New updates have been reported about SandboxAQ (SANDB)
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SandboxAQ has introduced AQCat25-EV2, a groundbreaking quantitative AI model designed to transform the landscape of catalyst discovery across various industries. This new model, trained on the extensive AQCat25 dataset, marks a significant advancement in computational catalyst research, enabling the rapid and accurate identification of catalysts for sectors such as energy, chemical production, and automotive. By incorporating the quantum effect of spin polarization, AQCat25-EV2 expands its applicability to all industrially relevant elements, offering a comprehensive approach to catalyst discovery.
The AQCat25-EV2 model predicts energetics with an accuracy nearing that of physics-based quantum-mechanical methods, while operating up to 20,000 times faster. This capability allows for large-scale virtual screening, overcoming a major bottleneck in materials innovation. Developed using over 500,000 GPU-hours on NVIDIA’s DGX Cloud, the model is set to de-risk R&D processes across the materials science spectrum. According to Aayush Singh, head of Catalytic Sciences at SandboxAQ, industries facing critical challenges in catalysis, such as CO2 reduction and plastic recycling, stand to benefit significantly. The launch of AQCat25-EV2, available on Hugging Face, underscores SandboxAQ’s commitment to leveraging AI and quantum techniques to drive innovation and sustainability in industrial processes.