Barbara Kirchner | CRC 1639 NuMeriQS

A multifidelity Monte Carlo approach for simulating the diffusion coefficient of water. I. Forward problem

Thu, 01 Jan 2026 00:00:00 +0000

Building Nanoplastic Models for Molecular Calculations

Thu, 01 Jan 2026 00:00:00 +0000

Carbon Polarization and Carbon Nanotube Stacking Impacts the Behavior of Water in Nanoconfinement

Thu, 01 Jan 2026 00:00:00 +0000

Denaturation of Water in Alkaline Melts

Mon, 01 Dec 2025 00:00:00 +0000

MakroLyzer: A Graph-Based Software to Comb through Molecular Hairballs using the Example of Nanoplastics

Mon, 01 Dec 2025 00:00:00 +0000

Beyond Pairwise Interactions: How Interfacial Polarization Modulates Water Flow in Graphene Nanochannels

Fri, 01 Aug 2025 00:00:00 +0000

Explicit Modeling of Electronic Polarization Reveals Water-Mediated Screening of Ion Adsorption at Hexagonal Boron Nitride Interfaces

Fri, 01 Aug 2025 00:00:00 +0000

Formulation of Polarizable Force Fields to Model Simple Ionic Liquid/Graphite Interfaces

Fri, 01 Aug 2025 00:00:00 +0000

Induced Chirality and Vibrational Optical Activity in an Ionic-Liquid Anion

Tue, 01 Jul 2025 00:00:00 +0000

Lessons Learned on Obtaining Reliable Dynamic Properties for Ionic Liquids

Tue, 01 Apr 2025 00:00:00 +0000

A Hybrid Monte Carlo study of argon solidification

Mon, 01 Apr 2024 00:00:00 +0000

Coordination Behavior of a Confined Ionic Liquid in Carbon Nanotubes from Molecular Dynamics Simulations

Mon, 01 Apr 2024 00:00:00 +0000

A01

Sat, 20 Jan 2024 17:07:25 +0100

Investigating energy-device systems with computational methods requires large timescales and an accurate treatment of all the atomic interactions present, thus this project aims to develop methods that can efficiently yield accurate results for this purpose. In order to overcome the limitations of conventional Ab Initio Molecular Dynamics (AIMD), the project will employ Hamiltonian Monte Carlo (HMC) approaches as well as Machine Learning based Force Fields trained using ab initio data from AIMD simulations. Overall this project aims to unlock new possibilities in investigating highly complex systems and to provide valuable insights into materials used in energy devices.

A03

Sat, 20 Jan 2024 17:07:25 +0100

This project aims to develop fast methods to quantify the uncertainty in properties of liquids predicted by computational chemistry methods like Molecular Dynamics (MD) simulations, Ab Initio MD (AIMD) simulations, and Quantum Cluster Equilibrium (QCE) calculations. The focus is on liquids, such as water, alcohols, and ionic liquids. The goal is to improve computational efficiency by using proven and newly developed multi-fidelity approaches to handle random errors in input quantities. By quantifying the resulting uncertainties, the project seeks to gain a better understanding and develop new computational methods in computational chemistry. These methods can have broader applications and be beneficial to other projects of the CRC.

A05

Sat, 20 Jan 2024 17:07:25 +0100

The project aims to improve Ab initio molecular dynamics (AIMD) simulations and the analysis of their results for liquid systems in theoretical chemistry. The approach involves running multiple AIMD simulations simultaneously and using novel graph algorithms to exchange and analyze trajectory data. This will enable the study of more complex molecular systems and emergent structural properties. The goal is to make more efficient use of computer resources, improve application performance, and investigate complex chemical problems. The use of Modular Supercomputing Architecture (MSA) will match diverse workflow requirements and provide valuable insights for next-generation MSA-systems.