113 citations · 220 across the 6 of their papers we have counts for
6 papers
How Atoms Interact Within Molecules
Adil Kabylda, Malte Esders, Matteo Gori +3
Fundamental understanding of interatomic forces in molecules must emerge from quantum mechanics, yet widely used empirical force fields rely on simplified mechanistic approximation…
Analyzing Atomic Interactions in Molecules as Learned by Neural Networks
Malte Esders, Thomas Schnake, Jonas Lederer +4
While machine learning (ML) models have been able to achieve unprecedented accuracies across various prediction tasks in quantum chemistry, it is now apparent that accuracy on a te…
Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points
Malte Esders, Gimmy Alex Fernandez Ramirez, Michael Gastegger +1
Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data. We use a structured ap…
Autonomous robotic nanofabrication with reinforcement learning
Philipp Leinen, Malte Esders, Kristof T. Schütt +3
The ability to handle single molecules as effectively as macroscopic building-blocks would enable the construction of complex supramolecular structures inaccessible to self-assembl…
From Clustering to Cluster Explanations via Neural Networks
Jacob Kauffmann, Malte Esders, Lukas Ruff +3
A recent trend in machine learning has been to enrich learned models with the ability to explain their own predictions. The emerging field of Explainable AI (XAI) has so far mainly…
Dynamic Pricing and Learning with Competition: Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference
Ruben van de Geer, Arnoud V. den Boer, Christopher Bayliss +10
This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017 in…