Machine Learning in Chemistry


Recent advances in machine learning or artificial intelligence for vision and natural language processing that have enabled the development of new technologies such as personal assistants or self-driving cars have brought machine learning and artificial intelligence to the forefront of popular culture. The accumulation of these algorithmic advances along with the increasing availability of large data sets and readily available high performance computing has played an important role in bringing machine learning applications to such a wide range of disciplines. Given the emphasis in the chemical sciences on the relationship between structure and function, whether in biochemistry or in materials chemistry, adoption of machine learning by chemists. Machine Learning in Chemistry focuses on the following to launch your understanding of this highly relevant topic:Topics most relevant to chemical sciences are the focus.Focus on concepts rather than technical details. Comprehensive referencing provides sources to go to for more technical details.Key details about methods that underlie machine learning (not easy, but important to understand the strengths as well as the limitations of these methods and to identify where domain knowledge can be most readily applied.Familiarity with basic single variable calculus and in linear algebra will be helpful although we have provided step-by-step derivations where they are important

Machine Learning in Chemistry, ACS InFocus (2020)
Heather J. Kulik
Heather J. Kulik
Professor of Chemical Engineering and Chemistry