论文标题
Gauche：化学中高斯流程的库
GAUCHE: A Library for Gaussian Processes in Chemistry
论文作者
论文摘要
我们介绍了Gauche，这是化学高斯过程的库。长期以来，高斯工艺一直是概率机器学习的基石，为不确定性定量和贝叶斯优化提供了特殊的优势。但是，将高斯过程扩展到化学表示是不平凡的，需要在结构化输入（例如图，字符串和位向量）上定义内核。通过定义Gauche的此类内核，我们寻求为不确定性定量和化学中贝叶斯优化的强大工具打开大门。在实验化学中经常遇到的情况下，我们在分子发现和化学反应优化中展示了Gauche的应用。该代码库可在https://github.com/leojklarner/gauche上找到
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
