Quantum Generative Materials LLC (“GenMat” or the “Company”) today announced that it has developed ZENO, a proprietary generative artificial intelligence that simulates known and new materials in an exponentially shorter time than traditional methods allow, including crucial properties such as electrical and thermal conductivity, heat capacity, local and charged density of states, band gap, formation energies, magnetic properties, and more.
GenMat has assembled a world-class interdisciplinary team of material scientists, computational chemists, quantum physicists, and quantum, machine learning and aerospace engineers that develop classical and quantum machine learning models for electronic structure calculations, molecular dynamics simulations, and multi-scale simulations, and then validate those models.
GenMat’s team deployed a state-of-the-art high-performance computing platform with the capacity to conduct physics-based material simulations at workloads comparable to many conventional supercomputers, and then utilized that platform to train, develop and launch GenMat’s proprietary new generative artificial intelligence for new material and mineral discoveries.
“Having faster, cheaper, more accurate multi-scale materials simulations powered by a truly generative artificial intelligence will drastically reduce trial and error costs for product development,” said Deep Prasad, GenMat’s founder and chief executive officer. “ZENO was recently used to successfully simulate critical properties of known catalysts during calibration testing late last year, and it has already begun to simulate new materials in selected applications.”
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