New for 2020.1 release – aromatics analyser in Mercury
April 29, 2020
We live in exciting times for Artificial Intelligence (AI) - with the rise of new and easy to implement Machine Learning (ML) algorithms. Many of us would sooner trust a GPS to take us from point A to point B than consult a map ourselves, and robots are already being used to perform medical procedures. But what do all of these advanced techniques and algorithms mean for us as scientists and how can we use them to advance science? Presumably, many would ask if AI approaches can help, or even replace scientific experiments?
The neural network model reproduces the quantum mechanical calculations with 97% precision. To test the performance of our model we looked at real crystal structures containing aromatic functional groups from the CSD. To calculate the strength of aromatic interactions we took the following steps:
- the atomic positions of the phenyl dimers were extracted
- substituents were replaced with H atoms
- then DFT quantum mechanical calculations were performed for these dimers.
The neural network prediction also performed well over a heterogenous orientation of phenyl groups which were extracted from paracetamol (refcode HXACAN).
How neural networks function:The input values are multiplied with initial random weight values (close to zero, but not zero). In our case the input values were the geometric parameters (e.g. atom-atom distance, centroid-centroid distance and plane-plane angle).
Each node in the neural network will use a function - in this case we selected the Rectified Linear Unit (ReLu) to produce an output value.
The output values is compared with the actual value and then something called "backwards propagation" happens where the weights are adjusted in such a way that minimises the difference between the output value and actual value. To minimise those values and to ensure that no local minimum occurs the algorithm uses something called Stochastic Gradient Descent. If you are curious of how the world could look with fully functional AI mechanisms, or you want to teach your children more about neural networks, these two books are for you: If you would like any more information about the Aromatics Analyser component, please don't hesitate to get in touch, you can email us at .
Aromatics Analyser (4)
Release 2020.1 (4)