This Collection supports and amplifies research related to SDG 9 - Industry, Innovation & Infrastructure. Discovering new materials with customizable and optimized properties, driven either by ...

(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 ...

In late 2023, a robotic laboratory at Lawrence Berkeley National Laboratory ran nonstop for 17 days without a single human ...

Machine learning (ML) enables the accurate and efficient computation of fundamental electronic properties of binary and ternary oxide surfaces, as shown by scientists from Tokyo Tech. Their ML-based ...

A general-purpose LLM is fine-tuned with inorganic material knowledge datasets and used to predict the synthesizability and precursor compounds of hypothetical inorganic materials. Seoul National ...

Materials informatics applies data-driven strategies to materials R&D. Long before generative AI technology reached peak hype, it had a long history of success in this field. A common approach is to ...

Using the second-nearest neighboring atoms to predict metallic glass stability can help researchers more accurately model the disordered solid with strong, elastic properties, according to a recent ...

Spread the loveThe landscape of scientific inquiry is constantly evolving, and recent advancements in reverse thermal diffusion are reshaping our understanding of material sciences. Researchers have ...

Overview of class Key models of electron and ion vacancy transport in hard and soft, crystalline and non-crystalline materials, including hopping, tunneling, polaronic transport and mixed conduction.