no code implementations • 26 Feb 2024 • Naixu Guo, Zhan Yu, Matthew Choi, Aman Agrawal, Kouhei Nakaji, Alán Aspuru-Guzik, Patrick Rebentrost

Generative machine learning methods such as large-language models are revolutionizing the creation of text and images.

1 code implementation • 13 Feb 2024 • Mohammad Ghazi Vakili, Christoph Gorgulla, AkshatKumar Nigam, Dmitry Bezrukov, Daniel Varoli, Alex Aliper, Daniil Polykovsky, Krishna M. Padmanabha Das, Jamie Snider, Anna Lyakisheva, Ardalan Hosseini Mansob, Zhong Yao, Lela Bitar, Eugene Radchenko, Xiao Ding, Jinxin Liu, Fanye Meng, Feng Ren, Yudong Cao, Igor Stagljar, Alán Aspuru-Guzik, Alex Zhavoronkov

The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology.

1 code implementation • 7 Feb 2024 • Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alán Aspuru-Guzik, Geoff Pleiss

Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior domain knowledge into efficient exploration of a large molecular space.

no code implementations • 13 Jan 2024 • Kourosh Darvish, Marta Skreta, Yuchi Zhao, Naruki Yoshikawa, Sagnik Som, Miroslav Bogdanovic, Yang Cao, Han Hao, Haoping Xu, Alán Aspuru-Guzik, Animesh Garg, Florian Shkurti

Despite the many benefits incurred by the integration of advanced and special-purpose lab equipment, many aspects of experimentation are still manually conducted by chemists, for example, polishing an electrode in electrochemistry experiments.

1 code implementation • 21 Nov 2023 • Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina, Maksim Kuznetsov, Daniil Polykovskiy, Annika Brundyn, Aastha Jhunjhunwala, Anthony Costa, Alex Aliper, Alán Aspuru-Guzik, Alex Zhavoronkov

Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions.

no code implementations • 20 Oct 2023 • Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alán Aspuru-Guzik, Yoshua Bengio

Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule.

1 code implementation • 17 Oct 2023 • Austin Cheng, Alston Lo, Santiago Miret, Brooks Pate, Alán Aspuru-Guzik

KREED's top-1 predictions identify the correct 3D structure with >98% accuracy on the QM9 and GEOM datasets when provided with substitution coordinates of all heavy atoms with natural isotopic abundance.

no code implementations • 16 Aug 2023 • Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik

However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary.

1 code implementation • 17 Jul 2023 • Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.

no code implementations • 9 May 2023 • Daniel Flam-Shepherd, Alán Aspuru-Guzik

In doing so, we demonstrate that it is not necessary to use simplified molecular representations to train chemical language models -- that they are powerful generative models capable of directly exploring chemical space in three dimensions for very different structures.

1 code implementation • 7 Feb 2023 • Alston Lo, Robert Pollice, AkshatKumar Nigam, Andrew D. White, Mario Krenn, Alán Aspuru-Guzik

String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted into machine learning pipelines.

1 code implementation • NeurIPS 2023 • Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Samuel Stanton, Gary Tom, Bojana Rankovic, Yuanqi Du, Arian Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Durholt, Saudamini Chaurasia, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang

By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.

no code implementations • 27 Nov 2022 • Luca Thiede, Chong Sun, Alán Aspuru-Guzik

In this paper, we introduce four main novelties: First, we present a new way of handling the topology problem of normalizing flows.

1 code implementation • 23 Nov 2022 • Austin Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, Alán Aspuru-Guzik

We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees.

no code implementations • 6 Jul 2022 • Abhinav Anand, Jakob S. Kottmann, Alán Aspuru-Guzik

As we continue to find applications where the currently available noisy devices exhibit an advantage over their classical counterparts, the efficient use of quantum resources is highly desirable.

no code implementations • 4 Apr 2022 • Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, AkshatKumar Nigam, Zhenpeng Yao, Alán Aspuru-Guzik

Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein.

1 code implementation • 31 Mar 2022 • Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F. Lameiro, Dominik Lemm, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew White, Adamo Young, Rose Yu, Alán Aspuru-Guzik

We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.

1 code implementation • 29 Mar 2022 • Riley J. Hickman, Matteo Aldeghi, Florian Häse, Alán Aspuru-Guzik

The tools developed constitute a simple, yet versatile strategy to enable model-based optimization with known experimental constraints, contributing to its applicability as a core component of autonomous platforms for scientific discovery.

no code implementations • 1 Feb 2022 • Daniel Flam-Shepherd, Alexander Zhigalin, Alán Aspuru-Guzik

We introduce a novel RL framework for scalable 3D design that uses a hierarchical agent to build molecules by placing molecular substructures sequentially in 3D space, thus attempting to build on the existing human knowledge in the field of molecular design.

1 code implementation • 6 Dec 2021 • Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik

In this work, we investigate the capacity of simple language models to learn distributions of molecules.

1 code implementation • 20 Oct 2021 • Matthew Choi, Daniel Flam-Shepherd, Thi Ha Kyaw, Alán Aspuru-Guzik

The core objective of machine-assisted scientific discovery is to learn physical laws from experimental data without prior knowledge of the systems in question.

1 code implementation • 27 Sep 2021 • Alba Cervera-Lierta, Mario Krenn, Alán Aspuru-Guzik

In this work, we propose the use of logic AI for the design of optical quantum experiments.

1 code implementation • 5 Mar 2021 • Riley J. Hickman, Florian Häse, Loïc M. Roch, Alán Aspuru-Guzik

We recommend using Gemini for regression tasks with sparse data and in an autonomous workflow setting where its predictions of expensive to evaluate objectives can be used to construct a more informative acquisition function, thus reducing the number of expensive evaluations an optimizer needs to achieve desired target values.

1 code implementation • 5 Mar 2021 • Matteo Aldeghi, Florian Häse, Riley J. Hickman, Isaac Tamblyn, Alán Aspuru-Guzik

Design of experiment and optimization algorithms are often adopted to solve these tasks efficiently.

1 code implementation • 23 Feb 2021 • AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley, Riley J. Hickman, Matteo Aldeghi, Naruki Yoshikawa, Seyone Chithrananda, Vincent A. Voelz, Alán Aspuru-Guzik

Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design.

no code implementations • 21 Jan 2021 • 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, Alán Aspuru-Guzik

We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.

no code implementations • 30 Nov 2020 • Abhinav Anand, Matthias Degroote, Alán Aspuru-Guzik

We implement two specific approaches, the exponential and separable natural evolutionary strategies, for parameter optimization of PQCs and compare them against standard gradient descent.

1 code implementation • 11 Nov 2020 • Jakob S. Kottmann, Abhinav Anand, Alán Aspuru-Guzik

We show that, within our framework, the gradient of an expectation value with respect to a parameterized n-fold fermionic excitation can be evaluated by four expectation values of similar form and size, whereas most standard approaches based on the direct application of the parameter-shift-rule come with an associated cost of O(2^(2n)) expectation values.

Quantum Physics Chemical Physics Computational Physics

4 code implementations • 5 Nov 2020 • 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, Alán Aspuru-Guzik

As in classical computing, heuristics play a crucial role in the development of new quantum algorithms, resulting in high demand for flexible and reliable ways to implement, test, and share new ideas.

Quantum Physics Chemical Physics Computational Physics

no code implementations • 3 Nov 2020 • Tony C. Wu, Daniel Flam-Shepherd, Alán Aspuru-Guzik

This paper focuses on Bayesian Optimization in combinatorial spaces.

1 code implementation • 8 Oct 2020 • Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Melodie Christensen, Elena Liles, Jason E. Hein, Alán Aspuru-Guzik

Experiment planning strategies based on off-the-shelf optimization algorithms can be employed in fully autonomous research platforms to achieve desired experimentation goals with the minimum number of trials.

3 code implementations • 28 Sep 2020 • Alba Cervera-Lierta, Jakob S. Kottmann, Alán Aspuru-Guzik

We present the meta-VQE, an algorithm capable to learn the ground state energy profile of a parametrized Hamiltonian.

Quantum Physics

1 code implementation • 6 Aug 2020 • Jakob S. Kottmann, Philipp Schleich, Teresa Tamayo-Mendoza, Alán Aspuru-Guzik

We present a basis-set-free approach to the variational quantum eigensolver using an adaptive representation of the spatial part of molecular wavefunctions.

Quantum Physics Chemical Physics Computational Physics

2 code implementations • 4 Jun 2020 • Jakob S. Kottmann, Mario Krenn, Thi Ha Kyaw, Sumner Alperin-Lea, Alán Aspuru-Guzik

It is not clear how the full potential of large quantum systems can be exploited.

Quantum Physics Computational Physics Optics

no code implementations • 2 Jun 2020 • Abhinav Anand, Jonathan Romero, Matthias Degroote, Alán Aspuru-Guzik

In this paper, we employ a hybrid architecture for quantum generative adversarial networks (QGANs) and study their robustness in the presence of noise.

BIG-bench Machine Learning
Generative Adversarial Network
**+1**

no code implementations • 13 May 2020 • Mario Krenn, Jakob Kottmann, Nora Tischler, Alán Aspuru-Guzik

Here we present Theseus, an efficient algorithm for the design of quantum experiments, which we use to solve several open questions in experimental quantum optics.

Quantum Physics Optics

no code implementations • 26 Mar 2020 • Florian Häse, Matteo Aldeghi, Riley J. Hickman, Loïc M. Roch, Alán Aspuru-Guzik

Leveraging domain knowledge in the form of physicochemical descriptors, Gryffin can significantly accelerate the search for promising molecules and materials.

2 code implementations • 6 Dec 2019 • Tim Menke, Florian Häse, Simon Gustavsson, Andrew J. Kerman, William D. Oliver, Alán Aspuru-Guzik

Superconducting circuits have emerged as a promising platform to build quantum processors.

Quantum Physics

no code implementations • 23 Oct 2019 • Benjamin Sanchez-Lengeling, Jennifer N. Wei, Brian K. Lee, Richard C. Gerkin, Alán Aspuru-Guzik, Alexander B. Wiltschko

Based on these early results with graph neural networks for molecular properties, we hope machine learning can eventually do for olfaction what it has already done for vision and hearing.

2 code implementations • ICLR 2020 • AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik

Challenges in natural sciences can often be phrased as optimization problems.

1 code implementation • 8 Sep 2019 • Stefan Langner, Florian Häse, José Darío Perea, Tobias Stubhan, Jens Hauch, Loïc M. Roch, Thomas Heumueller, Alán Aspuru-Guzik, Christoph J. Brabec

Fundamental advances to increase the efficiency as well as stability of organic photovoltaics (OPVs) are achieved by designing ternary blends which represents a clear trend towards multi-component active layer blends.

Applied Physics

no code implementations • 14 Jul 2019 • Lasse Bjørn Kristensen, Matthias Degroote, Peter Wittek, Alán Aspuru-Guzik, Nikolaj T. Zinner

Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing.

2 code implementations • 31 May 2019 • Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

SELFIES can be directly applied in arbitrary machine learning models without the adaptation of the models; each of the generated molecule candidates is valid.

9 code implementations • 24 Dec 2018 • Yudong Cao, Jonathan Romero, Jonathan P. Olson, Matthias Degroote, Peter D. Johnson, Mária Kieferová, Ian D. Kivlichan, Tim Menke, Borja Peropadre, Nicolas P. D. Sawaya, Sukin Sim, Libor Veis, Alán Aspuru-Guzik

Practical challenges in simulating quantum systems on classical computers have been widely recognized in the quantum physics and quantum chemistry communities over the past century.

Quantum Physics

2 code implementations • 27 Aug 2018 • Eric R. Anschuetz, Jonathan P. Olson, Alán Aspuru-Guzik, Yudong Cao

In this work, we revisit the problem of factoring, developing an alternative to Shor's algorithm, which employs established techniques to map the factoring problem to the ground state of an Ising Hamiltonian.

Quantum Physics

1 code implementation • 4 Jan 2018 • Florian Häse, Loïc M. Roch, Christoph Kreisbeck, Alán Aspuru-Guzik

In this work we introduce PHOENICS, a probabilistic global optimization algorithm combining ideas from Bayesian optimization with concepts from Bayesian kernel density estimation.

2 code implementations • 30 Nov 2017 • Yudong Cao, Gian Giacomo Guerreschi, Alán Aspuru-Guzik

In the construction of feedforward networks of quantum neurons, we provide numerical evidence that the network not only can learn a function when trained with superposition of inputs and the corresponding output, but that this training suffices to learn the function on all individual inputs separately.

1 code implementation • 22 Nov 2017 • Teresa Tamayo-Mendoza, Christoph Kreisbeck, Roland Lindh, Alán Aspuru-Guzik

Automatic Differentiation (AD) is a powerful tool that allows calculating derivatives of implemented algorithms with respect to all of their parameters up to machine precision, without the need to explicitly add any additional functions.

Chemical Physics

1 code implementation • 7 Nov 2017 • Peter D. Johnson, Jonathan Romero, Jonathan Olson, Yudong Cao, Alán Aspuru-Guzik

Current approaches to fault-tolerant quantum computation will not enable useful quantum computation on near-term devices of 50 to 100 qubits.

Quantum Physics

no code implementations • 20 Jul 2017 • Florian Häse, Christoph Kreisbeck, Alán Aspuru-Guzik

Understanding the relationship between the structure of light-harvesting systems and their excitation energy transfer properties is of fundamental importance in many applications including the development of next generation photovoltaics.

no code implementations • ICML 2017 • José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik

These results show that PDTS is a successful solution for large-scale parallel BO.

1 code implementation • 30 May 2017 • Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, Alán Aspuru-Guzik

In unsupervised data generation tasks, besides the generation of a sample based on previous observations, one would often like to give hints to the model in order to bias the generation towards desirable metrics.

Ranked #1 on Molecular Graph Generation on ZINC (QED Top-3 metric)

11 code implementations • 7 Oct 2016 • Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik

We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation.

no code implementations • 22 Aug 2016 • Jennifer N. Wei, David Duvenaud, Alán Aspuru-Guzik

Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning.

4 code implementations • 26 Jan 2016 • Mikhail Smelyanskiy, Nicolas P. D. Sawaya, Alán Aspuru-Guzik

We present qHiPSTER, the Quantum High Performance Software Testing Environment.

Quantum Physics Distributed, Parallel, and Cluster Computing

8 code implementations • NeurIPS 2015 • David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, Ryan P. Adams

We introduce a convolutional neural network that operates directly on graphs.

Ranked #2 on Drug Discovery on HIV dataset

1 code implementation • 29 Jul 2014 • Jarrod R. McClean, Ryan Babbush, Peter J. Love, Alán Aspuru-Guzik

Accurate prediction of chemical and material properties from first principles quantum chemistry is a challenging task on traditional computers.

Quantum Physics Chemical Physics

no code implementations • 10 Apr 2013 • Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik, Jeremy L. O'Brien

Quantum computers promise to efficiently solve important problems that are intractable on a conventional computer.

Quantum Physics Chemical Physics

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