arXiv subjects

Moritz René Schäfer

Publications and source records attributed to Moritz René Schäfer.

2 recordsLinked to original sources

arXiv2026

Enhanced Representation-Based Sampling for the Efficient Generation of Datasets for Machine-Learned Interatomic Potentials

In this work, we present Enhanced Representation-Based Sampling (ERBS), a novel enhanced sampling method designed to generate structurally diverse training datasets for machine-learned interatomic potentials. ERBS automatically identifies collective variables by dimensionality reduction of atomic descriptors and applies a bias potential inspired by the On-the-Fly Probability Enhanced Sampling framework. We highlight the ability of Gaussian moment descriptors to capture collective molecular motions and explore the impact of biasing parameters using alanine dipeptide as a benchmark system. We show that free energy surfaces can be reconstructed with high fidelity using only short biased trajectories as training data. Further, we apply the method to the iterative construction of a liquid water dataset and compare the quality of simulated self-diffusion coefficients for models trained with molecular dynamics and ERBS data. Further, we active-learn models for liquid water with and without enhanced sampling and compare the quality of simulated self-diffusion coefficients. The self-diffusion coefficients closely match those simulated with a reference model at a significantly reduced dataset size. Finally, we compare the sampling behaviour of enhanced sampling methods by benchmarking the mean squared displacements of \ce{BMIM+BF4-} trajectories simulated with uncertainty-driven dynamics and ERBS and find that the latter significantly increases the exploration of configurational space.

physics.chem-ph↗

arXiv2025

Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials

We introduce Atomistic learned potentials in JAX (apax), a flexible and efficient open source software package for training and inference of machine-learned interatomic potentials. Built on the JAX framework, apax supports GPU acceleration and implements flexible model abstractions for fast development. With features such as kernel-based data selection, well-calibrated uncertainty estimation, and enhanced sampling, it is tailored to active learning applications and ease of use. The features and design decisions made in apax are discussed before demonstrating some of its capabilities. First, a data set for the room-temperature ionic liquid EMIM+BF4- is created using active learning. It is highlighted how continuously learning models between iterations can reduce training times up to 85 % with only a minor reduction of the models' accuracy. Second, we show good scalability in a data-parallel training setting. We report that a Gaussian Moment Neural Network model, as implemented in apax, achieves higher accuracy and up to 10 times faster inference times than a performance-optimized Allegro model. A recently published Li3PO4 dataset, reported with comparable accuracy and inference performance metrics, is used as a point of comparison. Moreover, the inference speeds of the available simulation engines are compared. Finally, to highlight the modularity of apax, an equivariant message-passing model is trained as a shallow ensemble and used to perform uncertainty-driven dynamics.

physics.chem-ph↗