84 citations · 177 across the 9 of their papers we have counts for
9 papers
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…
Reducing Training Data Needs with Minimal Multilevel Machine Learning (M3L)
Stefan Heinen, Danish Khan, Guido Falk von Rudorff +7
For many machine learning applications in science, data acquisition, not training, is the bottleneck even when avoiding experiments and relying on computation and simulation. Corre…
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
Siwoo Lee, Stefan Heinen, Danish Khan +1
We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed liter…
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…
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…
Geometry Relaxation and Transition State Search throughout Chemical Compound Space with Quantum Machine Learning
S. Heinen, G. F. von Rudorff, O. A. von Lilienfeld
We use energies and forces predicted within response operator based quantum machine learning (OQML) to perform geometry optimization and transition state search calculations with l…
Towards the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld
While sophisticated numerical methods for studying equilibrium states have well advanced, quantitative predictions of kinetic behaviour remain challenging. We introduce a reactant-…
Thousands of reactants and transition states for competing E2 and S2 reactions
Guido Falk von Rudorff, Stefan N. Heinen, Marco Bragato +1
Reaction barriers are a crucial ingredient for first principles based computational retro-synthesis efforts as well as for comprehensive reactivity assessments throughout chemical…
Machine learning the computational cost of quantum chemistry
Stefan Heinen, Max Schwilk, Guido Falk von Rudorff +1
Computational quantum mechanics based molecular and materials design campaigns consume increasingly more high-performance compute resources, making improved job scheduling efficien…