{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/inorganic-materials-synthesis-planning-with","title":"Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks","arxiv_id":"1901.00032","date":"2018-12-31","proceeding":null,"authors":["Edward Kim","Zach Jensen","Alexander van Grootel","Kevin Huang","Matthew Staib","Sheshera Mysore","Haw-Shiuan Chang","Emma Strubell","Andrew McCallum","Stefanie Jegelka","Elsa Olivetti"],"abstract":"Leveraging new data sources is a key step in accelerating the pace of\nmaterials design and discovery. To complement the strides in synthesis planning\ndriven by historical, experimental, and computed data, we present an automated\nmethod for connecting scientific literature to synthesis insights. Starting\nfrom natural language text, we apply word embeddings from language models,\nwhich are fed into a named entity recognition model, upon which a conditional\nvariational autoencoder is trained to generate syntheses for arbitrary\nmaterials. We show the potential of this technique by predicting precursors for\ntwo perovskite materials, using only training data published over a decade\nprior to their first reported syntheses. We demonstrate that the model learns\nrepresentations of materials corresponding to synthesis-related properties, and\nthat the model's behavior complements existing thermodynamic knowledge.\nFinally, we apply the model to perform synthesizability screening for proposed\nnovel perovskite compounds.","url_abs":"http://arxiv.org/abs/1901.00032v2","url_pdf":"http://arxiv.org/pdf/1901.00032v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"inorganic-materials-synthesis-planning-with","repo_url":"https://github.com/olivettigroup/materials-synthesis-generative-models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.00032","atlas_url":"https://app.syntology.ai/?focus=1901.00032","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}