arXiv 2212.044502022-12physics.chem-phcond-mat.mtrl-scics.LG

GAUCHE：高斯过程库

GAUCHE: A Library for Gaussian Processes in Chemistry

University of Cambridge（剑桥大学）；University of Oxford（牛津大学）；Secondmind Labs（Secondmind实验室）；Stanford University（斯坦福大学）；Genentech（基因泰克）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；EPFL（洛桑联邦理工学院）；Cornell University（康奈尔大学）；Washington State University（华盛顿州立大学）；King’s College London（伦敦国王学院）；University College London（伦敦大学学院）；Evonik Industries AG（赢创工业集团）；Syracuse University（雪城大学）；IST Austria（奥地利科学技术研究所）；MILA Quebec AI Institute（魁北克人工智能研究所MILA）；HEC Montreal（蒙特利尔高等商学院）

Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Samuel Stanton, Gary Tom, Bojana Rankovic, Yuanqi Du, Arian Jamasb, Aryan Deshwal, … 展开作者

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

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摘要：

我们介绍GAUCHE，一个库的高斯过程在CHESTRY。高斯过程长期以来一直是概率机器学习的基石，为不确定性量化和贝叶斯优化提供了特别的优势。然而，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（如图形，字符串和位向量）上定义内核。通过在GAUCHE中定义这样的内核，我们试图为化学中的不确定性量化和贝叶斯优化打开强大工具的大门。受实验化学中经常遇到的场景的启发，我们展示了GAUCHE在分子发现和化学反应优化中的应用。代码库可在https://github.com/leojklarner/gauche上获得

英文摘要：

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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