scholarly journals Machine learning enables long time scale molecular photodynamics simulations

2019 ◽  
Vol 10 (35) ◽  
pp. 8100-8107 ◽  
Author(s):  
Julia Westermayr ◽  
Michael Gastegger ◽  
Maximilian F. S. J. Menger ◽  
Sebastian Mai ◽  
Leticia González ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Excited State ◽  
Molecular Dynamics Simulations ◽  
Time Scales ◽  
Time Scale ◽  
Long Time ◽  
Dynamics Simulations

Machine learning enables excited-state molecular dynamics simulations including nonadiabatic couplings on nanosecond time scales.

scholarly journals Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

2020 ◽  
Author(s):  
Punyaslok Pattnaik ◽  
Shampa Raghunathan ◽  
Tarun Kalluri ◽  
Prabhakar Bhimalapuram ◽  
C. V. Jawahar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Time Scales ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Experimental Conditions ◽  
The Neural Network ◽  
Dynamics Simulations

<p>The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.</p>

scholarly journals Interaction Networks in Protein Folding via Atomic-Resolution Experiments and Long-Time-Scale Molecular Dynamics Simulations

2015 ◽  
Vol 137 (20) ◽  
pp. 6506-6516 ◽  
Author(s):  
Lorenzo Sborgi ◽  
Abhinav Verma ◽  
Stefano Piana ◽  
Kresten Lindorff-Larsen ◽  
Michele Cerminara ◽  
...  
Keyword(s):  
Molecular Dynamics ◽  
Protein Folding ◽  
Molecular Dynamics Simulations ◽  
Time Scale ◽  
Interaction Networks ◽  
Atomic Resolution ◽  
Long Time ◽  
Dynamics Simulations

Machine Learning for Accurate Force Calculations in Molecular Dynamics Simulations

2020 ◽  
Author(s):  
Punyaslok Pattnaik ◽  
Shampa Raghunathan ◽  
Tarun Kalluri ◽  
Prabhakar Bhimalapuram ◽  
C. V. Jawahar ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Dynamics ◽  
Molecular Dynamics Simulations ◽  
Time Scales ◽  
Density Functional ◽  
Ab Initio Molecular Dynamics ◽  
Experimental Conditions ◽  
The Neural Network ◽  
Dynamics Simulations

<p>The computationally expensive nature of ab initio molecular dynamics simulations severely limits its ability to simulate large system sizes and long time scales, both of which are necessary to imitate experimental conditions. In this work, we explore an approach to make use of the data obtained using the quantum mechanical density functional theory (DFT) on small systems and use deep learning to subsequently simulate large systems by taking liquid argon as a test case. A suitable vector representation was chosen to represent the surrounding environment of each Ar atom, and a DNetFF machine learning model where, the neural network was trained to predict the difference in resultant forces obtained by DFT and classical force fields was introduced. Molecular dynamics simulations were then performed using forces from the neural network for various system sizes and time scales depending on the properties we calculated. A comparison of properties obtained from the classical force field and the neural network model was presented alongside available experimental data to validate the proposed method.</p>

Mechanistic analysis of light-driven overcrowded alkene-based molecular motors by multiscale molecular simulations

2021 ◽  
Vol 23 (14) ◽  
pp. 8525-8540
Author(s):  
Mudong Feng ◽  
Michael K. Gilson
Keyword(s):  
Molecular Dynamics ◽  
Excited State ◽  
Molecular Dynamics Simulations ◽  
Ground State ◽  
Molecular Motors ◽  
Motor Performance ◽  
Molecular Simulations ◽  
Mechanistic Analysis ◽  
Dynamics Simulations ◽  
Shed Light

Ground-state and excited-state molecular dynamics simulations shed light on the rotation mechanism of small, light-driven molecular motors and predict motor performance. How fast can they rotate; how much torque and power can they generate?

Machine learning and molecular dynamics simulations assisted evolutionary design and discovery pipeline to screen the efficient small molecule acceptors for PTB7-Th based organic solar cells with over 15% efficiency

2022 ◽  
Author(s):  
Jin-Liang Wang ◽  
Asif Mahmood ◽  
Ahmad Irfan
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Solar Cells ◽  
Molecular Dynamics Simulations ◽  
Small Molecule ◽  
Organic Solar Cells ◽  
Evolutionary Design ◽  
New Materials ◽  
Dynamics Simulations ◽  
Discovery Pipeline

Organic solar cells are the most promising candidates for future commercialization. This goal can be quickly achieved by designing new materials and predicting their performance without experimentation to reduce the...

Ab Initio Molecular Dynamics Simulations of an Excited State of X-(H2O)3(X = Cl, I) Complex

2005 ◽  
Vol 109 (42) ◽  
pp. 9419-9423 ◽  
Author(s):  
M. Kołaski ◽  
Han Myoung Lee ◽  
Chaeho Pak ◽  
M. Dupuis ◽  
Kwang S. Kim
Keyword(s):  
Molecular Dynamics ◽  
Excited State ◽  
Ab Initio ◽  
Molecular Dynamics Simulations ◽  
Ab Initio Molecular Dynamics ◽  
Dynamics Simulations
Sign in / Sign up

Export Citation Format

Share Document