Johannes Hachmann

Assistant Professor

University at Buffalo, SUNY

Dr. Johannes Hachmann is an Assistant Professor of Chemical Engineering at
the University at Buffalo, The State University of New York, and his research
spans the areas of computational chemistry, computational materials science,
and applied data science. After undergraduate studies at the University of Jena
(Germany) and the University of Cambridge (UK), he earned a Dipl.-Chem.
degree in 2004, and then conducted graduate studies under the supervision of
Prof. Garnet Chan at Cornell University. He work on density matrix
renormalization group theory and computational transition metal chemistry,
and received an M.Sc. in 2007 and a Ph.D. 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 joined the
Department of Chemical and Biological Engineering at Buffalo in 2014 and he is
also affiliated with the New York State Center of Excellence in Materials
Informatics and the Computational and Data-Enabled Science and Engineering
Graduate Program. The research of the Hachmann Group 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. The
research of the Hachmann group aligns directly with the goals of the Materials
Genome Initiative. One of the centerpieces of this work 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.