Laurent Karim Béland

Electrochemical CO2 reduction to hydrocarbons requires catalysts that activate carbon-bound intermediates while suppressing the competing hydrogen evolution reaction (HER). Here, we use density functional theory to study single-metal adatoms on Ag(211). We identify two key effects. First, depositing intrinsically strong CO-binding metals (Ni, Co, and Fe) on Ag weakens CO adsorption relative to the pure metals, moving them toward a more favorable CO-binding regime for further reduction without surface poisoning. Second, early transition metals—especially Ti—behave differently: Ti maintains moderate CO binding but stabilizes CHO much more strongly, effectively decoupling CO and CHO adsorption and deviating from linear scaling relationships. As a result, the CO → CHO activation barrier drops from 1.23 eV on Cu/Ag, taken here as an experimental benchmark for hydrocarbon production, to 0.62 eV on Ti/Ag. Single adatoms bind hydrogen weakly and suppress HER, while larger clusters restore strong H binding and increase HER activity. Our work shows that deposited-metal identity and site size independently control CO activation and HER, providing simple design rules for CO2 reduction catalysts for hydrocarbon production.

Radiation-induced segregation (RIS) is the redistribution of alloying elements near defect sinks under irradiation. It is commonly modeled using rate theory (RT) to describe the spatial and temporal evolution of solute concentrations. The accuracy of RT predictions strongly depends on the reliability of the constants and parameters used in the model. However, inconsistencies in reported parameter values across the literature, stemming from differences in calculation methods, approximations, and the complexity of the studied systems, limit the generalizability of RT models. In this work, we employ atom probe tomography to measure solute distributions at grain boundaries in 304L stainless steel irradiated with 2 MeV protons to a dose of 1.5 displacements per atom (dpa) at 100$^{\circ}$C and 360$^{\circ}$C. These measurements are complemented by additional RIS data from the literature, encompassing a range of temperatures, irradiation types, doses, and alloy chemistries. Two conventional optimization techniques, a gradient-based constrained nonlinear optimizer and a Genetic Algorithm, which is a population-based approach, along with physics-informed neural network (PINN) were employed in this work. Using these methods we extract the migration energies of Fe, Cr, Ni, and Si via vacancy and interstitial mechanisms in irradiated austenitic stainless steels. Our results show that, for a single dataset, PINNs and constrained gradient-based optimization converge to similar migration energy values. Notably, PINNs achieve this convergence approximately 25 times faster. In contrast, genetic algorithms are two orders of magnitude slower and yield less reliable migration energy estimates, with notable discrepancies observed.

Identifying the diffusion path of point defects is a critical step in understanding their evolution and the mechanisms of related phenomena. Defect diffusion occurs at small length and time scales, with impacts on material properties that may continue to evolve over ns to μs, ms, and the continuum scale (s, min, etc., and cm, m, etc.). The time scale accessible to molecular dynamics (MD) simulations is limited by small step sizes, typically in the fs range. Thus, surrogate models of MD simulations through machine learning (ML)-based algorithms are of great interest, especially for complex systems such as high entropy alloys (HEAs). In this work, dynamics governing vacancy migration in HEA were approximated with graph convolutional network (GCN) models as ansatzes for kinetic Monte Carlo (KMC) rate catalogs. Network design considered that diffusion in crystalline solids generally depends on interactions between defects and their immediate neighbor atoms. Graphs represented the vacancy surroundings, MD-generated trajectories provided training and comparison datasets, and unsupervised GCN models approximated interatomic dynamics governing vacancy migration in HEAs as ansatzes for KMC. A proof-of-concept model trained on MD data for the Fe, Ni, Cr, Co, and Cu HEA environment was used with two different neighbor interactions to assess the feasibility of training a GCN to predict vacancy defect transition rates in the HEA environment. The resulting setup rapidly generated MD-formatted synthetic trajectories based on dynamics learned from the MD training set, with a time acceleration of roughly two orders of magnitude and a similar diffusion coefficient to MD observations. Additionally, Nudged Elastic Band (NEB) calculations were performed on randomly generated FeNiCrCoCu HEA structures to determine vacancy migration barriers across nearest-neighbor sites. Transition probabilities for each jump, categorized by atomic type, were extracted from these calculations. NEB-based and GCN-based approaches led to similar outcomes.