At a glance
Experimental validation: Using high-throughput screening with MatterSim-v1, we previously identified tetragonal tantalum phosphorus (TaP) as a potential high-performance thermal conductor. Now we have experimentally synthesized it and measured its thermal conductivity (152 W/m/K) to be close to the thermal conductivity of silicon.
Faster simulation: We have accelerated MatterSim-v1 model inference by 3-5x and integrated it with the LAMMPS software package, enabling large-scale simulations across multiple GPUs.
New model release: We are introducing MatterSim-MT, a multi-task foundation model for in silico materials characterization that enables the simulation of complex, multi-property phenomena beyond what potential energy surfaces alone can capture.
Materials design underpins a wide range of technological advances, from nanoelectronics to semiconductor design and energy storage. Yet development cycles for novel materials remain slow and costly. Universal machine learning interatomic potentials aim to accelerate the materials design process by providing accurate stability and property predictions for a wide range of materials. These models are orders of magnitude faster than traditional first-principles simulations, turning previously impractical problems into routine computations that can be completed in a few hours. Since we launched our MatterSim-v1 model, it has gained popularity in the materials science community for its ability to accurately simulate materials under realistic conditions, including finite temperature and pressure.
