# SciQu: Accelerating Materials Properties Prediction with Automated Literature Mining for Self-Driving Laboratories

arXiv: https://arxiv.org/abs/2407.08270 · HTML: https://mlsanity.com/paper/2407.08270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models](https://mlsanity.com/paper/2402.12289/) (2024)
- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agents](https://mlsanity.com/paper/2402.05746/) (2024)
- [Automated Evaluation of Large Vision-Language Models on Self-driving Corner Cases](https://mlsanity.com/paper/2404.10595/) (2024)
- [LitLLM: A Toolkit for Scientific Literature Review](https://mlsanity.com/paper/2402.01788/) (2024)
- [Embodied Understanding of Driving Scenarios](https://mlsanity.com/paper/2403.04593/) (2024)
- [LLM-Assist: Enhancing Closed-Loop Planning with Language-Based Reasoning](https://mlsanity.com/paper/2401.00125/) (2023)
- [LimSim++: A Closed-Loop Platform for Deploying Multimodal LLMs in Autonomous Driving](https://mlsanity.com/paper/2402.01246/) (2024)
- [Reality Bites: Assessing the Realism of Driving Scenarios with Large Language Models](https://mlsanity.com/paper/2403.09906/) (2024)
- [Instruct Large Language Models to Drive like Humans](https://mlsanity.com/paper/2406.07296/) (2024)
- [AlloyBERT: Alloy Property Prediction with Large Language Models](https://mlsanity.com/paper/2403.19783/) (2024)

## Abstract (arXiv)

Assessing different material properties to predict specific attributes, such as band gap, resistivity, young modulus, work function, and refractive index, is a fundamental requirement for materials science-based applications. However, the process is time-consuming and often requires extensive literature reviews and numerous experiments. Our study addresses these challenges by leveraging machine learning to analyze material properties with greater precision and efficiency. By automating the data extraction process and using the extracted information to train machine learning models, our developed model, SciQu, optimizes material properties. As a proof of concept, we predicted the refractive index of materials using data extracted from numerous research articles with SciQu, considering input descriptors such as space group, volume, and bandgap with Root Mean Square Error (RMSE) 0.068 and R2 0.94. Thus, SciQu not only predicts the properties of materials but also plays a key role in self-driving laboratories by optimizing the synthesis parameters to achieve precise shape, size, and phase of the materials subjected to the input parameters.