Microsoft has released two advanced machine learning models, MatterSimV1-1M and MatterSimV1-5M, for materials science, available on GitHub. These models revolutionize material property prediction by combining deep graph neural networks with uncertainty-aware sampling, achieving near-first-principles accuracy for thermodynamic and mechanical properties across broad temperature and pressure ranges. MatterSimV1-1M is optimized for general-purpose simulations, while MatterSimV1-5M excels in high-precision tasks, offering a mean absolute error as low as 36 meV/atom. With applications in material design, phase stability, mechanical properties, and molecular dynamics, the models deliver significant improvements in accuracy and efficiency, outperforming traditional force fields and first-principles methods. Researchers can fine-tune the models with minimal data, making them versatile tools for accelerating discoveries in materials science.
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Dr. Gabriele Mogni
Technical Consultant and EU Representative
Virtual Lab Inc., the parent company of the Materials Square platform
Website: Home | Virtual Lab Inc.
Email: gabriele@simulation.re.kr
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