no code implementations • 16 Oct 2018 • Yu-Hang Tang, Wibe A. de Jong
Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management.
1 code implementation • 2 Mar 2020 • Muammar El Khatib, Wibe A. de Jong
It provides an extendable platform to develop and deploy machine learning models and pipelines and is targeted to the non-expert and expert users.
2 code implementations • 7 Jul 2020 • David B. Williams-Young, Wibe A. de Jong, Hubertus J. J. van Dam, Chao Yang
We demonstrate the performance and scalability of the implementation of the purposed method in the NWChemEx software package by comparing to the existing scalable CPU XC integration in NWChem.
Computational Physics Distributed, Parallel, and Cluster Computing Chemical Physics
no code implementations • 20 Jan 2021 • Thien Nguyen, Lindsay Bassman, Dmitry Lyakh, Alexander McCaskey, Vicente Leyton-Ortega, Raphael Pooser, Wael Elwasif, Travis S. Humble, Wibe A. de Jong
Subsequently, it allows a synthesis of new hybrid algorithms and workflows via the extension, specialization, and dynamic customization of the abstract core classes defined by our design.
Quantum Physics
no code implementations • 4 Mar 2021 • Mekena Metcalf, Emma Stone, Katherine Klymko, Alexander F. Kemper, Mohan Sarovar, Wibe A. de Jong
Modeling a macroscopic environment on a quantum simulator may be achieved by coupling independent ancilla qubits that facilitate energy exchange in an appropriate manner with the system and mimic an environment.
Quantum Physics Statistical Mechanics
1 code implementation • 12 Mar 2021 • Lindsay Bassman, Roel Van Beeumen, Ed Younis, Ethan Smith, Costin Iancu, Wibe A. de Jong
Current algorithms for Hamiltonian simulation, however, produce circuits that grow in depth with increasing simulation time, limiting feasible simulations to short-time dynamics.
Quantum Physics
no code implementations • 21 Mar 2021 • Yu-Hang Tang, Yuanran Zhu, Wibe A. de Jong
Optimizing the noise model using maximum likelihood estimation leads to the containment of the GPR model's predictive error by the posterior standard deviation in leave-one-out cross-validation.
4 code implementations • ICLR 2022 • Yulun Wu, Mikaela Cashman, Nicholas Choma, Érica T. Prates, Verónica G. Melesse Vergara, Manesh Shah, Andrew Chen, Austin Clyde, Thomas S. Brettin, Wibe A. de Jong, Neeraj Kumar, Martha S. Head, Rick L. Stevens, Peter Nugent, Daniel A. Jacobson, James B. Brown
We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain.
2 code implementations • 9 Jun 2023 • Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali, Shruti Badhwar, Joshua D. Bocarsly, Andres M Bran, Stefan Bringuier, L. Catherine Brinson, Kamal Choudhary, Defne Circi, Sam Cox, Wibe A. de Jong, Matthew L. Evans, Nicolas Gastellu, Jerome Genzling, María Victoria Gil, Ankur K. Gupta, Zhi Hong, Alishba Imran, Sabine Kruschwitz, Anne Labarre, Jakub Lála, Tao Liu, Steven Ma, Sauradeep Majumdar, Garrett W. Merz, Nicolas Moitessier, Elias Moubarak, Beatriz Mouriño, Brenden Pelkie, Michael Pieler, Mayk Caldas Ramos, Bojana Ranković, Samuel G. Rodriques, Jacob N. Sanders, Philippe Schwaller, Marcus Schwarting, Jiale Shi, Berend Smit, Ben E. Smith, Joren Van Herck, Christoph Völker, Logan Ward, Sean Warren, Benjamin Weiser, Sylvester Zhang, Xiaoqi Zhang, Ghezal Ahmad Zia, Aristana Scourtas, KJ Schmidt, Ian Foster, Andrew D. White, Ben Blaiszik
Recent studies suggested that these models could be useful in chemistry and materials science.