4 ms·
From the paper... https://arxiv.org/abs/2401.04070 https://arxiv.org/abs/2401.04070 >Here we demonstrate how this vision became reality by first combining sta
by hnburnsy 3y ago
From the paper...
https://arxiv.org/abs/2401.04070 https://arxiv.org/abs/2401.04070
>Here we demonstrate how this vision became reality by first combining state-of-the-art artificial intelligence (AI) models and traditional physics-based models on cloud high-performance computing (HPC) resources to quickly navigate through more than 32 million candidates and predict around half a million potentially stable materials. By focusing on solid-state electrolytes for battery applications, our discovery pipeline further identified 18 promising candidates with new compositions and rediscovered a decade's worth of collective knowledge in the field as a byproduct. By employing around one thousand virtual machines (VMs) in the cloud, this process took less than 80 hours.
- illiac786 3y ago>rediscovered a decade's worth of collective knowledge Unless one can guarantee this knowledge wasn't in the training data, it's not rediscovered, it's regurgitated. Good example of the bias that exist with AI: when the output/performance can be interpreted in two different manners, I consistently find people unconsciously choosing the option painting the AI in a better light. I don't think AI / deep learning is useless, but I do think it's overrated at the moment.