45 citations · 117 across the 12 of their papers we have counts for
12 papers
Accelerating Discovery of Metal-Insulator Transition Compounds Using Physics-Informed Machine Learning
Alexandru B. Georgescu, Peiwen Ren, Harshul Bhatt +4
Metal-insulator transition (MIT) materials are a useful platform for emerging microelectronic, optoelectronic, and neuromorphic devices, but their discovery is hindered by the high…
MatKG: The Largest Knowledge Graph in Materials Science -- Entities, Relations, and Link Prediction through Graph Representation Learning
Vineeth Venugopal, Sumit Pai, Elsa Olivetti
This paper introduces MatKG, a novel graph database of key concepts in material science spanning the traditional material-structure-property-processing paradigm. MatKG is autonomou…
Deep Reinforcement Learning for Inverse Inorganic Materials Design
Elton Pan, Christopher Karpovich, Elsa Olivetti
A major obstacle to the realization of novel inorganic materials with desirable properties is the inability to perform efficient optimization across both materials properties and s…
Data-driven prediction of room temperature density for multicomponent silicate-based glasses
Kai Gong, Elsa Olivetti
Density is one of the most commonly measured or estimated materials properties, especially for glasses and melts that are of significant interest to many fields, including metallur…
Augmenting Scientific Creativity with Retrieval across Knowledge Domains
Hyeonsu B. Kang, Sheshera Mysore, Kevin Huang +5
Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existin…
Inorganic Synthesis Reaction Condition Prediction with Generative Machine Learning
Christopher Karpovich, Zach Jensen, Vineeth Venugopal +1
Data-driven synthesis planning with machine learning is a key step in the design and discovery of novel inorganic compounds with desirable properties. Inorganic materials synthesis…