Machine learning interatomic potentials (MLIPs) are rapidly changing the landscape of materials simulation. This post provides a practical walkthrough of using FAIRChem, a powerful MLIP model, to perform geometry optimisation on crystal structures, building upon our previous introduction to the topic.
FAIRChem is a powerful machine learning framework developed by Meta AI for accelerating materials discovery and catalysis research. This comprehensive guide walks you through using FAIRChem, from exploring the web-based demo to performing local energy calculations on macOS.
Crystal structure modeling is a fundamental skill in materials science and computational chemistry. VESTA (Visualization for Electronic and STructural Analysis) is a powerful, user-friendly software tool that enables researchers to create, visualize, and manipulate crystal structures with ease. This guide demonstrates how to build a face-centered cubic (FCC) aluminum structure from scratch using VESTA.
Setting up a reproducible computational environment is crucial for density functional theory (DFT) calculations and materials science research. This guide walks you through creating a dedicated Conda environment equipped with essential tools for DFT analysis, visualization, and machine learning applications in computational chemistry.