21 citations · 46 across the 7 of their papers we have counts for
5 papers · 1 filter
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…
Development of structural descriptors to predict dissolution rate of volcanic glasses: molecular dynamic simulations
Kai Gong, Elsa Olivetti
Establishing the composition-structure-property relationships for amorphous materials is critical for many important natural and engineering processes, including the dissolution of…
Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks
Edward Kim, Zach Jensen, Alexander van Grootel +8
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, experime…