Deep Learning Discovers Millions Of New Materials (Google)


A technical paper titled “Scaling deep learning for materials discovery” was published by researchers at Google DeepMind and Google Research. Abstract: "Novel functional materials enable fundamental breakthroughs across technological applications from clean energy to information processing. From microchips to batteries and photovoltaics, discovery of inorganic crystals has been bottleneck... » read more

Predicting Defect Properties In Semiconductors With Graph Neural Networks


A technical paper titled “Accelerating Defect Predictions in Semiconductors Using Graph Neural Networks” was published by researchers at Purdue University, Indian Institute of Technology (IIT) Madras, GE Research, and National Institute of Standards and Technology (NIST). Abstract: "Here, we develop a framework for the prediction and screening of native defects and functional impurities i... » read more

RL-Guided Detailed Routing Framework for Advanced Custom Circuits


A technical paper titled "Reinforcement Learning Guided Detailed Routing for Custom Circuits" was published by researchers at UT Austin, Princeton University, and NVIDIA. "This paper presents a novel detailed routing framework for custom circuits that leverages deep reinforcement learning to optimize routing patterns while considering custom routing constraints and industrial design rules. C... » read more

Deep Learning Applications For Material Sciences: Methods, Recent Developments


New technical paper titled "Recent advances and applications of deep learning methods in materials science" from researchers at NIST, UCSD, Lawrence Berkeley National Laboratory, Carnegie Mellon University, Northwestern University, and Columbia University. Abstract "Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning... » read more

There’s More To Machine Learning Than CNNs


Neural networks – and convolutional neural networks (CNNs) in particular – have received an abundance of attention over the last few years, but they're not the only useful machine-learning structures. There are numerous other ways for machines to learn how to solve problems, and there is room for alternative machine-learning structures. “Neural networks can do all this really comple... » read more