NIST Develops Advanced Materials Design Framework Using Quantum Computing and AI
The National Institute of Standards and Technology (NIST) has developed a comprehensive framework for designing advanced electronic and functional materials using quantum computing and artificial intelligence. The framework, called JARVIS (Joint Automated Repository for Various Integrated Simulations), automates the materials discovery process by combining classical force-field calculations, density functional theory (DFT), machine learning, and experimental data.
Key Features of JARVIS:
1. JARVIS-FF: Performs thousands of automated force-field calculations using LAMMPS software to determine material properties like energetics, elastic constants, and phonon frequencies.
2. JARVIS-DFT: Conducts thousands of VASP-based DFT calculations for various material systems, including 3D bulk, 2D single layers, 1D nanowires, and 0D molecules. Includes calculations for properties like band-structure, density of states, and thermoelectric properties.
3. JARVIS-ML: Utilizes machine learning models trained on JARVIS-DFT data to predict material properties such as energetics, heat of formation, and bandgaps.
The framework focuses on discovering materials with specific properties, such as charge density wave, IR, piezoelectric, and magnetic materials. By automating the materials discovery process, JARVIS aims to accelerate the development of new quantum materials and devices for various applications, including electronics, energy storage, and quantum computing.
Keywords: Materials Science, Density Functional Theory, Machine Learning, Quantum Computation, Materials Properties
