SAUCE = sensible asymmetric units for crystal exploration

These methods transfer structural features from shorter or smaller crystal structure prediction calculations into the process of structure generation for more complex searches. Effectively, this lowers the dimensionality of the search space.

So, apart from the evolutionary method that we have developed, the work has produced a large, valuable dataset of crystal structures, their calculated energies and properties.

9/9

We search a moderately sized chemical space of approximately 136,000 aza-substituted polycyclic aromatic hydrocarbons for the best molecules. Through parameter testing and evaluation of the method, we have performed CSP on over 9000 unique molecules.

8/n

The approach will have broad applicability for materials discovery, wherever the property of interest is computable from the crystal structure. Here, we address electron mobility in organic semiconductors, where intermolecular electronic coupling depends strongly on crystal structure.

7/n

This is what we have done: CSP performed on-the-fly for an evolving population of molecules. We have recently shown that we can perform crystal structure prediction at large scale (doi.org/10.1039/D4FD...), so we're now making use of this capability.

6/n

The problem that we tackle here is that materials properties can depends strongly on the crystal structure. So, to evaluate the fitness of molecules in an evolving population, we need to predict their most probably crystal structures.

5/n

Generative ML methods are getting a lot of attention, but evolutionary methods are also effective: create a population of molecules and let them evolve towards a target property of set of properties, through mutations and cross-over operations on the chemical structures.

4/n

With improving reliability of CSP, we want to make better use of these methods to accelerate the discovery of functional materials. We have had success in applying CSP to sets of molecules designed from chemical intuition; now we want approaches that search more broadly for new molecules.

3/n

This paper, led by @jayjohal.bsky.social, presents a major development in a long-term project: integrating crystal structure prediction (CSP) methods for organic molecules into an evolutionary method for exploring chemical space.

2/n

Our best method reaches a top-1 accuracy of 47% and 90% when top 5 space groups are selected. That's very good, given what we know about polymorphism and the tight energetic spacing of structures with different space groups from crystal structure prediction studies.
#compchemsky #machinelearning #ML

Hi. We do currently calculate (upper bounds for) energy barriers between structures. We do get some insight into transition pathways from the calculations, but are doing other work along those lines to get more info on pathways - more to come soon.

Congratulations Dr @jennieemartin.bsky.social on an excellent PhD.