Machine learning is transforming many scientific fields, including computational materials science. For about two decades, scientists have been using it to make accurate yet inexpensive calculations ...

Electro- and photocatalytic materials are central to enabling sustainable energy conversion processes such as water splitting, CO2 reduction, oxygen ...

(a) A feasible route for developing large materials models capable of describing the structure-property relationship of materials. The universal materials model of DeepH accepts an arbitrary material ...

You’ll tackle projects in computational materials design (from high-throughput modeling and phase-diagram simulations to training machine-learning models on experimental signals such as UV–Vis/IR) ...

Faculty conducting research in these areas focus on synthesis of novel materials, the use of novel materials for separations and coatings, computational modeling, and simulation of chemical and ...

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...

A revolution is underway in the nuclear energy sector. For the first time in decades, the field is brimming with urgency, ambition, and capital. Buoyed by growing energy demands, geopolitical ...

Key TakeawaysThe Materials Project is the most-cited resource for materials data and analysis tools in materials science.The ...

Researchers from China University of Petroleum (East China), in collaboration with international partners, have reported a ...

Shape memory alloys are exotic materials that can be deformed at room temperature and return to their "remembered," ...