Published January 26, 2017
The Johannes Hachmann lab group aims to chart new paths in data-driven in silico research and a rational design paradigm by creating an open, general-purpose software ecosystem, filling the prevalent infrastructure gap, and thus making data-driven research a viable and widely accessible proposition.
Trial-and-error research approaches are increasingly ill equipped to meeting the complex challenges involved in the discovery and design of next-generation chemistry and materials.
The software ecosystem fuses in silico modeling (in particular computational quantum chemistry), high-throughput screening techniques, and Big Data analytics into an integrated research infrastructure. The group has been developing the necessary methods, algorithms, protocols, and codes, and assembled them in three loosely connected program suites: ChemHTPS provides an automated platform for the virtual high-throughput screening of compound and material candidate libraries as well as reaction networks; ChemBDDB offers a database and data model template for the massive information volumes created by data-intensive projects; and ChemML is a machine learning and informatics toolbox for the validation, analysis, mining, and modeling of such data sets.
Contributors to this research are Mojtaba Haghighatlari, William Evangelista, Mohammad Atif Faiz Afzal, Ching-Yen Shih, Bryan A. Moore, Mikhail Pechagin, Yujie Tian, and Johannes Hachmann.