Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science

Austin Cheng, Cher Tian Ser, Marta Skreta, Andrés Guzmán-Cordero, Luca Thiede +6 more
9/16/2024
cs.LGcond-mat.mtrl-scics.AI

No confident code implementation found for this paper.

Abstract

Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspective, we first outline current applications across a diversity of problems in chemistry. Then, we discuss how machine learning researchers view and approach problems in the field. Finally, we provide our considerations for maximizing impact when researching machine learning for chemistry.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Synergizing chemical and AI communities for advancing laboratories of the future

83% match2025

Constructing and explaining machine learning models for chemistry: example of the exploration and design of boron-based Lewis acids

82% match1 repo22025

Machine Learning Potentials: A Roadmap Toward Next-Generation Biomolecular Simulations

81% match2024

Survey on Recent Progress of AI for Chemistry: Methods, Applications, and Opportunities

81% match2025

Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science

81% match2024

Artificial Intelligence in Materials Science and Engineering: Current Landscape, Key Challenges, and Future Trajectorie

81% match2026

Cite this paper

@article{cheng2024spiers,
  title  = {Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science},
  author = {Austin Cheng and Cher Tian Ser and Marta Skreta and Andrés Guzmán-Cordero and Luca Thiede and Andreas Burger and Abdulrahman Aldossary and Shi Xuan Leong and Sergio Pablo-García and Felix Strieth-Kalthoff and Alán Aspuru-Guzik},
  year   = {2024},
  eprint = {2409.10304},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2409.10304v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2409.10304.svg)](https://codeofpaper.com/paper/2409.10304)

Discussion