SCHEDULED MAINTENANCE
22 items
Accepted Manuscript -
Paper
How to do impactful research in artificial intelligence for chemistry and materials science.
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Making the InChI FAIR and sustainable while moving to Inorganics
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Prediction rigidities for data-driven chemistry
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Specialising and Analysing Instruction-Tuned and Byte-Level Language Models for Organic Reaction Prediction
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Beyond theory driven discovery: introducing hot random search and datum derived structures
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Re-evaluating Retrosynthesis Algorithms with Syntheseus
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Sequence determinants of protein phase separation and recognition by protein phase-separated condensates through molecular dynamics and active learning
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Modelling ligand exchange in metal complexes with machine learning potentials
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Web-BO: Towards increased accessibility of Bayesian optimisation (BO) for chemistry
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Discovery of highly anisotropic dielectric crystals with equivariant graph neural networks
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Accurate and Reliable Thermochemistry by Data Analysis of Complex Thermochemical Networks using Active Thermochemical Tables: The Case of Glycine Thermochemistry
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Knowledge distillation of neural network potential for molecular crystals
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Embedding human knowledge in material screening pipeline as filters to identify novel synthesizable inorganic materials
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
How big is Big Data?
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Optical materials discovery and design via federated databases and machine learning
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Leveraging natural language processing to curate the tmCAT, tmPHOTO, tmBIO, and tmSCO datasets of functional transition metal complexes
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Are we fitting data or noise? Analysing the predictive power of commonly used datasets in drug-, materials-, and molecular-discovery.
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Predictive crystallography at scale: mapping, validating, and learning from 1,000 crystal energy landscapes
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Integration of generative machine learning with the heuristic crystal structure prediction code FUSE
From the themed collection:
Data-driven discovery in the chemical sciences
Accepted Manuscript -
Paper
Mapping inorganic crystal chemical space
From the themed collection:
Data-driven discovery in the chemical sciences
22 items
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.