ReviewDigital discovery2022
Natural language processing models that automate programming will transform chemistry research and teaching.
Review in Digital discovery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Looking back and to the future after four-plus years of language in chemistry.Digital discovery · 2026Review
- Word embeddings as autonomous predictors in materials design-the effect of inherent variability on information transfer.Journal of cheminformatics · 2026Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- A ChatGPT Assisted Reading Protocol for Undergraduate Research Students.bioRxiv : the preprint server for biology · 2024Article
- A human-machine interface for automatic exploration of chemical reaction networks.Nature communications · 2024Article
- Accelerated chemical science with AI.Digital discovery · 2024Review
- Augmenting large language models with chemistry tools.Nature machine intelligence · 2024Article
- Language models and protocol standardization guidelines for accelerating synthesis planning in heterogeneous catalysis.Nature communications · 2023Article
- 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon.Digital discovery · 2023Review
- Assessment of chemistry knowledge in large language models that generate code.Digital discovery · 2023Article
- Google Colab and Virtual Simulations: Practical e-Learning Tools to Support the Teaching of Thermodynamics and to Introduce Coding to Students.ACS omega · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Natural language processing models have emerged that can generate useable software and automate a number of programming tasks with high fidelity. These tools have yet to have an impact on the chemistry community. Yet, our initial testing demonstrates that this form of artificial intelligence is poised to transform chemistry and chemical engineering research. Here, we review developments that brought us to this point, examine applications in chemistry, and give our perspective on how this may fundamentally alter research and teaching.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.