SES, a leader in next-generation battery technology, faced a monumental challenge: identifying optimal molecular structures from a virtually infinite dataset—novemdecillion (10⁶⁰) small molecules—to enhance battery efficiency and performance. To tackle this, SES partnered with MAGIC to leverage AI-driven computational strategies, accelerating the discovery of breakthrough materials for energy storage.
Developing high-performance batteries requires precise molecular-level insights into material properties, stability, and conductivity. The vast Molecular Universe presented an overwhelming computational challenge, making traditional research methods inefficient and costly.
MAGIC deployed its computational hypergrid and AI-powered molecular analysis to optimize SES’s search for next-generation battery materials. By enhancing computational efficiency and automating complex molecular simulations, MAGIC enabled SES to rapidly identify promising compounds for improved energy storage solutions.
MAGIC optimized computational efficiency for Alphafold and small molecule research, reducing costs and accelerating discoveries in molecular biology and drug development.
MAGIC enhanced Intel CPU performance for AI and molecular research, optimizing computations like protein folding and molecular dynamics, reducing costs, and improving accessibility to high-performance computing.
MAGIC enhanced Intel CPU performance for AI and molecular research, optimizing computations like protein folding and molecular dynamics, reducing costs, and improving accessibility to high-performance computing.
MAGIC enhanced Intel CPU performance for AI and molecular research, optimizing computations like protein folding and molecular dynamics, reducing costs, and improving accessibility to high-performance computing.
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