General Science

We aim to transform scientific research itself. Many scientific endeavors can benefit from large scale experimentation, data gathering, and machine learning (including deep learning). We aim to accelerate scientific research by applying Google’s computational power and techniques in areas such as drug discovery, biological pathway modeling, microscopy, medical diagnostics, material science, and agriculture. We collaborate closely with world-class research partners to help solve important problems with large scientific or humanitarian benefit.

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ProtSeq: towards high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes

We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid identifying barcodes on the chip. The end goal is that over multiple binding cycles, a sequential...

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ProtSeq: towards high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes

Jessica Hong, Michael Connor Gibbons, Ali Bashir, Diana Wu, Shirley Shao, Zachary Cutts, Mariya Chavarha, Ye Chen, Lauren Schiff, Mikelle Foster, Victoria Church, Llyke Ching, Sara Ahadi, Anna Hieu-Thao Le, Alexander Tran, Michelle Therese Dimon, Marc Coram, Brian Patrick Williams, Phillip Jess, Marc Berndl, Annalisa Pawlosky

iScience, vol. 25 (2022), pp. 32

SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects

Machine-learned force fields combine the accuracy of ab initio methods with the efficiency of conventional force fields. However, current machine-learned force fields typically ignore electronic degrees of freedom, such as the total charge or spin state, and assume chemical locality, which is problematic when molecules have inconsistent electronic states, or when nonlocal effects play a...

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SpookyNet: Learning Force Fields with Electronic Degrees of Freedom and Nonlocal Effects

Oliver Unke, Stefan Chmiela, Michael Gastegger, Kristof T. Schütt, Huziel Saucceda, Klaus-Robert Müller

Nature Communications, vol. 12 (2021), pp. 7273

Towards understanding retrosynthesis by energy-based models

Retrosynthesis is the process of identifying a set of reactants to synthesize a target molecule. It is critical to material design and drug discovery. Existing machine learning approaches based on language models and graph neural networks have achieved encouraging results. However, the inner connections of these models are rarely discussed, and rigorous evaluations of these models are largely...

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Towards understanding retrosynthesis by energy-based models

Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai

NeurIPS 2021 (2021)

Realizing topologically ordered states on a quantum processor

The discovery of topological order has revolutionized the understanding of quantum matter in modern physics and provided the theoretical foundation for many quantum error correcting codes. Realizing topologically ordered states has proven to be extremely challenging in both condensed matter and synthetic quantum systems. Here, we prepare the ground state of the emblematic toric code Hamiltonian...

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Realizing topologically ordered states on a quantum processor

Kevin Satzinger, Y.-J. Liu, A. Smith, C. Knapp, M. Newman, N. C. Jones, Z. Chen, C. Quintana, X. Mi, A. Dunsworth, C. Gidney, I. Aleiner, F. Arute, K. Arya, J. Atalaya, R. Babbush, J. C. Bardin, R. Barends, J. Basso, A. Bengtsson, A. Bilmes, M. Broughton, B. B. Buckley, D. A. Buell, B. Burkett, N. Bushnell, B. Chiaro, R. Collins, W. Courtney, S. Demura, A. R Derk, D. Eppens, C. Erickson, L. Faoro, E. Farhi, B. Foxen, M. Giustina, A. Greene, J. A. Gross, M. P. Harrigan, S. D. Harrington, J. Hilton, S. Hong, T. Huang, W. J. Huggins, L. B. Ioffe, S. V. Isakov, E. Jeffrey, Z. Jiang, D. Kafri, K. Kechedzhi, T. Khattar, S. Kim, P. V. Klimov, A. N. Korotkov, F. Kostritsa, D. Landhuis, P. Laptev, A. Locharla, E. Lucero, O. Martin, J. R. McClean, M. McEwen, K. C. Miao, M. Mohseni, S. Montazeri, W. Mruczkiewicz, J. Mutus, O. Naaman, M. Neeley, C. Neill, M. Y. Niu, T. E. O'Brien, A. Opremcak, B. Pato, A. Petukhov, N. C. Rubin, D. Sank, V. Shvarts, D. Strain, M. Szalay, B. Villalonga, T. C. White, Z. Yao, P. Yeh, J. Yoo, A. Zalcman, H. Neven, S. Boixo, A. Megrant, Y. Chen, J. Kelly, V. Smelyanskiy, A. Kitaev, M. Knap, F. Pollmann, Pedram Roushan

Science, vol. 374 (2021), pp. 1237-1241

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities

Machine learning has enabled the prediction of quantum chemical properties with high accuracy and efficiency, allowing to bypass computationally costly ab initio calculations. Instead of training on a fixed set of properties, more recent approaches attempt to learn the electronic wavefunction (or density) as a central quantity of atomistic systems, from which all other observables can be...

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SE(3)-equivariant prediction of molecular wavefunctions and electronic densities

Oliver Thorsten Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess Smidt, Klaus-Robert Müller

Advances in Neural Information Processing Systems (2021)

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