GAUCHE: A Library for Gaussian Processes in Chemistry
Abstrak
We introduce GAUCHE, a library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to chemical representations, however, is nontrivial, necessitating kernels defined over structured inputs such as graphs, strings and bit vectors. By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry. Motivated by scenarios frequently encountered in experimental chemistry, we showcase applications for GAUCHE in molecular discovery and chemical reaction optimisation. The codebase is made available at https://github.com/leojklarner/gauche
Topik & Kata Kunci
Penulis (27)
Ryan-Rhys Griffiths
Leo Klarner
Henry B. Moss
Aditya Ravuri
Sang Truong
Samuel Stanton
Gary Tom
Bojana Rankovic
Yuanqi Du
Arian Jamasb
Aryan Deshwal
Julius Schwartz
Austin Tripp
Gregory Kell
Simon Frieder
Anthony Bourached
Alex Chan
Jacob Moss
Chengzhi Guo
Johannes Durholt
Saudamini Chaurasia
Felix Strieth-Kalthoff
Alpha A. Lee
Bingqing Cheng
Alán Aspuru-Guzik
Philippe Schwaller
Jian Tang
Akses Cepat
- Tahun Terbit
- 2022
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓