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.
This comprehensive guide demonstrates how to construct aluminium slab and bulk structures using the Atomic Simulation Environment (ASE) in Python. We explore automated surface generation methods, structural transformations, and best practices for creating simulation-ready geometries in VASP format.
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.