Johannes Hachmann | AIChE

Johannes Hachmann

Citation name

Hachmann, J.

Affiliation

University at Buffalo, SUNY

State

NY

Country

USA

Johannes Hachmann is an Associate Professor of Chemical Engineering at the University at Buffalo (UB), the Director of the Engineering Science in Data Science graduate program, a Core Member of the UB Computational and Data-Enabled Science and Engineering graduate program, and a Faculty Member of the New York State Center of Excellence in Materials Informatics. He earned a Dipl.-Chem. degree (2004) after undergraduate studies at the universities of Jena and Cambridge, M.Sc. (2007) and Ph.D. (2010) degrees in Chemistry from Cornell University, and he conducted postdoctoral research at Harvard University before joining the UB faculty in 2014. The research of the Hachmann Group fuses (first-principles) molecular and materials modeling with virtual high-throughput screening and modern 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.

Groups/Topicals chaired or co-chaired

Sessions chaired or co-chaired

Applications of Data Science in Molecular Sciences I
2018 AIChE Annual Meeting (ISBN: 978-0-8169-1108-0)
Applications of Data Science in Molecular Sciences I
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
Applications of Data Science in Molecular Sciences I
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
Applications of Data Science in Molecular Sciences I
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
Applications of Data Science in Molecular Sciences II
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
Applications of Data Science in Molecular Sciences II
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
Applications of Data Science in Molecular Sciences II
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
Applications of Data Science in Molecular Sciences II
2020 Virtual AIChE Annual Meeting (ISBN: 978-0-8169-1114-1)
Applications of Data Science to High Throughput Experimentation
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
Applications of Data Science to High Throughput Experimentation
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
Applications of Data Science to Molecules and Materials
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
Data Mining and Machine Learning in Molecular Sciences I
2021 Annual Meeting (ISBN: 978-0-8169-1116-5)
Machine Learning for Soft and Hard Materials
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
Machine Learning for Soft and Hard Materials II
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)

Areas chaired or co-chaired

Applications of Data Science to Molecules and Materials
2019 AIChE Annual Meeting (ISBN: 978-0-8169-1112-7)
Applications of Data Science to Molecules and Materials
2022 Annual Meeting (ISBN: 978-0-8169-1118-9)
Applications of Data Science to Molecules and Materials
2023 AIChE Annual Meeting (ISBN: 978-0-8169-1120-2)

Authored

Associated proceedings 

2018 AIChE Annual Meeting
2019 AIChE Annual Meeting
2020 Virtual AIChE Annual Meeting
2021 Annual Meeting
2022 Annual Meeting
2023 AIChE Annual Meeting