Tuesday 2:30 PM

March 1, 2022

Room: 255C

Location: Anaheim Convention Center

Accurate phase diagram calculation from molecular dynamics requires systematic treatment and convergence of statistical averages. In this work we propose a Gaussian process regression based framework for reconstructing the free energy functions using data of various origin. Our framework allows for propagating statistical uncertainty from finite molecular dynamics trajectories to the phase diagram and automatically performing convergence with respect to simulation parameters. Furthermore, our approach provides a way for automatic optimal sampling in the simulation parameter space based on Bayesian optimization approach. We validate our methodology by constructing phase diagrams of two model systems, the Lennard-Jones and soft-core potential, and compare the results with existing works studies and our coexistence simulations. Finally, we construct the phase diagram of lithium at temperatures above 300 K and pressures below 30 GPa from a machine-learning potential trained on ab initio data. Our approach performs well when compared to coexistence simulations and experimental results.

The development of a consistent framework for Calphad model sensitivity is necessary for the rational reduction of uncertainty via new models and experiments. In the present work, a sensitivity theory for Calphad was developed, and a closed-form expression for the log-likelihood gradient and Hessian of a multi-phase equilibrium measurement was derived. A case study of the Cr-Ni system was used to demonstrate visualizations and analyses enabled by the developed theory. Criteria based on the classical Cramér–Rao bound were shown to be a useful diagnostic in assessing the accuracy of Bayesian parameter covariance estimates from Markov Chain Monte Carlo. The developed sensitivity framework was applied to estimate the statistical value of phase equilibria measurements in comparison with thermochemical measurements, with implications for Calphad model uncertainty reduction, as well as the design of new experiments.

Defects in a crystal, like point, line, and planar defects, alter the local atomic structure and hence must alter average elastic constants. In this work, we consider a solid with defects as a composite wherein the defected regions have a bulk modulus, K, different from that of the perfect crystal. If the volume fraction of the defected regions is sufficiently small, Vegards Law should apply so that K of the composite depends linearly on the volume fraction. We identify the slope of this linear dependence as the signature, denoted S. Atomistic models of EAM Ni were used to compute K vs. reciprocal volume for several kinds of defects. The results show that S has a distinct value for each defect and is negative for vacancies but positive for the other defects. This data can be used to correlate elastic properties measurements to defect populations under radiation environments.

The grain boundary (GB) counterparts to bulk phase diagrams are important materials science tool to optimize the properties of polycrystalline materials. In this talk, we will first review (i) an atomistic simulation study to construct GB diagrams of Si-Au system and (ii) a data-driven study to predict the GB diagrams of Cu-Ag system as a function of GB five crystallographic degrees of freedom (DOFs) by using a genetic algorithm-guided deep learning technique. Second, we will discuss our recent study to compute GB mechanical properties diagrams for a classical GB embrittlement system Al-Ga alloy. Finally, we will discuss how to use machine learning techniques to predict GB diagrams of high-entropy Cantor alloys (CrMnFeCoNi) as a function of temperature and four independent compositional DOFs in 5-D space. The coupling effect between GB segregation of multiple element and disordering in HEA will be discussed along with a surrogate data-based analytical model (DBAM).

Grain boundaries greatly influence many properties of engineering materials. Accurate prediction of their structure and possible transitions using atomistic modeling are important for strategies that aim to improve properties of materials. Recent years have seen a rapid growth of evidence suggesting that materials interfaces are capable of first-order structural transformations in which the interface properties undergo discontinuous changes. Experiments have linked these transitions to abnormal grain growth, activated sintering and liquid metal embrittlement and raised a number of fundamental questions concerning the atomic structures and kinetic properties of these interface phases. In this work, we expand our recent model of grain boundary phase nucleation to include heterogeneous grain boundary phase nucleation in elastically anisotropic materials, and additionally consider the possible metastability of patterns of grain boundary phase nuclei. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.