Accepted Manuscript -
Paper
Are we fitting data or noise? Analysing the predictive power of commonly used datasets in drug-, materials-, and molecular-discovery.
Accepted Manuscript -
Paper
Predictive crystallography at scale: mapping, validating, and learning from 1,000 crystal energy landscapes
Accepted Manuscript -
Paper
Integration of generative machine learning with the heuristic crystal structure prediction code FUSE
Accepted Manuscript -
Paper
Mapping inorganic crystal chemical space
About this collection
We are delighted to share with you a selection of the papers associated with a Faraday Discussion on Data-driven discovery in the chemical sciences. More information about the related event may be found here: http://rsc.li/data-fd2024. Additional articles will be added to the collection as they are published. The final versions of all the articles presented and a record of the discussions will be published after the event.
The Discussion will involve four central themes – each focused on different aspects of chemical "discovery", and each aiming to promote the exchange of ideas between the molecular and materials communities: Discovering chemical structure, Discovering structure–property correlations, Discovering synthesis targets, Discovering trends in big data.
On behalf of the Scientific Committee, we hope you join us and participate in this exciting event, and that you enjoy these articles and the record of the discussion.