arXiv subjects

Yezhi Jin

Publications and source records attributed to Yezhi Jin.

4 recordsLinked to original sources

arXiv2026

Fortuitous Cancellation of Competing Effects in the Free Energy of Water Autoionization: Long-Range Electrostatics, System Size, and Nuclear Quantum Effects

Few molecular processes are more fundamental to chemistry than the autoionization of water, whose equilibrium constant, $K_w \approx 10^{-14}$, underlies acid-base equilibria and the chemistry of life. Yet the molecular origins of the free energy cost of forming and separating ions remain incompletely understood. Efforts to explain it have been limited by the difficulty of modeling water reactivity in sufficient detail. Here we combine machine learning interatomic potentials (MLIPs) with spectral adaptive biasing force enhanced sampling to connect proton transfer and water wire configurations to the underlying free energy landscape. We identify competing influences of long-range electrostatics, system size, temperature, and nuclear quantum motion on the predicted ionization thermodynamics. The free energy surface extends from neutral water to ions separated beyond the solvent-separated ion pair (SSIP). It reveals a transition state in which a proton is shared between the second and third molecules of a three-membered water wire. The contact ion pair is unstable and the SSIP only weakly metastable, so determining pK$_w$ requires sampling longer wires. Long-range electrostatics raise the free energy at ion separations beyond $3\,\mathrm{\mathring{A}}$. Models with comparable force errors give substantially different free energy profiles in this region. Increasing system size lowers the calculated pK$_w$, while nuclear quantum effects bring it closer to experiment and reproduce the higher pK$_w$ of D$_2$O relative to H$_2$O. These results establish a baseline for interpreting thermodynamic results from MLIP simulations in which charged and polar groups react and diffuse in finite cells. Our analysis also explains how cancellation among competing effects can yield ionization constants close to experiment even when simulations omit several physical contributions.

physics.chem-ph↗

arXiv2025

Autodifferentiable Geometric Restraints for Enhanced Sampling Simulations with Classical and Machine Learned Force Fields

The use of external restraints is ubiquitous in advanced molecular simulation techniques. In general, restraints serve to reduce the configurational space that is available for sampling, thereby reducing the computational demands associated with a given simulations. Examples include the use of positional restraints in docking simulations or positional restraints in studies of catalysis. Past work has sought to couple complex restraining potentials with enhanced sampling methods, including Metadynamics or Extended Adaptive Biasing Force approaches. Here, we introduce the use of more general geometric potentials coupled with enhanced sampling methods that incorporate neural networks or spectral decomposition to achieve more efficient sampling in the context of advanced materials design.

physics.comp-ph↗

arXiv2024

The Importance of Learning without Constraints: Reevaluating Benchmarks for Invariant and Equivariant Features of Machine Learning Potentials in Generating Free Energy Landscapes

Machine-learned interatomic potentials (MILPs) are rapidly gaining interest for molecular modeling, as they provide a balance between quantum-mechanical level descriptions of atomic interactions and reasonable computational efficiency. However, questions remain regarding the stability of simulations using these potentials, as well as the extent to which the learned potential energy function can be extrapolated safely. Past studies have reported challenges encountered when MILPs are applied to classical benchmark systems. In this work, we show that some of these challenges are related to the characteristics of the training datasets, particularly the inclusion of rigid constraints. We demonstrate that long stability in simulations with MILPs can be achieved by generating unconstrained datasets using unbiased classical simulations if the fast modes are correctly sampled. Additionally, we emphasize that in order to achieve precise energy predictions, it is important to resort to enhanced sampling techniques for dataset generation, and we demonstrate that safe extrapolation of MILPs depends on judicious choices related to the system's underlying free energy landscape and the symmetry features embedded within the machine learning models.

physics.comp-ph↗

arXiv2023

PySAGES: flexible, advanced sampling methods accelerated with GPUs

Molecular simulations are an important tool for research in physics, chemistry, and biology. The capabilities of simulations can be greatly expanded by providing access to advanced sampling methods and techniques that permit calculation of the relevant underlying free energy landscapes. In this sense, software that can be seamlessly adapted to a broad range of complex systems is essential. Building on past efforts to provide open-source community supported software for advanced sampling, we introduce PySAGES, a Python implementation of the Software Suite for Advanced General Ensemble Simulations (SSAGES) that provides full GPU support for massively parallel applications of enhanced sampling methods such as adaptive biasing forces, harmonic bias, or forward flux sampling in the context of molecular dynamics simulations. By providing an intuitive interface that facilitates the management of a system's configuration, the inclusion of new collective variables, and the implementation of sophisticated free energy-based sampling methods, the PySAGES library serves as a general platform for the development and implementation of emerging simulation techniques. The capabilities, core features, and computational performance of this new tool are demonstrated with clear and concise examples pertaining to different classes of molecular systems. We anticipate that PySAGES will provide the scientific community with a robust and easily accessible platform to accelerate simulations, improve sampling, and enable facile estimation of free energies for a wide range of materials and processes.

physics.comp-ph↗