Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
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DOI: 10.1021/acs.jcim.9b00995
Abstract: Leveraging new data sources is a key step in accelerating the pace of materials design and discovery. To complement the strides in synthesis planning driven by historical, experimental, and computed data, we present an automated,… read more here.
Keywords: synthesis planning; inorganic materials; planning literature; materials synthesis ... See more keywords