works on

From the 1 of 6 linked papers with an AI index.

activity
20242026
collaborators

6 papers

physics.chem-ph2026

Aromatic Molecule Solvation in Liquid Water with Coupled Cluster Accuracy: The Balance of Pi-Interactions and Hydrophobicity

Nore Stolte, Harald Forbert, Yury Lysogorskiy +2

The paper presents a data‑efficient machine‑learning interatomic potential, trained on CCSD(T) data, that accurately captures the balance of π‑hydrogen bonding and hydrophobic solv…

physics.comp-ph2025

Formation of abiogenic hydrocarbons in supercritical fluids under Earth's upper mantle conditions

Nore Stolte, Tao Li, Ding Pan

The formation of hydrocarbons in Earth's interior has traditionally been considered to have biogenic origins; however, growing evidence suggests that some hydrocarbons may instead…

physics.chem-ph2024

When Theory Meets Experiment: What Does it Take to Accurately Predict H NMR Dipolar Relaxation Rates in Neat Liquid Water from Theory?

Dietmar Paschek, Johanna Busch, Angel Mary Chiramel Tony +5

In this contribution, we compute the H nuclear magnetic resonance (NMR) relaxation rate of liquid water at ambient conditions. We are using structural and dynamical information…

physics.chem-ph2024

Nuclear Quantum Effects in Liquid Water Are Negligible for Structure but Significant for Dynamics

Nore Stolte, János Daru, Harald Forbert +2

Isotopic substitution, which can be realized both in experiment and computer simulations, is a direct approach to assess the role of nuclear quantum effects on the structure and dy…

physics.bio-ph2024

Synthesis and stability of biomolecules in C-H-O-N fluids under Earth's upper mantle conditions

Tao Li, Nore Stolte, Renbiao Tao +3

How life started on Earth is an unsolved mystery. There are various hypotheses for the location ranging from outer space to the seafloor, subseafloor or potentially deeper. Here, w…

physics.chem-ph2024

Random sampling versus active learning algorithms for machine learning potentials of quantum liquid water

Nore Stolte, János Daru, Harald Forbert +2

Training accurate machine learning potentials requires electronic structure data comprehensively covering the configurational space of the system of interest. As the construction o…