May 9, 2024, 4:42 a.m. | Michael Vitz, Hamed Mohammadbagherpoor, Samarth Sandeep, Andrew Vlasic, Richard Padbury, Anh Pham

cs.LG updates on

arXiv:2405.05205v1 Announce Type: cross
Abstract: To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learning (ML) algorithms, which span the ML discipline, have demonstrated ability to predict various properties of materials with the level of accuracy similar to explicit calculation of quantum mechanical theories, but with significantly reduced run time and computational resources. Within ML, graph neural networks have emerged as an important algorithm within the …

abstract accuracy algorithms arxiv cond-mat.mtrl-sci cs.lg data design extract graph graph neural network hybrid information machine machine learning materials materials science network neural network prediction process property quant-ph quantum science type

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