Zobrazeno 1 - 10
of 115
pro vyhledávání: '"Mailoa, Jonathan P"'
Autor:
Li, Weitang, Allcock, Jonathan, Cheng, Lixue, Zhang, Shi-Xin, Chen, Yu-Qin, Mailoa, Jonathan P., Shuai, Zhigang, Zhang, Shengyu
TenCirChem is an open-source Python library for simulating variational quantum algorithms for quantum computational chemistry. TenCirChem shows high performance on the simulation of unitary coupled-cluster circuits, using compact representations of q
Externí odkaz:
http://arxiv.org/abs/2303.10825
Publikováno v:
Digital Discovery (2023)
Recently, machine learning methods have been used to propose molecules with desired properties, which is especially useful for exploring large chemical spaces efficiently. However, these methods rely on fully labelled training data, and are not pract
Externí odkaz:
http://arxiv.org/abs/2301.04814
The generation of small molecule candidate (ligand) binding poses in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose g
Externí odkaz:
http://arxiv.org/abs/2301.00984
Autor:
Shmilovich, Kirill, Willmott, Devin, Batalov, Ivan, Kornbluth, Mordechai, Mailoa, Jonathan, Kolter, J. Zico
Leveraging ab initio data at scale has enabled the development of machine learning models capable of extremely accurate and fast molecular property prediction. A central paradigm of many previous works focuses on generating predictions for only a fix
Externí odkaz:
http://arxiv.org/abs/2205.06133
Autor:
Batzner, Simon, Musaelian, Albert, Sun, Lixin, Geiger, Mario, Mailoa, Jonathan P., Kornbluth, Mordechai, Molinari, Nicola, Smidt, Tess E., Kozinsky, Boris
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aw
Externí odkaz:
http://arxiv.org/abs/2101.03164
A community-powered search of machine learning strategy space to find NMR property prediction models
Autor:
Bratholm, Lars A., Gerrard, Will, Anderson, Brandon, Bai, Shaojie, Choi, Sunghwan, Dang, Lam, Hanchar, Pavel, Howard, Addison, Huard, Guillaume, Kim, Sanghoon, Kolter, Zico, Kondor, Risi, Kornbluth, Mordechai, Lee, Youhan, Lee, Youngsoo, Mailoa, Jonathan P., Nguyen, Thanh Tu, Popovic, Milos, Rakocevic, Goran, Reade, Walter, Song, Wonho, Stojanovic, Luka, Thiede, Erik H., Tijanic, Nebojsa, Torrubia, Andres, Willmott, Devin, Butts, Craig P., Glowacki, David R., participants, Kaggle
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in adva
Externí odkaz:
http://arxiv.org/abs/2008.05994
Autor:
Park, Cheol Woo, Kornbluth, Mordechai, Vandermause, Jonathan, Wolverton, Chris, Kozinsky, Boris, Mailoa, Jonathan P.
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically
Externí odkaz:
http://arxiv.org/abs/2007.14444
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Mailoa, Jonathan P., Kornbluth, Mordechai, Batzner, Simon L., Samsonidze, Georgy, Lam, Stephen T., Ablitt, Chris, Molinari, Nicola, Kozinsky, Boris
Publikováno v:
Nature Machine Intelligence 1 (2019)
Neural network force field (NNFF) is a method for performing regression on atomic structure-force relationships, bypassing expensive quantum mechanics calculation which prevents the execution of long ab-initio quality molecular dynamics simulations.
Externí odkaz:
http://arxiv.org/abs/1905.02791
Autor:
Mailoa, Jonathan P
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
Externí odkaz:
http://hdl.handle.net/1721.1/105950