Search research articles
Contact Us
Filters
Showing results (1-10 of 14) with videos related to
Page
of 2
Sort By:
The Journal of Physical Chemistry. A
|
February 21, 2023
Machine Learning Interatomic Potentials and Long-Range Physics
Dylan M Anstine, Olexandr Isayev
Journal of the American Chemical Society
|
April 13, 2023
Generative Models as an Emerging Paradigm in the Chemical Sciences
Dylan M Anstine, Olexandr Isayev
Chemical Science
|
May 9, 2025
AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs
Dylan M Anstine, Roman Zubatyuk, Olexandr Isayev
The Journal of Physical Chemistry. B
|
August 15, 2022
Temperature Effects in Flexible Adsorption Processes for Amorphous Microporous Polymers
Wesley J Morgan, Dylan M Anstine, Coray M Colina
Chemical Science
|
November 30, 2023
Δ<sup>2</sup> machine learning for reaction property prediction
Qiyuan Zhao, Dylan M Anstine, Olexandr Isayev, et al.
Dalton Transactions (Cambridge, England : 2003)
|
December 1, 2015
New Pd(II) hemichelates devoid of incipient bridging COPd interactions
Christophe Werlé, Dylan M Anstine, Lydia Karmazin, et al.
Soft Matter
|
April 25, 2022
PEGDA hydrogel structure from semi-dilute concentrations: insights from experiments and molecular simulations
Jomary Mercado-Montijo, Dylan M Anstine, Shalini J Rukmani, et al.
ACS Applied Materials & Interfaces
|
December 20, 2021
Incorporating Flexibility Effects into Metal-Organic Framework Adsorption Simulations Using Different Models
Zhenzi Yu, Dylan M Anstine, Salah Eddine Boulfelfel, et al.
Angewandte Chemie (International Ed. in English)
|
December 16, 2025
AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry
Bhupalee Kalita, Roman Zubatyuk, Dylan M Anstine, et al.
Journal of Chemical Theory and Computation
|
February 3, 2023
Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials
Kaihang Shi, Zhao Li, Dylan M Anstine, et al.
Page
of 2
Search research articles
Search
Showing results (1-10 of 14) with videos related to
Sort By:
Page
of 2
The Journal of Physical Chemistry. A
|
February 21, 2023
Machine Learning Interatomic Potentials and Long-Range Physics
Dylan M Anstine, Olexandr Isayev
Journal of the American Chemical Society
|
April 13, 2023
Generative Models as an Emerging Paradigm in the Chemical Sciences
Dylan M Anstine, Olexandr Isayev
Chemical Science
|
May 9, 2025
AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs
Dylan M Anstine, Roman Zubatyuk, Olexandr Isayev
The Journal of Physical Chemistry. B
|
August 15, 2022
Temperature Effects in Flexible Adsorption Processes for Amorphous Microporous Polymers
Wesley J Morgan, Dylan M Anstine, Coray M Colina
Chemical Science
|
November 30, 2023
Δ<sup>2</sup> machine learning for reaction property prediction
Qiyuan Zhao, Dylan M Anstine, Olexandr Isayev, et al.
Dalton Transactions (Cambridge, England : 2003)
|
December 1, 2015
New Pd(II) hemichelates devoid of incipient bridging COPd interactions
Christophe Werlé, Dylan M Anstine, Lydia Karmazin, et al.
Soft Matter
|
April 25, 2022
PEGDA hydrogel structure from semi-dilute concentrations: insights from experiments and molecular simulations
Jomary Mercado-Montijo, Dylan M Anstine, Shalini J Rukmani, et al.
ACS Applied Materials & Interfaces
|
December 20, 2021
Incorporating Flexibility Effects into Metal-Organic Framework Adsorption Simulations Using Different Models
Zhenzi Yu, Dylan M Anstine, Salah Eddine Boulfelfel, et al.
Angewandte Chemie (International Ed. in English)
|
December 16, 2025
AIMNet2-NSE: A Transferable Reactive Neural Network Potential for Open-Shell Chemistry
Bhupalee Kalita, Roman Zubatyuk, Dylan M Anstine, et al.
Journal of Chemical Theory and Computation
|
February 3, 2023
Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials
Kaihang Shi, Zhao Li, Dylan M Anstine, et al.
Page
of 2