Macs in Chemistry

Insanely Great Science

Mac backup options

 

We now have so much of our digital life on a hard drive including photos, music, emails from friends and family. In addition it is always worth having a current backup prior to a major OS upgrade, and with macOS Catalina on the horizon now would be a good time to review.

This page reviews backup options including external or network hard drives, and cloud storage, in addition to the software tools available.

Backup Options for Mac.

cloud-computing-1990405_1280


Comments

BBEdit Update

 

Everybody's favourite text editor BBEdit has been updated.

BBEdit 12.6.7 contains fixes for reported issues.

BBEdit 12.6.7 does not add any new features. (It doesn't take away any, either.)

Made a change to ask the OS-provided print panel to place the page attribute controls (orientation, scaling, paper size) in the panel proper, rather than hiding them behind the "Page Attributes" section in the popup menu.

Plus several bug fixes


Comments

Python and CUDA

 

After my last post on Macs and CUDA I was sent a link to CuPy which is a library that is supported by NVIDIA that allows to easily run CUDA code in Python using NumPy arrays as input.

CuPy's interface is highly compatible with NumPy; in most cases it can be used as a drop-in replacement. All you need to do is just replace numpy with cupy in your Python code. It supports various methods, indexing, data types, broadcasting and more.

To install

pip install cupy

Note

The latest version of cuDNN and NCCL libraries are included in binary packages (wheels). For the source package, you will need to install cuDNN/NCCL before installing CuPy, if you want to use it.

Or you can install versions specific to the particular CUDA environment. Full details are on GitHub https://github.com/cupy/cupy.

Comments

Macs and CUDA

 

One of the highlights for me at the recent 2nd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry in Cambridge was the work of Adrian Roitberg and Olexandr Isayev et al on Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning DOI.

Here we train a general-purpose neural network potential (ANI- 1ccx) that approaches CCSD(T)/CBS accuracy on benchmarks for reaction thermochemistry, isomerization, and drug-like molecular torsions. This is achieved by training a network to DFT data then using transfer learning techniques to retrain on a dataset of gold standard QM calculations (CCSD(T)/CBS) that optimally spans chemical space. The resulting potential is broadly applicable to materials science, biology, and chemistry, and billions of times faster than CCSD(T)/CBS calculations.

The presentation was really compelling and really looks like an example where AI can be truly transformational. The good news is the code is all freely available on Github https://github.com/isayev/ASE_ANI, the bad news is that it "Works only under Ubuntu variants of Linux with a NVIDIA GPU" and Python binaries built for python 3.6 and CUDA 9.2.

In the past I would have stopped there but with the increasing number of external GPU and a NVIDIA CUDA Installation Guide for Mac OS X I'm wondering if there might be a path forward. I'd be very interested to hear about experiences with external GPU with NVIDIA graphics cards and using the CUDA toolkit on a Mac.

Update

Olexandr emailed me to to mention they have a pure Python version https://github.com/aiqm/torchani this will run on Mac however there is no GPU acceleration.

TorchANI is a pytorch implementation of ANI. It is currently under alpha release, which means, the API is not stable yet. If you find a bug of TorchANI, or have some feature request, feel free to open an issue on GitHub, or send us a pull requests

Also stumbled across the paper

Ab-Initio Solution of the Many-Electron Schrödinger Equation with Deep Neural Networks https://arxiv.org/abs/1909.02487Arxiv


Comments

2nd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry

 

The 2nd RSC-BMCS / RSC-CICAG Artificial Intelligence in Chemistry is now over, two intensive days of presentations and posters. Many thanks for all who took part and made it such a successful event.

Special mention to the Poster prize winners.

P17 by Jenke Scheen of the University of Edinburgh Entitled: "Improving the accuracy of alchemical free energy methods by learning correction terms for binding energy estimates"

P6 by Adam Green of the University of Leeds Entitled: "Activity-directed discovery of inhibitors of the p53/MDM2 interaction: towards autonomous functional molecule discovery"

P3 by Ya Chen of the University of Hamburg Entitled: "NP-Scout: machine learning approach for the identification of natural products and natural product-like compounds in large molecular databases"

If you want to browse through the Twitter feeds search for the #AIChem19 hashtag.

Many of the presentations are now available in pdf format on the meeting website.

We are already thinking about a possible 3rd meeting, and any feedback would be much appreciated.

Comments