Author

Jim Pfaendtner

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Open access Oct 2026

Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules

Molecular simulation is a powerful tool to describe chemical and physical processes across length scales. Fundamental tradeoffs between the accuracy of quantum mechanics and speed of classical models makes simulating biosystems very difficult. Machine learning potentials (MLPs) offer a potential solution which combines...

Lejla S. Biberic, Nisarg Joshi, Jim Pfaendtner · RSC Advances · 0 citations

Open access Oct 2026

Beyond Energies and Forces: Systematic Evaluation of Machine-Learned Potentials for Solid–Liquid Interfaces

Atomistic simulations have proven to be a powerful tool in obtaining chemical insights for complex solid–liquid interfaces. However, studying solid–liquid interfaces with first-principles methods is a challenging task due to the underlying computational limitations. Empirical force fields have been extensively applie...

Nisarg Joshi, Rahul Verma, Jim Pfaendtner · ACS Physical Chemistry Au · 0 citations