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12 papers · 1 filter
Combining Hammett constants for -machine learning and catalyst discovery
V. Diana Rakotonirina, Marco Bragato, Stefan Heinen +1
We study the applicability of the Hammett-inspired product (HIP) Ansatz to model relative substrate binding within homogenous organometallic catalysis, assigning and to lig…
Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design
Konstantin Karandashev, Jan Weinreich, Stefan Heinen +4
Optimizing a target function over the space of organic molecules is an important problem appearing in many fields of applied science, but also a very difficult one due to the vast…
Towards self-driving laboratories: The central role of density functional theory in the AI age
Bing Huang, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Density functional theory (DFT) plays a pivotal role for the chemical and materials science due to its relatively high predictive power, applicability, versatility and computationa…
Conformational and state-specific effects in reactions of 2,3-dibromobutadiene with Coulomb-crystallized calcium ions
Ardita Kilaj, Silvan Käser, Jia Wang +7
Recent advances in experimental methodology enabled studies of the quantum-state and conformational dependence of chemical reactions under precisely controlled conditions in the ga…
Kernel based quantum machine learning at record rate : Many-body distribution functionals as compact representations
Danish Khan, Stefan Heinen, O. Anatole von Lilienfeld
The feature vector mapping used to represent chemical systems is a key factor governing the superior data-efficiency of kernel based quantum machine learning (QML) models applicabl…
Recent advances in the Self-Referencing Embedding Strings (SELFIES) library
Alston Lo, Robert Pollice, AkshatKumar Nigam +3
String-based molecular representations play a crucial role in cheminformatics applications, and with the growing success of deep learning in chemistry, have been readily adopted in…