Aspuru-Guzik, Alan

36 publications

CoRL 2025 AnyPlace: Learning Generalizable Object Placement for Robot Manipulation Yuchi Zhao, Miroslav Bogdanovic, Chengyuan Luo, Steven Tohme, Kourosh Darvish, Alan Aspuru-Guzik, Florian Shkurti, Animesh Garg

FnTML 2025 Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems 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, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, 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 J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

ICLRW 2025 DEQuify Your Force Field: More Efficient Simulations Using Deep Equilibrium Models Andreas Burger, Luca Thiede, Alan Aspuru-Guzik, Nandita Vijaykumar

NeurIPS 2025 ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals Jonas Elsborg, Luca Thiede, Alan Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik

ICLRW 2025 ELECTRA: A Symmetry-Breaking Cartesian Network for Charge Density Prediction with Floating Orbitals Jonas Elsborg, Luca Thiede, Alan Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik

ICLR 2025 Efficient Evolutionary Search over Chemical Space with Large Language Models Haorui Wang, Marta Skreta, Cher Tian Ser, Wenhao Gao, Lingkai Kong, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

ICML 2025 Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alan Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov

ICLRW 2025 Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alan Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov

ICLR 2025 Stiefel Flow Matching for Moment-Constrained Structure Elucidation Austin Henry Cheng, Alston Lo, Kin Long Kelvin Lee, Santiago Miret, Alan Aspuru-Guzik

ICLRW 2025 What Actually Matters for Materials Discovery: Pitfalls and Recommendations in Bayesian Optimization Tristan Cinquin, Stanley Lo, Felix Strieth-Kalthoff, Alan Aspuru-Guzik, Geoff Pleiss, Robert Bamler, Tim G. J. Rudner, Vincent Fortuin, Agustinus Kristiadi

ICML 2024 A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization over Molecules? Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alan Aspuru-Guzik, Geoff Pleiss

NeurIPSW 2024 Dimension Deficit: Is 3D a Step Too Far for Optimizing Molecules? Andres Guzman Cordero, Luca Thiede, Gary Tom, Alan Aspuru-Guzik, Felix Strieth-Kalthoff, Agustinus Kristiadi

NeurIPS 2024 Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noé, Carla P. Gomes, Alán Aspuru-Guzik, Kirill Neklyudov

ICMLW 2024 Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noe, Carla P Gomes, Alan Aspuru-Guzik, Kirill Neklyudov

ICMLW 2024 Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noe, Carla P Gomes, Alan Aspuru-Guzik, Kirill Neklyudov

ICMLW 2024 Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noe, Carla P Gomes, Alan Aspuru-Guzik, Kirill Neklyudov

ICMLW 2024 Efficient Evolutionary Search over Chemical Space with Large Language Models Haorui Wang, Marta Skreta, Yuanqi Du, Wenhao Gao, Lingkai Kong, Cher Tian Ser, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

ICMLW 2024 Efficient Evolutionary Search over Chemical Space with Large Language Models Haorui Wang, Marta Skreta, Yuanqi Du, Wenhao Gao, Lingkai Kong, Cher Tian Ser, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

NeurIPSW 2024 If Optimizing for General Parameters in Chemistry Is Useful, Why Is It Hardly Done? Stefan P. Schmid, Ella Miray Rajaonson, Cher Tian Ser, Mohammad Haddadnia, Shi Xuan Leong, Alan Aspuru-Guzik, Agustinus Kristiadi, Kjell Jorner, Felix Strieth-Kalthoff

NeurIPS 2024 Infusing Synthetic Data with Real-World Patterns for Zero-Shot Material State Segmentation Sagi Eppel, Jolina Yining Li, Manuel S. Drehwald, Alan Aspuru-Guzik

ICMLW 2024 PAIR: Boosting the Predictive Power of Protein Representations with a Corpus of Text Annotations Haonan Duan, Marta Skreta, Leonardo Cotta, Ella Miray Rajaonson, Nikita Dhawan, Alan Aspuru-Guzik, Chris J. Maddison

ICML 2024 Position: Application-Driven Innovation in Machine Learning David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi, Hannah Kerner, Claire Monteleoni, Esther Rolf, Milind Tambe, Adam White

NeurIPS 2024 Quantum Deep Equilibrium Models Philipp Schleich, Marta Skreta, Lasse B. Kristensen, Rodrigo A. Vargas-Hernández, Alán Aspuru-Guzik

ICMLW 2024 Sorting Out Quantum Monte Carlo Jack Richter-Powell, Luca Thiede, Alan Aspuru-Guzik, David Duvenaud

NeurIPS 2023 GAUCHE: A Library for Gaussian Processes in Chemistry Ryan-Rhys Griffiths, Leo Klarner, Henry Moss, Aditya Ravuri, Sang Truong, Yuanqi Du, Samuel Stanton, Gary Tom, Bojana Rankovic, Arian Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha Lee, Bingqing Cheng, Alan Aspuru-Guzik, Philippe Schwaller, Jian Tang

ICCV 2023 One-Shot Recognition of Any Material Anywhere Using Contrastive Learning with Physics-Based Rendering Manuel S. Drehwald, Sagi Eppel, Jolina Li, Han Hao, Alan Aspuru-Guzik

NeurIPSW 2023 Reflection-Equivariant Diffusion for 3D Structure Determination from Isotopologue Rotational Spectra in Natural Abundance Austin Henry Cheng, Alston Lo, Santiago Miret, Brooks Pate, Alan Aspuru-Guzik

NeurIPS 2023 Tartarus: A Benchmarking Platform for Realistic and Practical Inverse Molecular Design AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, John Willes, Luca Thiede, Anshul Kundaje, Alan Aspuru-Guzik

NeurIPSW 2023 Towards Equilibrium Molecular Conformation Generation with GFlowNets Alexandra Volokhova, Michał Koziarski, Alex Hernández-García, Cheng-Hao Liu, Santiago Miret, Pablo Lemos, Luca Thiede, Zichao Yan, Alan Aspuru-Guzik, Yoshua Bengio

NeurIPSW 2022 Assessing Multi-Objective Optimization of Molecules with Genetic Algorithms Against Relevant Baselines Nathanael Kusanda, Gary Tom, Riley Hickman, AkshatKumar Nigam, Kjell Jorner, Alan Aspuru-Guzik

NeurIPSW 2022 Conformer Search Using SE3-Transformers and Imitation Learning Luca Thiede, Santiago Miret, Krzysztof Sadowski, Haoping Xu, Mariano Phielipp, Alan Aspuru-Guzik

NeurIPSW 2022 Group SELFIES: A Robust Fragment-Based Molecular String Representation Austin Henry Cheng, Andy Cai, Santiago Miret, Gustavo Malkomes, Mariano Phielipp, Alan Aspuru-Guzik

CoRL 2021 Seeing Glass: Joint Point-Cloud and Depth Completion for Transparent Objects Haoping Xu, Yi Ru Wang, Sagi Eppel, Alan Aspuru-Guzik, Florian Shkurti, Animesh Garg

ICLR 2020 Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik

ICML 2017 Parallel and Distributed Thompson Sampling for Large-Scale Accelerated Exploration of Chemical Space José Miguel Hernández-Lobato, James Requeima, Edward O. Pyzer-Knapp, Alán Aspuru-Guzik

NeurIPS 2015 Convolutional Networks on Graphs for Learning Molecular Fingerprints David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, Ryan P. Adams