Quantum electronic structure calculations are among the most computationally demanding applications and comprise a significant fraction of compute cycles at supercomputing facilities around the world. The resources required for these calculations are matched by the promise of unlocking key insights across a wide variety of scientific and engineering domains spanning materials science, drug discovery, soft matter physics, manufacturing, and many others.
Today, scientists are making important strides toward approximating the most computationally challenging pieces of these calculations by employing machine learning, opening the door to potential speed-ups while maintaining the same level of accuracy. Several recent examples will be highlighted from both molecular dynamics and quantum electronic structure.
By attending this webinar, you’ll learn: