Computational chemistry and materials science; virtual high-throughput and Big Data; machine learning; electronic structure theory and methods; quantum effects in catalysis and materials; rational design
Johannes Hachmann is active in the areas of computational chemistry, computational materials science, and applied data science. He is also affiliated with the New York State Center of Excellence in Materials Informatics (CMI) and the Computational and Data-Enabled Science and Engineering (CDSE) Graduate Program.
The Hachmann group’s research fuses (first-principles) molecular and materials modeling with virtual high-throughput screening and modern big data science (i.e., the use of database technology, machine learning, and informatics) to advance a data-driven discovery and rational design paradigm in the chemical and materials disciplines. The primary application focus is on the development of novel molecular materials and catalysts, e.g., for renewable energy technology and advanced electronics. This research aligns directly with the goals of the Materials Genome Initiative.
One of its centerpieces is the creation of an open, general-purpose software ecosystem for the data-driven design of chemical systems and the exploration of chemical space. It consists of 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.
After undergraduate studies at the University of Jena (Germany) and the University of Cambridge (UK), he obtained a Dipl.-Chem. degree in 2004. He then moved to the US to conduct his graduate studies under the supervision of Dr. Garnet Chan at Cornell University. He work on density matrix renormalization group theory and computational transition metal chemistry, and received an MSc in 2007 as well as a PhD in 2010. He subsequently joined the Aspuru-Guzik Group at Harvard University where he spearheaded the Clean Energy Project, a computational high-throughput screening of organic semiconductors for photovoltaic applications.
Dr. Hachmann’s work is well cited and was recognized with several prestigious awards, including the ACS PHYS Postdoctoral Research Award, RSC Scholarship Award for Scientific Excellence of ACS CINF, IBM-Löwdin Award, IBM-Zerner Award, SCES Young Investigator Award, and CCG Excellence Award of ACS COMP. In 2016, he was a Finalist of the Emerging Technologies in Computational Chemistry Competition of ACS COMP.