As large language models begin to plan and run experiments on their own, the reflex is to demand that they be interpretable before they are trusted. We argue that what grounds trust in autonomous scie...

Carnegie Mellon chemistry professors, Olexandr Isayev and Gabriel Gomes, are making breakthroughs as part of the NSF Center for Computer Assisted Synthesis.

Other researchers question whether autonomous AI scientists are possible or even desirable.

This Perspective highlights the potential integrations of large language models (LLMs) in chemical research and provides guidance on the effective use of LLMs as research partners, noting the ethical and performance-based challenges that must be addressed moving forward.

Modern optimization in experimental chemistry employs algorithmic search through black-box parameter spaces. Here we demonstrate that pre-trained knowledge in large language models (LLMs) fundamentall...

In Nature Computational Science, Gomes and his coauthors offer a roadmap toward more strategic implementations of LLMs.

This Perspective highlights the potential integrations of large language models (LLMs) in chemical research and provides guidance on the effective use of LLMs as research partners, noting the ethical ...

In Nature Machine Intelligence, Gabe Gomes, Daniil Boiko and collaborators propose a new type of molecular ML representation.

YouTube video by Deafheaven

YouTube video by Deafheaven

Machine learning model Catnip helps chemists find biocatalysts to try in synthesis

Machine learning model Catnip helps chemists find biocatalysts to try in synthesis

Machine learning model Catnip helps chemists find biocatalysts to try in synthesis

Thalidomide: A Chiral Molecule with a Complex Toxicity Profile Thalidomide, a glutamic acid derivative, has a long and controversial history. Initially marketed as a sedative and antiemetic in the 195...

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Shared via ChatGPT

Sustainability and artificial intelligence dominate our seventh annual round-up of exciting innovations.

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. We report efforts to overcome these limitations and leverage DL to...