ARIA is a three-tier causal framework that conditions LLM knowledge use on mechanistic completeness for forward prediction and inverse design of 2D materials, producing auditable traces.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
A fine-tuned Mistral LLM answers multiple-choice and short-answer questions on binary and ternary alloy phase diagrams and generates novel diagrams from component inputs alone.
citing papers explorer
-
ARIA: A Causal-Aware Framework for Rescuing LLM Reasoning in Trustworthy Materials Discovery
ARIA is a three-tier causal framework that conditions LLM knowledge use on mechanistic completeness for forward prediction and inverse design of 2D materials, producing auditable traces.
-
aLLoyM: A large language model for alloy phase diagram prediction
A fine-tuned Mistral LLM answers multiple-choice and short-answer questions on binary and ternary alloy phase diagrams and generates novel diagrams from component inputs alone.