Ab Initio Investigations for the Role of Compositional Complexities in Affecting Hydrogen Trapping and Hydrogen Embrittlement:A Review

ydrogen embrittlement(HE)seriously restricts the service safety of structural metallic materials applicate in aerospace,ocean,and transportation.Recent studies aiming at increasing the HE-resistance have been focusing on trapping diffusible H atoms by inherent microstructural features in materials.Alloying-induced compositional complexities,including different types of solute atoms,lattice chemical heterogeneities,and carbide precipitates,have attracted research efforts regarding the H trapping capabilities and potential to reduce the susceptibility to HE.In this paper,we review recent progress in exploiting compositional complexities to regulate the hydrogen trapping characteristics and mechanical properties in H-containing environments.The focus is placed on results and insights from ab initio calculations based on density functional theory(DFT).Quantitative predictions of trapping parameters and atomic scale details that are hardly to be gained through traditional experimental characterizations are provided.Additionally,we overview the electronic/atomistic mechanisms of H trapping energetics in metallic materials.Finally,we propose some key challenges and prospects in simulation of defect interactions,interpretation of experimental characterizations,and developing microstructure-based H diffusion prediction models.For the applications of first principle calculations,we illustrate how the DFT data can complement experimental characterizations to guide composition and microstructure design for better HE-resistant materials.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation

Targeted protein degradation(TPD)has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell’s endogenous protein degradation machinery.However,the susceptibility of proteins for targeting by TPD approaches,termed“degradability”,is largely unknown.Here,we developed a machine learning model,model-free analysis of protein degradability(MAPD),to predict degradability from features intrinsic to protein targets.MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds[with an area under the precision–recall curve(AUPRC)of 0.759 and an area under the receiver operating characteristic curve(AUROC)of 0.775]and is likely generalizable to independent non-kinase proteins.We found five features with statistical significance to achieve optimal prediction,with ubiquitination potential being the most predictive.By structural modeling,we found that E2-accessible ubiquitination sites,but not lysine residues in general,are particularly associated with kinase degradability.Finally,we extended MAPD predictions to the entire proteome to find964 disease-causing proteins(including proteins encoded by 278 cancer genes)that may be tractable to TPD drug development.

Phase-Field Modeling of Hydrogen Diffusion and Trapping in Steels

Hydrogen embrittlement of steels is directly linked to hydrogen diffusion and trapping in the microstructure,which can hardly be precisely measured by modern experimental techniques.A phase-field model,in which a chemical potential well of hydrogen in the grain boundaries is introduced,is proposed to simulate hydrogen diffusion and trapping in the polycrystalline iron.It was interestingly found that grain boundaries,as connected trap sites,have a complex influence on the effective diffusivity of hydrogen,which are strongly linked to grain boundary diffusivity and binding energy.