University at Buffalo

Welcome to the Hachmann Lab!

We are a group pursuing computational and data-driven research in chemistry and materials in the Department of Chemical and Biological Engineering (CBE) at the University at Buffalo. The group is also part of UB's Computational and Data-Enabled Science and Engineering Graduate Program (CDSE), the New York State Center of Excellence in Materials Informatics (CMI), and the UB Institute for Research and Education in eNergy, Environment, and Water (RENEW).

Our research is concerned with one of the most demanding and simultaneously rewarding challenges for computational chemistry: the accurate modeling of coordination compounds and predictive simulation of catalytic processes. We address real-life chemical problems ranging from transition metal complexes with exotic properties to bio-, organo-, and metal-catalysis. A second area of interest is the development of electronic materials, in particular for renewable energy technology. Quantum effects play an important role in both these areas, and we employ cutting-edge computational techniques in carefully designed studies to account for them.

Our work also tackles the inherent methodological and algorithmic issues associated with these applications, and the group is hence home for both applied computational chemists and method developers. Our work combines the traditional use of theory, modeling, and simulation with modern concepts such as virtual high-throughput and Big Data approaches, materials informatics, and machine learning. Our goal is to facilitate a truly rational design of reactions, compounds, and materials at the heart of chemical engineering.

 


The Hachmann Group, Summer 2016

The Hachmann Group, Summer 2016: Vigneshwar, Johannes, Mikhail, Andrew, Atif, Yujie, Dana, Bill, Supriya, Mojtaba, Yudhajit, Chris, and Mark.

Research Areas of Interest

  • computational chemistry, computational materials science
  • electronic structure theory and methods
  • virtual high-throughput screening, hyperscreening
  • cheminformatics, materials informatics
  • big data analytics, data mining, machine learning
  • data-driven discovery, rational design, inverse engineering
  • quantum effects in catalysis, materials, and biological structures
  • molecular and electronic materials
  • renewable energy solutions
  • coordination and transition metal chemistry, molecular magnets
  • metal-, organo-, and biocatalysis

  • Learn more about our research.

     


    Selected Publications

  • M.A. Faiz Afzal, C. Cheng, J. Hachmann, Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers, ChemRxiv (2017), 5446564.v1.
  • R. Asatryan, E. Ruckenstein, J. Hachmann, Revisiting the polytopal rearrangements in penta-coordinate d7-metallocomplexes: modified Berry pseudorotation, octahedral switch, and butterfly isomerization, Chem. Sci. 8 (2017), 5512–5525.
  • J. Hachmann, R. Olivares-Amaya, A. Jinich, A.L. Appleton, M.A. Blood-Forsythe, L.R. Seress, C. Román-Salgado, K. Trepte, S. Atahan-Evrenk, S. Er, S. Shrestha, R. Mondal, A. Sokolov, Z. Bao, A. Aspuru-Guzik, Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project, Energy Environ. Sci. 7 (2014), 698–704.
  • J. Hachmann, R. Olivares-Amaya, S. Atahan-Evrenk, C. Amador-Bedolla, R.S. Sánchez-Carrera, A. Gold-Parker, L. Vogt, A.M. Brockway, A. Aspuru-Guzik, The Harvard Clean Energy Project: large-scale computational screening and design of organic photovoltaics on the World Community Grid, J. Phys. Chem. Lett. 2 (2011), 2241–2251.
  • J. Hachmann, J.J. Dorando, M. Avilés, G.K.-L. Chan, The radical character of the acenes: A density matrix renormalization group study, J. Chem. Phys. 127 (2007), 134309.
  • J. Hachmann, W. Cardoen, G.K.-L. Chan, Multireference correlation in long molecules with the quadratic scaling density matrix renormalization group, J. Chem. Phys. 125 (2006), 144101.

  • See all publications.