Advancing AI for materials with MatterSim: experimental synthesis, faster simulation, and multi-task models

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. Today, we have several exciting MatterSim updates to share. These include experimental validation of MatterSim predictions for thermal conductors, performance improvements for faster simulation, and the introduction of a new multi-task foundation model for materials characterization. Experimental validation Materials with high thermal conductivity play a critical role in heat management, preventing overheating and improving energy efficiency. For example, established high thermal conductors like diamond, copper and silicon are widely used across a broad range of cooling applications. Designing next-generation thermal conductors may enable advances in computing, power electronics, and aerospace technologies. However, doing so requires accurate predictions of thermal conductivity values for candidate materials. In solids, heat is carried in two main ways: by vibrating atoms (phonons) and by moving electrons. The phonon contribution can be estimated using machine-learning interatomic potentials to enable screening of thousands of candidates, narrowing the search space to the most promising materials before expensive experimental validation. “MatterSim has generated by far the largest database of computational thermal conductivities. This opens the door to exploring a far broader materials space than before […].” – Prof. Bing Lv, University of Texas Dallas In collaboration with the University of Texas Dallas (UT Dallas), University of Illinois Urbana-Champaign, and University of California Davis (UC Davis), we have used MatterSim-v1 to screen over 240,000 candidate materials for high thermal conductors. As shown in Fig. 1 (left), MatterSim’s predictions have good agreement with first-principles simulations. Prof. Davide Donadio from UC Davis: “I was amazed by how the MatterSim model combined accuracy and computational efficiency to predict such a sensitive property as thermal conductivity. That was the key that unlocked screening at this scale, hundreds of thousands of crystals, that would have been completely out of reach with conventional methods.”…