292 citations · 709 across the 9 of their papers we have counts for
10 papers
14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon
Kevin Maik Jablonka, Qianxiang Ai, Alexander Al-Feghali +50
Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To…
Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks
Mayk Caldas Ramos, Andrew D. White
Aqueous solubility is a valuable yet challenging property to predict. Computing solubility using first-principles methods requires accounting for the competing effects of entropy a…
Active Learning in Symbolic Regression with Physical Constraints
Jorge Medina, Andrew D. White
Evolutionary symbolic regression (SR) fits a symbolic equation to data, which gives a concise interpretable model. We explore using SR as a method to propose which data to gather i…
Bloom filters for molecules
Jorge Medina, Andrew D White
Ultra-large chemical libraries are reaching 10s to 100s of billions of molecules. A challenge for these libraries is to efficiently check if a proposed molecule is present. Here we…
Censoring chemical data to mitigate dual use risk
Quintina L. Campbell, Jonathan Herington, Andrew D. White
Machine learning models have dual-use potential, potentially serving both beneficial and malicious purposes. The development of open-source models in chemistry has specifically sur…
ChemCrow: Augmenting large-language models with chemistry tools
Andres M Bran, Sam Cox, Oliver Schilter +3
Over the last decades, excellent computational chemistry tools have been developed. Integrating them into a single platform with enhanced accessibility could help reaching their fu…
Bayesian Optimization of Catalysis With In-Context Learning
Mayk Caldas Ramos, Shane S. Michtavy, Marc D. Porosoff +1
Large language models (LLMs) can perform accurate classification with zero or few examples through in-context learning. We extend this capability to regression with uncertainty est…
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
Alston Lo, Robert Pollice, AkshatKumar Nigam +3
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted in…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…
Federated Learning of Molecular Properties with Graph Neural Networks in a Heterogeneous Setting
Wei Zhu, Jiebo Luo, Andrew White
Chemistry research has both high material and computational costs to conduct experiments. Institutions thus consider chemical data to be valuable and there have been few efforts to…