{"author":{"name":"Thomas Marwitz","slug":"thomas-marwitz","article_count":1,"latest_published_at":"2026-04-18T19:13:08.216+00:00","profile_url":"https://platform.waiboom.ai/authors/thomas-marwitz","api_url":"https://platform.waiboom.ai/api/authors/thomas-marwitz"},"articles":[{"slug":"predicting-new-research-directions-in-materials-science-using-large-language-mod","title":"LLMs predict emerging materials science research directions","url":"https://platform.waiboom.ai/article/2026/04/18/predicting-new-research-directions-in-materials-science-using-large-language-mod","content_type":"research_summary","summary":"Researchers led by Thomas Marwitz have demonstrated a method to predict emerging research directions in materials science by combining large language models with concept graphs built from scientific abstracts. The team trained a machine learning model on historical data to identify novel topic combinations that could inspire new research directions. The approach enables materials science experts to discover non-obvious research suggestions by analyzing semantic relationships in the literature. This work shows practical application of LLMs beyond text generation, using them to structure domain knowledge and forecast scientific trends.","published_at":"2026-04-18T19:13:08.216+00:00","updated_at":"2026-04-22T00:59:04.768177+00:00","source":{"url":"https://www.nature.com/articles/s42256-026-01206-y","name":"Nature Machine Intelligence"},"featured_image":{"url":"https://pixelplex.io/wp-content/uploads/2024/01/llm-applications-meta.jpg","alt":null},"categories":[{"name":"Research","slug":"research"},{"name":"Generative AI","slug":"generative-ai"}]}]}