Machine Learning Force Field-Aided Cluster Expansion Approach to Phase Diagram of Alloyed Materials.

First-principles approaches based on density functional theory (DFT) have played important roles in the theoretical study of multicomponent alloyed materials. Considering the highly demanding computational cost of direct DFT-based sampling of the configurational space, it is crucial to build efficient and low-cost surrogate Hamiltonian models with DFT accuracy for efficient simulation of alloyed systems with configurational disorder. Recently, the machine learning force field (MLFF) method has been proposed to tackle complicated multicomponent disordered systems. However, the importance of integrating significant physical considerations, including, in particular, convex hull preservation, which is the prerequisite for the accurate prediction of phase diagrams, into the training process of the MLFF remains rarely addressed. In this work, a workflow is proposed to train a convex-hull-preserved (CHP) MLFF for binary alloy systems, based on which the order-disorder phase boundary is predicted by using the Wang-Landau Monte Carlo (WLMC) technique. The predicted values for order-disorder phase transition temperatures agree well with the experiment. The CHP-MLFF is further used to build CE models with the same accuracy as the MLFF and higher efficiency in sampling configurational space. Using the results obtained from the MLFF-based WLMC simulation as a reference, the performances of different schemes for constructing CE models were evaluated in a transparent manner, which revealed the close correlation between the prediction accuracy of ground-state configurations and that of the order-disorder phase transition temperature. This work clearly indicates the great importance of reproducing the convex hull and energetics of ground-state configurations when constructing surrogate Hamiltonians for the statistical modeling of alloyed systems.

Adsorption and desorption of Hg(II) by four aged microplastics and its effects on gaseous elemental mercury production in seawater.

Microplastics (MPs) weather after entering the environment gradually, and the interaction with metal ions in the aqueous environment has received extensive attention. However, there are few studies on Hg(Ⅱ), especially the effect of MPs on the release of Hg0(DEM) in water after entering the aqueous environment. In this study, four types of MPs (PP, PE, PET, PVC) were selected to study the adsorption and desorption behavior of Hg(Ⅱ) after photoaging and to explore the influence of MPs on the release of DEM in seawater under different lighting conditions. The results showed that the specific surface area, negative charges, and oxygen-containing functional group of MPs increased after aging. The adsorption capacity of aged MPs for Hg(Ⅱ) was significantly improved, which was consistent with the pseudo-first-order and pseudo-second-order model, indicating that the adsorption process was a chemical and physical adsorption. The fitting results of the in-particle diffusion model indicated that the adsorption was controlled by multiple steps. Hg(Ⅱ) was easier to desorb in the simulated gastric fluid environment. Because the aged MPs had the stronger binding force to Hg(Ⅱ), their desorption rate is lower than new MPs. Under visible light and UVA irradiation, MPs inhibited the release of Hg0. Under UVA, the mass of DEM produced in seawater with aged PE and PVC was higher than that of new PE and PVC. The aged PE and PVC could produce more ·O2-, which was conducive to the reduction of mercury. However, in UVB irradiation, the addition of MPs promoted the release of DEM, and ·O2- also played an important contribution in affecting the photochemical reaction of mercury. Therefore, the presence of aged MPs will significantly affect the water-air exchange of Hg in water. Compared with new MPs, aged MPs improved the contribution of free radicals in Hg transformation by releasing reactive oxygen species. This study extends the understanding of the effects of MPs on the geochemical cycle of Hg(Ⅱ) in seawater, better assesses the potential combined ecological risks of MPs and Hg(Ⅱ), and provides certain guidance for the pollution prevention and control of MPs.

Perspective on optimal strategies of building cluster expansion models for configurationally disordered materials.

Cluster expansion (CE) provides a general framework for first-principles-based theoretical modeling of multicomponent materials with configurational disorder, which has achieved remarkable success in the theoretical study of a variety of material properties and systems of different nature. On the other hand, there remains a lack of consensus regarding what is the optimal strategy to build CE models efficiently that can deliver accurate and robust prediction for both ground state energetic properties and statistical thermodynamic properties at finite temperature. There have been continuous efforts to develop more effective approaches to CE model building, which are further promoted by recent tremendous interest of applying machine learning techniques in materials research. In this Perspective, we present a critical review of recent methodological developments in building CE models for multicomponent materials, with particular focus on different approaches and strategies proposed to address cluster selection and training data generation. We comment on the pros and cons of different methods in a general formalism and present some personal views on the prospects of theoretical approaches to multicomponent materials.

Machine Learning Force Field Aided Cluster Expansion Approach to Configurationally Disordered Materials: Critical Assessment of Training Set Selection and Size Convergence.

Cluster expansion (CE) is a powerful theoretical tool to study the configuration-dependent properties of substitutionally disordered systems. Typically, a CE model is built by fitting a few tens or hundreds of target quantities calculated by first-principles approaches. To validate the reliability of the model, a convergence test of the cross-validation (CV) score to the training set size is commonly conducted to verify the sufficiency of the training data. However, such a test only confirms the convergence of the predictive capability of the CE model within the training set, and it is unknown whether the convergence of the CV score would lead to robust thermodynamic simulation results such as order-disorder phase transition temperature Tc. In this work, using carbon defective MoC1-x as a model system and aided by the machine-learning force field technique, a training data pool with about 13000 configurations has been efficiently obtained and used to generate different training sets of the same size randomly. By conducting parallel Monte Carlo simulations with the CE models trained with different randomly selected training sets, the uncertainty in calculated Tc can be evaluated at different training set sizes. It is found that the training set size that is sufficient for the CV score to converge still leads to a significant uncertainty in the predicted Tc and that the latter can be considerably reduced by enlarging the training set to that of a few thousand configurations. This work highlights the importance of using a large training set to build the optimal CE model that can achieve robust statistical modeling results and the facility provided by the machine-learning force field approach to efficiently produce adequate training data.

Investigation of the agglomeration reduction mechanism of the aluminized HTPB propellant containing ferric perfluorooctanoate [Fe(PFO)3].

In this study, ferric perfluorooctanoate [Fe(PFO)3] was used in the aluminized HTPB propellant to reduce Al agglomeration during solid propellant combustion, and the agglomeration reduction mechanism was experimentally demonstrated via the burning rate measurement, heat of explosion and Al agglomeration analysis. The behavior of the burning particles on the burning surface as well as the morphology and composition of the quenched burning particles were characterized by microscopic high-speed photography and X-ray photoelectron spectroscopy, respectively; the thermal decomposition properties and gaseous decomposition products of Fe(PFO)3 were investigated by thermal gravimetry-differential scanning calorimetry joint analysis (TG-DSC), Fourier transform infrared spectroscopy (FTIR) and mass spectrometry (MS). The results show that Fe(PFO)3 can significantly increase the burning rate of the aluminized HTPB propellant and reduce Al agglomeration. The aluminized HTPB propellant containing Fe(PFO)3 exhibited a more efficient aluminum combustion process and smaller solid combustion product generation; the agglomeration reduction mechanism was revealed by the comprehensive effects of Fe(PFO)3 on the thermal decomposition of AP and promotion of the thermite reaction with aluminum. It led to the special "immediate detachment upon ignition" phenomenon of Al particles in the propellant and caused the generation of smaller detached burning Al particles. The highly reactive gaseous decomposition products of Fe(PFO)3 could reduce the accumulation of the generated Al2O3 on the burning Al particles.