Moving Beyond Serendipity in Science with Simulations, from Quantum to AI
November 25, 2024
For centuries, serendipity and trial-and-error have driven scientific breakthroughs, but progress has often been slow and incremental. Today, machine learning in science and multiscale simulations are transforming research, accelerating discovery, and reducing reliance on chance. While AI in scientific research can rapidly analyze vast datasets and identify patterns, it often lacks scientific reasoning, producing results without explaining why they occur—underscoring the need for explainable AI (XAI).
Computational simulations bridge this gap, providing deeper insights into reaction mechanisms, material properties, and molecular systems modeling. By integrating AI-driven scientific breakthroughs with simulations, researchers can validate findings, refine materials, and transition from slow, trial-and-error-based workflows to data-driven science.
How will AI and simulations shape the next era of scientific breakthroughs? Explore the possibilities today.
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