Improving the reliability of machine learned potentials for modeling inhomogeneous liquids.

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids-including the density response, surface tension and size-dependent cavitation free energies-for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

First-principles molten salt phase diagrams through thermodynamic integration.

Precise prediction of phase diagrams in molecular dynamics simulations is challenging due to the simultaneous need for long time and large length scales and accurate interatomic potentials. We show that thermodynamic integration from low-cost force fields to neural network potentials trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid-liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange-correlation functionals for predicting the NaCl phase boundary and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid-liquid phase boundaries for any material at an ab initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

Measurement of Uranium Isotopes in Particles of U3O8 by Secondary Ion Mass Spectrometry-Single-Stage Accelerator Mass Spectrometry (SIMS-SSAMS).

A commercial secondary ion mass spectrometer (SIMS) was coupled to a ± 300 kV single-stage accelerator mass spectrometer (SSAMS). Positive secondary ions generated with the SIMS were injected into the SSAMS for analysis. This combined instrument was used to measure the uranium isotopic ratios in particles of three certified reference materials (CRM) of uranium, CRM U030a, CRM U500, and CRM U850. The ability to inject positive ions into the SSAMS is unique for AMS systems and allows for simple analysis of nearly the entire periodic table because most elements will readily produce positive ions. Isotopic ratios were measured on samples of a few picograms to nanograms of total U. Destruction of UH(+) ions in the stripper tube of the SSAMS reduced hydride levels by a factor of ∼3 × 10(4) giving the UH(+)/U(+) ratio at the SSAMS detector of ∼1.4 × 10(-8). These hydride ion levels would allow the measurement of (239)Pu at the 10 ppb level in the presence of U and the equivalent of ∼10(-10 236)U concentration in natural uranium. SIMS-SSAMS analysis of solid nuclear materials, such as these, with signals nearly free of molecular interferences, could have a significant future impact on the way some measurements are made for nuclear nonproliferation.