TensorMol

Tensorflow + Molecules = TensorMol

Project maintained by jparkhill Hosted on GitHub Pages — Theme by mattgraham

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-Title signature by Alex Graves’ handwriting LSTM https://arxiv.org/abs/1308.0850

Authors:

Kun Yao (kyao@nd.edu), John Herr (jherr1@nd.edu), David Toth (dtoth1@nd.edu), Ryker McIntyre(rmcinty3@nd.edu), Nicolas Casetti, John Parkhill (john.parkhill@gmail.com)

Model Chemistries:

Behler-Parrinello with electrostatics
Many Body Expansion
Bonds in Molecules NN
Atomwise Forces
Inductive Charges

Simulation Types:

Optimizations
Molecular Dynamics (NVE,NVT Nose-Hoover)
Monte Carlo
Open/Periodic Boundary Conditions
Meta-Dynamics
Infrared spectra by propagation
Infrared spectra by Harmonic Approximation.
Nudged Elastic Band
Path integral simulations via interface with I-PI MD engine.

News:

Did we disappear? NO! but TensorMol0.2 is being developed in a private developers branch. It’ll be back here soon.
(update 3/29/2018) TensorMol0.2 will arrive before 5/1. It adds additn’l element support, geometrical constraints, conformational search and other features.

License: GPLv3

By using this software you agree to the terms in COPYING

Installation:

Install TensorFlow(>1.1), otherwise TensorMol is self-contained.

Works on OSX, Ubuntu, and Windows subsystem for Linux:

git clone https://github.com/jparkhill/TensorMol.git
cd TensorMol
# If you are using python2x
sudo pip install -e .
# If you are using python3x
sudo pip3 install -e .
python test.py

Demo of training a neural network force field using TensorMol:

Copy the training script into the tensormol folder:cp samples/training_sample.py . Run the script: python training_sample.py This will train a network force field for water.

Test example for TensorMol01:

Download our pretrained neural networks (network.tar.gz). Networks for water and molecules that only contains C, H, O, N (The file is about 6 Gigabyte. This may take a while)
Copy the zipped trained networks file (network.tar.gz) into TensorMol folder. Unzip it. The networks should be in ‘./networks’ folder.
Copy the test script into the tensormol folder:cp samples/test_tensormol01.py . Run the script: python test_tensormol01.py The test sample contains geometry optimization, molecular dynamic, harmonic IR spectrum and realtime IR spectrum.

Timing Information

TensorMol is robust and fast. You can get an BP+electrostatic energy and force of this monstrous cube of 24,000 atoms in less than 100 seconds on a 2015 MacbookPro (Core i7 2.5Ghz, 16GB mem). Periodic simulations are about 3x more expensive.

Usage:

import TensorMol as tm
We are working on /doc/Tutorials, but it’s sparse now. We’ve done a lot of re-writing, and so if you are looking for good examples, look for /samples files with recent commits.
A collection of tests are located in samples/test_tensormol01.py but this requires first downloading the trained networks from the Arxiv paper.
python samples/test_tensormol01.py
IPI interface: start server: ~/i-pi/i-pi samples/i-pi_interface/H2O_cluster.xml > log &; run client: python test_ipi.py

Sample Results

Biological molecules

Because Neural network force fields do not rely on any specific atom typing or bond topology, the agony of setting up simulations of biological molecules is greatly reduced. This gif is a periodic optimization of PDB structure 2EVQ, in explicit polarizable TensorMol solvent.

Chemical Reactions

Converged nudged elastic band simulations of the cyclization cascade of endiandric acid C (c.f. K. C. Nicolaou, N. A. Petasis, R. E. Zipkin, 1982, The endiandric acid cascade. Electrocyclizations in organic synthesis. 4. Biomimetic approach to endiandric acids A-G. Total synthesis and thermal studies, J. Am. Chem. Soc. 104(20):5560–5562).

This reaction path can be found in a few minutes on an ordinary laptop. Relaxation from the linearly interpolated guess looks like this:

The associated energy surface is shown below.

Dynamic Properties

Tyrosine Harmonic IR spectrum

Publications and Press:

John E. Herr, Kun Yao, David W. Toth, Ryker Mcintyre, John Parkhill.Metadynamics for Training Neural Network Model Chemistries: a Competitive Assessment. Arxiv
Kun Yao, John E. Herr, David W. Toth, Ryker Mcintyre, John Parkhill. The TensorMol-0.1 Model Chemistry: a Neural Network Augmented with Long-Range Physics. Chemical Science
Writeup in Chemistry World
Kun Yao, John E. Herr, Seth N. Brown, & John Parkhill. Intrinsic Bond Energies from a Bonds-in-Molecules Neural Network. Journal of Physical Chemistry Letters (2017). DOI: 10.1021/acs.jpclett.7b01072
Kun Yao, John Herr, & John Parkhill. The Many-body Expansion Combined with Neural Networks. Journal of Chemical Physics (2016). DOI: 10.1063/1.4973380
Kun Yao, John Parkhill. The Kinetic Energy of Hydrocarbons as a Function of Electron Density and Convolutional Neural Networks. Journal of Chemical Theory and Computation (2016). DOI: 10.1021/acs.jctc.5b01011

Requirements:

Minimum Pre-Requisites: Python2.7x, TensorFlow sudo pip install tensorflow
Also now works in Python3.6.
Useful Pre-Requisites: CUDA7.5, PySCF
To Train Minimally: ~100GB Disk 20GB memory
To Train Realistically: 1TB Disk, GTX1070++
To Evaluate: Normal CPU and 10GB Mem

Acknowledgements:

Google Inc. (for TensorFlow)
NVidia Corp. (hardware)
von Lilienfeld Group (for GBD9)
Chan Group (for PySCF)

Common Issues:

nan during training due to bad checkpoints in /networks (clean.sh)
Also crashes when reviving networks from disk.
if you have these issues try re-installing or:

sh clean.sh