Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

Related tags

Deep LearningPyRAI2MD
Overview

Python Rapid Artificial Intelligence Ab Initio Molecular Dynamics

                              /\
   |\    /|                  /++\
   ||\  /||                 /++++\
   || \/ || ||             /++++++\
   ||    || ||            /PyRAI2MD\
   ||    || ||           /++++++++++\                    __
            ||          /++++++++++++\    |\ |  /\  |\/| | \
            ||__ __    *==============*   | \| /--\ |  | |_/

                          Python Rapid
                     Artificial Intelligence
                  Ab Initio Molecular Dynamics



                      Author @Jingbai Li
               Northeastern University, Boston, USA

                          version:   2.0 alpha
                          

  With contriutions from (in alphabetic order):
    Jingbai Li                 - Fewest switches surface hopping
                                 Zhu-Nakamura surface hopping
                                 Velocity Verlet
                                 OpenMolcas interface
                                 OpenMolcas/Tinker interface
                                 BAGEL interface
                                 Adaptive sampling
                                 Grid search
                                 Two-layer ONIOM (coming soon)
                                 Periodic boundary condition (coming soon)
                                 QC/ML hybrid NAMD

    Patrick Reiser             - Neural networks (pyNNsMD)

  Special acknowledgement to:
    Steven A. Lopez            - Project directorship
    Pascal Friederich          - ML directoriship>

Features

  • Machine learning nonadibatic molecular dyanmics (ML-NAMD).
  • Neural network training and grid search.
  • Active learning with ML-NAMD trajectories.
  • Support BAGEL, Molcas for QM, and Molcas/Tinker for QM/MM calculations.
  • Support nonadibatic coupling and spin-orbit coupling (Molcas only)

Prerequisite

  • Python >=3.7 PyRAI2MD is written and tested in Python 3.7.4. Older version of Python is not tested and might not be working properly.
  • TensorFlow >=2.2 TensorFlow/Keras API is required to load the trained NN models and predict energy and force.
  • Cython PyRAI2MD uses Cython library for efficient surface hopping calculation.
  • Matplotlib/Numpy Scientifc graphing and numerical library for plotting training statistic and array manipulation.

Content

 File/Folder Name                                  Description                                      
---------------------------------------------------------------------------------------------------
 pyrai2md.py                                       PyRAI2MD interface                              
 PyRAI2MD                                          source codes folder
  |--variables.py                                  PyRAI2MD input reader                           
  |--method.py                                     PyRAI2MD method manager                         
  |--Molecule                                      atom, molecule, trajectory code folder
  |   |--atom.py                                   atomic properties class                         
  |   |--molecule.py                               molecular properties class                      
  |   |--trajectory.py                             trajectory properties class                     
  |   |--pbc_helper.py                             periodic boundary condition functions           
  |    `-qmmm_helper.py                            qmmm functions                                  
  |
  |--Quantum_Chemistry                             quantum chemicial program interface folder
  |   |--qc_molcas.py                              OpenMolcas interface                            
  |   |--qc_bagel.py                               BAGEL interface                                 
  |    `-qc_molcas_tinker                          OpenMolcas/Tinker interface                     
  |
  |--Machine_Learning                              machine learning library interface folder
  |   |--training_data.py                          training data manager                           
  |   |--model_NN.py                               neural network interface                        
  |   |--hypernn.py                                hyperparameter manager                          
  |   |--permutation.py                            data permutation functions                      
  |   |--adaptive_sampling.py                      adaptive sampling class                         
  |   |--grid_search.py                            grid search class                               
  |   |--remote_train.py                           distribute remote training                      
  |    `-pyNNsMD                                   neural network library                         
  |
  |--Dynamics                                      ab initio molecular dynamics code folder
  |   |--aimd.py                                   molecular dynamics class                        
  |   |--mixaimd.py                                ML-QC hybrid molecular dynamics class           
  |   |--single_point.py                           single point calculation                        
  |   |--hop_probability.py                        surface hopping probability calculation         
  |   |--reset_velocity.py                         velocity adjustment functions                   
  |   |--verlet.py                                 velocity verlet method                          
  |   |--Ensembles                                 thermodynamics control code folder
  |   |   |--ensemble.py                           thermodynamics ensemble manager                 
  |   |   |--microcanonical.py                     microcanonical ensemble                         
  |   |    `-thermostat.py                         canonical ensemble                              
  |   |
  |    `-Propagators                               electronic propagation code folder
  |       |--surface_hopping.py                    surface hopping manager                         
  |       |--fssh.pyx                              fewest switches surface hopping method          
  |       |--gsh.py                                generalized surface hopping method              
  |        `-tsh_helper.py                         trajectory surface hopping tools                
  |
   `-Utils                                         utility folder
      |--aligngeom.py                              geometry aligment and comparison functions      
      |--coordinates.py                            coordinates writing functions                   
      |--read_tools.py                             index reader                                    
      |--bonds.py                                  bond length library                            
      |--sampling.py                               initial condition sampling functions            
      |--timing.py                                 timing functions                                
       `-logo.py                                   logo and credits                                    

Installation

Download the repository

git clone https://github.com/lopez-lab/PyRAI2MD.git

Specify environment variable of PyRAI2MD

export PYRAI2MD=/path/to/PyRAI2MD

Test PyRAI2MD

Copy the test script and modify environment variables

cp $PYRAI2MD/Tool/test_PyRAI2MD.sh .
bash test_PyRAI2MD.sh

Or directly run if environment variables are set

$PYRAI2MD/pyrai2md.py quicktest

Run PyRAI2MD

$PYRAI2MD/pyrai2md.py input

User manual

We are currently working on the user manual.

Cite us

  • Jingbai Li, Patrick Reiser, Benjamin R. Boswell, André Eberhard, Noah Z. Burns, Pascal Friederich, and Steven A. Lopez, "Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations", Chem. Sci. 2021. DOI: 10.1039/D0SC05610C
  • Jingbai Li, Rachel Stein, Daniel Adrion, Steven A. Lopez, "Machine-learning photodynamics simulations uncover the role of substituent effects on the photochemical formation of cubanes", ChemRxiv, preprint, DOI:10.33774/chemrxiv-2021-lxsjk
Content shared at DS-OX Meetup

Streamlit-Projects Streamlit projects available in this repo: An introduction to Streamlit presented at DS-OX (Feb 26, 2020) meetup Streamlit 101 - Ja

Arvindra 69 Dec 23, 2022
Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL)

Surrogate- and Invariance-Boosted Contrastive Learning (SIB-CL) This repository contains all source code used to generate the results in the article "

Charlotte Loh 3 Jul 23, 2022
HackBMU-5.0-Team-Ctrl-Alt-Elite - HackBMU 5.0 Team Ctrl Alt Elite

HackBMU-5.0-Team-Ctrl-Alt-Elite The search is over. We present to you ‘Health-A-

3 Feb 19, 2022
Photographic Image Synthesis with Cascaded Refinement Networks - Pytorch Implementation

Photographic Image Synthesis with Cascaded Refinement Networks-Pytorch (https://arxiv.org/abs/1707.09405) This is a Pytorch implementation of cascaded

Soumya Tripathy 63 Mar 27, 2022
Implementation of a memory efficient multi-head attention as proposed in the paper, "Self-attention Does Not Need O(n²) Memory"

Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(

Phil Wang 180 Jan 05, 2023
Analysis code and Latex source of the manuscript describing the conditional permutation test of confounding bias in predictive modelling.

Git repositoty of the manuscript entitled Statistical quantification of confounding bias in predictive modelling by Tamas Spisak The manuscript descri

PNI - Predictive Neuroimaging Lab, University Hospital Essen, Germany 0 Nov 22, 2021
Udacity's CS101: Intro to Computer Science - Building a Search Engine

Udacity's CS101: Intro to Computer Science - Building a Search Engine All soluti

Phillip 0 Feb 26, 2022
Object detection, 3D detection, and pose estimation using center point detection:

Objects as Points Object detection, 3D detection, and pose estimation using center point detection: Objects as Points, Xingyi Zhou, Dequan Wang, Phili

Xingyi Zhou 6.7k Jan 03, 2023
XtremeDistil framework for distilling/compressing massive multilingual neural network models to tiny and efficient models for AI at scale

XtremeDistilTransformers for Distilling Massive Multilingual Neural Networks ACL 2020 Microsoft Research [Paper] [Video] Releasing [XtremeDistilTransf

Microsoft 125 Jan 04, 2023
Deep Reinforcement Learning based Trading Agent for Bitcoin

Deep Trading Agent Deep Reinforcement Learning based Trading Agent for Bitcoin using DeepSense Network for Q function approximation. For complete deta

Kartikay Garg 669 Dec 29, 2022
Restricted Boltzmann Machines in Python.

How to Use First, initialize an RBM with the desired number of visible and hidden units. rbm = RBM(num_visible = 6, num_hidden = 2) Next, train the m

Edwin Chen 928 Dec 30, 2022
[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting

[CVPR 2020] 3D Photography using Context-aware Layered Depth Inpainting [Paper] [Project Website] [Google Colab] We propose a method for converting a

Virginia Tech Vision and Learning Lab 6.2k Jan 01, 2023
training script for space time memory network

Trainig Script for Space Time Memory Network This codebase implemented training code for Space Time Memory Network with some cyclic features. Requirem

Yuxi Li 100 Dec 20, 2022
A hybrid SOTA solution of LiDAR panoptic segmentation with C++ implementations of point cloud clustering algorithms. ICCV21, Workshop on Traditional Computer Vision in the Age of Deep Learning

ICCVW21-TradiCV-Survey-of-LiDAR-Cluster Motivation In contrast to popular end-to-end deep learning LiDAR panoptic segmentation solutions, we propose a

YimingZhao 103 Nov 22, 2022
TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning

TransZero++ This repository contains the testing code for the paper "TransZero++: Cross Attribute-guided Transformer for Zero-Shot Learning" submitted

Shiming Chen 6 Aug 16, 2022
Pytorch implement of 'Unmixing based PAN guided fusion network for hyperspectral imagery'

Pgnet There's a improved version compared with the publication in Tgrs with the modification in the deduction of the PDIN block: https://arxiv.org/abs

5 Jul 01, 2022
Official re-implementation of the Calibrated Adversarial Refinement model described in the paper Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Official re-implementation of the Calibrated Adversarial Refinement model described in the paper Calibrated Adversarial Refinement for Stochastic Semantic Segmentation

Elias Kassapis 31 Nov 22, 2022
An University Project of Quera Web Crawling.

WebCrawlerProject An University Project of Quera Web Crawling. خزشگر اینستاگرام در این پروژه شما باید با استفاده از کتابخانه های زیر یک خزشگر اینستاگر

Mahdi 3 Aug 12, 2022
Official implementation of the NeurIPS 2021 paper Online Learning Of Neural Computations From Sparse Temporal Feedback

Online Learning Of Neural Computations From Sparse Temporal Feedback This repository is the official implementation of the NeurIPS 2021 paper Online L

Lukas Braun 3 Dec 15, 2021
ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.

ML-PersonalWork - Big assignment PersonalWork in Machine Learning, 2021 autumn BUAA.

Snapdragon Lee 2 Dec 16, 2022