Graeme Day

We are looking forward to receiving your digital chemistry Lectureship nominations!
Please see here for details: rsc.li/chemsci-lectu...

#CompChem #MLChem #AIChem #MachineLearning

It's great to see Pedro's work on this week's cover of @chemicalscience.rsc.org

🔥 New and HOT in Chemical Science!

“Exciton trapping with a twist” by Eric Vauthey et al. from the University of Geneva.

Read it for free here: pubs.rsc.org/doi/D5S...

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.

It's great to see this preprint out. doi.org/10.26434/che...

This work is a step towards making crystal structure prediction more affordable for complex molecular materials where the unit cell contains multiple symmetry-independent molecules.

Congratulations @stochasticchemist.bsky.social

The Chemical Science team welcomes Xianfeng Li from the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, China as an Associate Editor!

Professor Li will be handling research on electrochemical energy storage and batteries.

This is great, @jennieemartin.bsky.social. Thanks for putting the time and work into this.
@unisouthampton.bsky.social

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

Schematic of an evolutionary algorithm for generating new organic molecules, with crystal structure prediction integrated into the fitness function calculation.

I'm excited to share the latest paper from our team, just published in Nature Communications: rdcu.be/eRTSs

"Exploring organic chemical space for materials discovery using crystal structure prediction-informed evolutionary optimisation"

#compchemsky #chemsky

1/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

A new preprint from our team @unisouthampton.bsky.social

Can machine learning predict the space group preference of organic molecules?

Work by Hannah Gittins exploring random forest and graph neural network models to predict space group preferences of organic molecules.
doi.org/10.26434/che...

Congratulations @jennieemartin.bsky.social on this publication.

This work develops a similarity kernel for comparing molecular crystal structures, with evaluation on several ML tasks applied to CSP.

It's great to see this out now in Crystal Growth & Design @acs.org.

#chemSky #compChemSky

Just about ready for our first workshop on mol-cspy: our source software for crystal structure prediction
gitlab.com/mol-cspy/mol...
A massive thank you to the research team in getting material together for this.
#compchemsky #chemsky

If you're attending the Materials and Molecular Modelling Hub #MMMHub conference this week, go listen to Jordan Dorrell: "Sensible Asymmetric Units for Crystal Exploration". These are new methods in crystal structure prediction aimed at better efficiency for complex structures. #compchemSky #ChemSky

Image of speaker at conference in front of projected slide showing computer simulation results on molecular crystals.

Thank you to the organisers of the "from molecules to materials" meeting in Bologna for inviting me to give a keynote talk.
Among other things, I spoke about @aichemyhub.bsky.social-funded large-scale crystal structure prediction and transferable ML potentials: doi.org/10.1039/D4FD...
#compchemsky

Congratulations @jennieemartin.bsky.social on this work.

The study adapts the SOAP (smooth overlap of atomic positions) kernel to molecular crystals and evaluates the resulting kernel for applications to crystal structure prediction landscapes.
doi.org/10.26434/che...

#CompChemSky #ChemSky

Congratulations @aicooper.bsky.social on the award of the @royalsociety.org Davy Medal "for creating innovative digital approaches to chemistry that combine first-principles computational chemistry, autonomous robots and artificial intelligence." #RSMedals
A very well deserved award!

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.

Graph showing the connections between crystal structures as a function of increasing energy. Each connection is represented as a node connecting lines from initial starting structures.

If you're at the 25th European crystallographic meeting in Poznan, I'll recommend MS43 – "Simulating and predicting structure" at 14:00 on Wed.

Pedro Juan Royo from our group will be presenting on our methods for mapping the interconnectivity of predicted crystal structures.
#CompChemSky #ChemSky

Phd candidate stood smiling with two examiners.

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

Thanks for the comments!

‘CrystalGPT’ set to enhance how chemists design crystals in silico Model for predicting molecular crystal properties is readily adaptable to specific tasks, even with limited data

@chemistryworld.com article on our recent @chemicalscience.rsc.org paper. #compchemsly #chemsky

'CrystalGPT’ set to enhance how chemists design crystals in silico
www.chemistryworld.com/news/crystal...

Exploring organic chemical space for materials discovery using crystal structure prediction-informed evolutionary optimisation Organic molecular crystals offer a broad spectrum of potential applications. The vast number of possible molecules is both an opportunity and a challenge, because of the prohibitive expense of exhaust...

corrected link to Jay's preprint: doi.org/10.26434/che...