126 citations · 134 across the 15 of their papers we have counts for
16 papers
Sparse Configuration Interaction for the Electronic Schrödinger Equation Revisited: Complete Basis Set Limit Complexity and Quantum-Encoding Impact
Michael Griebel, Jan Hamaekers
In this article we revisit regularity results for eigenfunctions in the discrete spectrum of the electronic Schrödinger equation and study their consequences for approximation comp…
Gaussian Process Regression-based Knowledge Distillation Framework for Simultaneous Prediction of Physical and Mechanical Properties of Epoxy Polymers
Sindu B. S., Jan Hamaekers
Epoxy polymers are widely used due to their multifunctional properties, but machine learning (ML) applications remain limited owing to their complex 3D molecular structure, multi-c…
Flexible Cutoff Learning: Optimizing Machine Learning Potentials After Training
Rick Oerder, Jan Hamaekers
We introduce Flexible Cutoff Learning (FCL), a method for training machine learning interatomic potentials (MLIPs) whose cutoff radii can be adjusted after training. Unlike convent…
Materium: An Autoregressive Approach for Material Generation
Niklas Dobberstein, Jan Hamaekers
We present Materium: an autoregressive transformer for generating crystal structures that converts 3D material representations into token sequences. These sequences include element…
On Multilevel Energy-Based Fragmentation Methods
James Barker, Michael Griebel, Jan Hamaekers
Energy-based fragmentation methods approximate the potential energy of a molecular system as a sum of contribution terms built from the energies of particular subsystems. Some such…
Feature-based prediction of properties of cross-linked epoxy polymers by molecular dynamics and machine learning techniques
Sindu B. S., Jan Hamaekers
Epoxy polymers are used in wide range of applications. The properties and performance of epoxy polymers depend upon various factors like the type of constituents and their proporti…
LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design
Niklas Dobberstein, Astrid Maass, Jan Hamaekers
Generative models have demonstrated substantial promise in Natural Language Processing (NLP) and have found application in designing molecules, as seen in General Pretrained Transf…
Predicting Properties of Oxide Glasses Using Informed Neural Networks
Gregor Maier, Jan Hamaekers, Dominik-Sergio Martilotti +1
Many modern-day applications require the development of new materials with specific properties. In particular, the design of new glass compositions is of great industrial interest.…
On the Interplay of Subset Selection and Informed Graph Neural Networks
Niklas Breustedt, Paolo Climaco, Jochen Garcke +5
Machine learning techniques paired with the availability of massive datasets dramatically enhance our ability to explore the chemical compound space by providing fast and accurate…
Parameterized Neural Networks for Finance
Daniel Oeltz, Jan Hamaekers, Kay F. Pilz
We discuss and analyze a neural network architecture, that enables learning a model class for a set of different data samples rather than just learning a single model for a specifi…
Interatomic-Potential-Free, Data-Driven Molecular Dynamics
J. Bulin, J. Hamaekers, M. P. Ariza +1
We present a Data-Driven (DD) paradigm that enables molecular dynamics calculations to be performed directly from sampled force-field data such as obtained, e.g., from ab initio ca…
Similarity of particle systems using an invariant root mean square deviation measure
Johannes Bulin, Jan Hamaekers
Determining whether two particle systems are similar is a common problem in particle simulations. When the comparison should be invariant under permutations, orthogonal transformat…
The octet rule in chemical space: Generating virtual molecules
Rafel Israels, Astrid Maaß, Jan Hamaekers
We present a generator of virtual molecules that selects valid chemistry on the basis of the octet rule. Also, we introduce a mesomer group key that allows a fast detection of dupl…
ATK-ForceField: A New Generation Molecular Dynamics Software Package
Julian Schneider, Jan Hamaekers, Samuel T. Chill +5
ATK-ForceField is a software package for atomistic simulations using classical interatomic potentials. It is implemented as a part of the Atomistix ToolKit (ATK), which is a Python…
Localized Coulomb Descriptors for the Gaussian Approximation Potential
James Barker, Johannes Bulin, Jan Hamaekers +1
We introduce a novel class of localized atomic environment representations, based upon the Coulomb matrix. By combining these functions with the Gaussian approximation potential ap…
An Adaptive Multiscale Approach for Electronic Structure Methods
Sambasiva Rao Chinnamsetty, Michael Griebel, Jan Hamaekers
In this paper, we introduce a new scheme for the efficient numerical treatment of the electronic Schrödinger equation for molecules. It is based on the combination of a many-body e…