A recent study published in Nature demonstrates a more efficient method for using quantum computers to simulate materials.

Many techniques in computational materials science require scientists to identify the right set of parameters that capture the physics of the specific material they are studying. Calculating these parameters ...

CrystaLLM uses GPT to arrange atoms, turning text-based data into numerical tokens

A breakthrough in decoding the growth process of hexagonal boron nitride (hBN), a 2D material, and its nanostructures on metal substrates could pave the way for more efficient electronics, cleaner energy ...

Researchers at Queen Mary University of London have cracked the code behind cordierite's exceptional thermal stability, a quality that makes it indispensable in high-temperature applications. By using molecular dynamics simulations, they have uncovered a…

npj Computational Materials - Systematic softening in universal machine learning interatomic potentials

The Dral’s group is seeking an outstanding postdoc for AI-enhanced atomistic simulations. The position is a unique opportunity to plunge into the frontiers of atomistic research in collaboration wi…

A new computational chemistry approach developed by MIT researchers could facilitate high-throughput molecular screening — task where achieving chemical accuracy is essential for identifying novel molecules and materials with desirable properties.

Skoltech researchers have presented a new simple physical model for predicting the hardness of materials based on information about the shear modulus and equations of the state of crystal structures. ...

Nature Chemistry - The tailoring of reticular materials is key for enhancing the complexity and diversity of their structure and function. Now, a series of isomeric pillar-layered...

Optimizing the performance of sub 10 nm SiO2 nanoparticles (NPs) requires controlling their aggregation, which depends on understanding the underexplored early stages of this process. This study uses reactive molecular dynamics (MD) simulations with the ReaxFF…

Академик Назар Тўраев ҳаётига чизгилар

Nature Communications - Atomic representations are crucial for building reliable and transferable machine learning models. Here, the authors propose transformer-based universal atomic embeddings to...

Nature Computational Science - Autonomous synthesis laboratories promise to streamline the plan–make–measure–analyze iteration loop. Here, we comment on the barriers in the field,...