Machine learning-based neural operators offer transformative potential for accelerating high-entropy alloy simulations while capturing complex many-body interactions beyond classical potentials. We are working on developing physics-informed neural networks (PINNs) and deep potential molecular dynamics (DeePMD) models trained on our FeNiCrCoAl compression data to predict stress-strain responses, defect evolution, and phase transformations at computational costs orders of magnitude lower than traditional MD. Graph neural networks could enable real-time prediction of local atomic environments during extreme deformation, and transfer learning approaches may generalize across compositional spaces, enabling rapid screening of novel HEA formulations for specific mechanical properties without extensive ab-initio calculations.

This study employs molecular dynamics simulations using LAMMPS to investigate laser-based manufacturing processes in aluminum alloys. We simulate two distinct processes: laser welding of butt-jointed aluminum plates and laser melting of spherical aluminum powder particles on a substrate. The simulations utilize the EAM potential to capture realistic melting behavior, incorporating localized laser energy deposition, melt pool dynamics, and thermal gradients. We analyze the evolution of temperature fields, stress distributions, and microstructural changes through coordination number and centro-symmetry parameters. The research provides atomistic insights into phase transformations, solidification patterns, and defect formation during laser-material interactions, contributing to the optimization of additive manufacturing and welding processes.

SolidAdditive AI is a specialized artificial intelligence assistant designed exclusively for solid-state additive manufacturing processes. Unlike conventional AI tools that attempt to cover all manufacturing domains, this application maintains laser-focused expertise on four critical solid-state processes: Cold Spray Additive Manufacturing (CSAM), Ultrasonic Additive Manufacturing (UAM), Friction Stir Additive Manufacturing (FSAM), and Additive Friction Stir Deposition (AFSD).

Built on Google's Gemini 2.5 Flash model, the application features a six-layer enforcement system ensuring 99.9% accuracy in maintaining its solid-state scope, automatically rejecting queries about fusion-based or polymer additive manufacturing. The system offers five specialized analysis modes, i.e., General, Microstructure Analysis, Process Design, Troubleshooting, and Comparison, each optimized with expert-level prompts tailored to specific use cases.

Application 

Additive Friction Stir Deposition (AFSD) builds metal parts layer by layer without melting the material. Instead of liquefying metal like traditional 3D printing or welding, AFSD uses rotation, pressure, and friction to smear solid material onto a surface. The material gets hot but stays solid, which avoids many problems associated with melting and re-solidifying metals. To understand what actually happens to the atoms during this process, we can run molecular dynamics simulations using LAMMPS software. We developed the first atomistic simulation-based approach to model the Additive Friction Stir Deposition (AFSD) process. 

The simulation starts with two main parts: a flat aluminum substrate at the bottom and a cylindrical aluminum rod above it. The substrate represents either the base plate or a previously deposited layer. The bottom of the substrate is frozen in place, so the whole system doesn’t just drift around during the simulation. Everything else can move freely and respond to the forces we apply. This setup mirrors what you would see in a real AFSD machine, i.e., a spinning rod of material being pressed down onto a surface.