Data-driven development of cycle prediction models for lithium metal batteries using multi modal mining

Recent advances in data-driven research have shown great potential in understanding the intricate relationships between materials and their performances. Herein, we introduce a novel multi modal data-driven approach employing an Automatic Battery data Collector (ABC) that integrates a large language model (LLM) with an automatic graph mining tool, Material Graph Digitizer (MatGD). This platform enables state-of-the-art accurate extraction of battery material data and cyclability performance metrics from diverse textual and graphical data sources. From the database derived through the ABC platform, we developed machine learning models that can accurately predict the capacity and stability of lithium metal batteries, which is the first-ever model developed to achieve such predictions. Our models were also experimentally validated, confirming practical applicability and reliability of our data-driven approach.

Inverse Design of Metal-Organic Frameworks Using Quantum Natural Language Processing

In this study, we explore the potential of using quantum natural language processing (QNLP) to inverse design metal-organic frameworks (MOFs) with targeted properties. Specifically, by analyzing 150 hypothetical MOF structures consisting of 10 metal nodes and 15 organic ligands, we categorize these structures into four distinct classes for pore volume and $H_{2}$ uptake values. We then compare various QNLP models (i.e. the bag-of-words, DisCoCat (Distributional Compositional Categorical), and sequence-based models) to identify the most effective approach to process the MOF dataset. Using a classical simulator provided by the IBM Qiskit, the bag-of-words model is identified to be the optimum model, achieving validation accuracies of 85.7% and 86.7% for binary classification tasks on pore volume and $H_{2}$ uptake, respectively. Further, we developed multi-class classification models tailored to the probabilistic nature of quantum circuits, with average test accuracies of 88.4% and 80.7% across different classes for pore volume and $H_{2}$ uptake datasets. Finally, the performance of generating MOF with target properties showed accuracies of 93.5% for pore volume and 89% for $H_{2}$ uptake, respectively. Although our investigation covers only a fraction of the vast MOF search space, it marks a promising first step towards using quantum computing for materials design, offering a new perspective through which to explore the complex landscape of MOFs.

ChatMOF: An Autonomous AI System for Predicting and Generating Metal-Organic Frameworks

ChatMOF is an autonomous Artificial Intelligence (AI) system that is built to predict and generate of metal-organic frameworks (MOFs). By leveraging a large-scale language model (gpt-3.5-turbo), ChatMOF extracts key details from textual inputs and delivers appropriate responses, thus eliminating the necessity for rigid structured queries. The system is comprised of three core components (i.e. an agent, a toolkit, and an evaluator) and it forms a robust pipeline that manages a variety of tasks, including data retrieval, property prediction, and structure generation. The study further explores the merits and constraints of using large language models (LLMs) AI system in material sciences using and showcases its transformative potential for future advancements.