems, Physical Chemistry Chemical Physics 17, 31371-31396 (2015)
N. P.D. Sawaya, J. Huh, S. K. Saikin, T. Fujita, and A. Aspuru-Guzik, Fast Delocalization Leads To Robust Long-Range Excitonic Transfer in a Large Quantum Chlorosome Model, Nano Letters 15, 1722-1729 (2015)
R. Babbush, J. McClean, D. Wecker, A. Aspuru-Guzik, and N. Wiebe, Chemical basis of Trotter-Suzuki errors in quantum chemistry simulation, Physical Review A 91, 022311 (2015)
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K. Hongo, M. Watson, T. Iitaka, A. Aspuru-Guzik, and R. Maezono, Diffusion Monte Carlo Study of Para-Diiodobenzene Polymorphism Revisited, Journal of Chemical Theory and Computation 11, 907-917 (2015)
Bryan O'Gorman, Alejandro Perdomo-Ortiz, Ryan Babbush, Alán Aspuru-Guzik, and Vadim Smelyanskiy, Bayesian network structure learning using quantum annealing, European Physical Journal Special Topics 224, 163-188 (2015)
D. Y. Zubarev, D. Rappoport, and A. Aspuru-Guzik, Uncertainty of Prebiotic Scenarios: The Case of the Non-Enzymatic Reverse Tricarboxylic Acid Cycle, Scientific Reports 5, 8009 (2015)
J. R. McClean and A. Aspuru-Guzik, Clock quantum Monte Carlo technique: An imaginary-time method for real-time quantum dynamics, Physical Review A 91, 012311 (2015)
D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, Convolutional Networks on Graphs for Learning Molecular Fingerprints, Advances in Neural Information Processing Systems 28, 2215-2223 (2015)
A. Johnson, J. Yuen-Zhou, A. Aspuru-Guzik, and J. Krich, Practical witness for electronic coherences , Journal of Chemical Physics 141, 244109(2014)
J. Huh, S. Mostame, T. Fujita, M. Yung, and A. Aspuru-Guzik, Linear-algebraic bath transformation for simulating complex open quantum systems, The New Journal of Physics 16, 123008(2014)