A pedagogical derivation clarifies how time-reversal symmetry breaking emerges from dynamical limits in transport coefficients and why this matters for modern computational materials science. Application
How intelligent data placement unlocks near-peak performance with limited high-bandwidth memory. Application sectors: HPC systems & supercomputing, Computational materials science & physics simulations, Data-intensive engineering.
Application sectors: Quantum computing, Energy and power transmission, Medical imaging and research infrastructure Keywords: superconductivity, high-throughput, electron-phonon, HPC, materials discovery Superconductors underpin transformative technologies such
MaX researchers developed machine-learned potentials capable of tracking oxidation states in materials, enabling accurate modelling of redox processes in Li-ion cathodes and other technologically relevant
Automated workflows predict electronic correlations for high-throughput materials design with HPC-enabled DFPT calculations. Application sectors: Energy storage, Materials design, Computational chemistry. Keywords: DFT+U+V, Hubbard parameters,
A novel machine learning approach reduces DFT computation time while accurately predicting electron densities for molecular and periodic systems. Applications: Materials discovery, Computational chemistry, Energy
Researchers from MaX advanced the predictive design of tantalum-based components, significantly enhancing the ability to model and understand how tantalum behaves under extreme conditions such
MaX researchers performed microsecond, first-principles simulations of glass-forming toluene by integrating Quantum ESPRESSO with machine-learned interatomic potentials. The researchers demonstrated that the temperature-dependent increase in
MaX is on a mission to harness HPC and AI to accelerate the design and discovery of next-generation materials for energy, health, and quantum technologies.
A new HPC-driven method for accurate estimation of transport coefficients in complex materials. Understanding how heat, charge, and mass move through materials is essential for
Researchers from MaX deliver a unified, high-accuracy method to compute transport coefficients from molecular dynamics, reducing simulation cost while improving statistical reliability across liquids and
Researchers from MaX optimized Quantum ESPRESSO to run many k-point calculations in parallel on GPUs, significantly speeding up electronic structure and phonon computations for metals