DataScribe: An AI-Native, Policy-Aligned Web Platform for Multi-Objective Materials Design and Discovery
🤖 AI Summary
This work addresses the lack of intelligent platforms in traditional materials discovery that integrate data management, learning, and optimization to support multi-objective, multi-fidelity exploration. The authors present an AI-native, cloud-deployed materials discovery platform that unifies heterogeneous experimental and computational data through an ontology-driven knowledge graph. It incorporates FAIR metadata, unit normalization, uncertainty-aware surrogate models, and multi-objective, multi-fidelity Bayesian optimization into a closed-loop “propose–measure–learn” workflow. Innovatively, data governance, optimization engines, and interpretability are embedded as intrinsic components of the intelligence stack rather than post-hoc add-ons. The platform demonstrates reproducible, end-to-end data fusion, real-time optimization, and trade-off analysis across multiple objectives in case studies on electrochemical materials and high-entropy alloys, offering a general-purpose infrastructure for self-driving laboratories.
Application Category
📝 Abstract
The acceleration of materials discovery requires digital platforms that go beyond data repositories to embed learning, optimization, and decision-making directly into research workflows. We introduce DataScribe, an AI-native, cloud-based materials discovery platform that unifies heterogeneous experimental and computational data through ontology-backed ingestion and machine-actionable knowledge graphs. The platform integrates FAIR-compliant metadata capture, schema and unit harmonization, uncertainty-aware surrogate modeling, and native multi-objective multi-fidelity Bayesian optimization, enabling closed-loop propose-measure-learn workflows across experimental and computational pipelines. DataScribe functions as an application-layer intelligence stack, coupling data governance, optimization, and explainability rather than treating them as downstream add-ons. We validate the platform through case studies in electrochemical materials and high-entropy alloys, demonstrating end-to-end data fusion, real-time optimization, and reproducible exploration of multi-objective trade spaces. By embedding optimization engines, machine learning, and unified access to public and private scientific data directly within the data infrastructure, and by supporting open, free use for academic and non-profit researchers, DataScribe functions as a general-purpose application-layer backbone for laboratories of any scale, including self-driving laboratories and geographically distributed materials acceleration platforms, with built-in support for performance, sustainability, and supply-chain-aware objectives.
Problem
Research questions and friction points this paper is trying to address.
materials discovery
multi-objective optimization
AI-native platform
data integration
closed-loop workflows
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI-native platform
multi-objective Bayesian optimization
knowledge graph
FAIR data
closed-loop materials discovery
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