Papers for

software developers for ai research tools

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Machine learning interatomic potential models organized in knowledge graph

Automatically Building and Updating a Knowledge Graph of MLIP Models

Abstract: Complementing the many efforts in providing semantic representations of concepts, notions, and entities in materials science, we report and illustrate a process by which we can automatically build a knowledge graph of the fast evolving field of machine learning applied to the prediction of material properties, focusing on MLIP (Machine Learning Interatomic Potential). This LLM-based process relies on multiple steps, from information extraction in documents and articles to a validation loop using SHACL constraints to detect and correct errors. It is carried out on a model-by-model basis, focusing on the consistency of representation, therefore enabling an iterative construction where the addition of new models is facilitated. We illustrate the process by showing a few interesting aspects that can be queried from a knowledge graph built from the models listed in the Matbench Discovery leaderboard.

Wed 7 OctArtificial Intelligence

The gist

Materials scientists use machine learning models to predict how materials behave, but keeping track of all these models is hard because the field changes quickly. The authors created a computer process that automatically reads texts about these models and builds a structured network, called a knowledge graph, linking information about each model. Their method checks for mistakes and keeps the information consistent, making it easy to add new models over time. They demonstrated this by organizing models from a popular materials prediction benchmark.

Open → 2610.09644v1