Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials

• URL: http://arxiv.org/abs/2411.14034v1
• Date: Thu, 21 Nov 2024 11:37:05 GMT
• Title: Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
• Authors: Federico Ottomano, John Y. Goulermas, Vladimir Gusev, Rahul Savani, Michael W. Gaultois, Troy D. Manning, Hai Lin, Teresa P. Manzanera, Emmeline G. Poole, Matthew S. Dyer, John B. Claridge, Jon Alaria, Luke M. Daniels, Su Varma, David Rimmer, Kevin Sanderson, Matthew J. Rosseinsky,
• Abstract summary: We propose a data-driven framework aimed at accelerating the discovery of new transparent conducting materials. To mitigate the shortage of available data, we create and validate unique experimental databases. We test our approach on a list of 55 compositions containing typical elements of known TCMs.
• Score: 10.3054383984768
• License:
• Abstract: Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face constraints imposed by the quantity and quality of available data. Moreover, ML is often employed in tandem with simulated datasets originating from density functional theory (DFT), and assessed through in-sample evaluation schemes. This scenario raises questions about the practical utility of ML in uncovering new and significant material classes for industrial applications. Here, we propose a data-driven framework aimed at accelerating the discovery of new transparent conducting materials (TCMs), an important category of semiconductors with a wide range of applications. To mitigate the shortage of available data, we create and validate unique experimental databases, comprising several examples of existing TCMs. We assess state-of-the-art (SOTA) ML models for property prediction from the stoichiometry alone. We propose a bespoke evaluation scheme to provide empirical evidence on the ability of ML to uncover new, previously unseen materials of interest. We test our approach on a list of 55 compositions containing typical elements of known TCMs. Although our study indicates that ML tends to identify new TCMs compositionally similar to those in the training data, we empirically demonstrate that it can highlight material candidates that may have been previously overlooked, offering a systematic approach to identify materials that are likely to display TCMs characteristics.

Related papers

• Star-Agents: Automatic Data Optimization with LLM Agents for Instruction Tuning [71.2981957820888]
  We propose a novel Star-Agents framework, which automates the enhancement of data quality across datasets. The framework initially generates diverse instruction data with multiple LLM agents through a bespoke sampling method. The generated data undergo a rigorous evaluation using a dual-model method that assesses both difficulty and quality.
  arXiv  Detail & Related papers  (2024-11-21T02:30:53Z)
• LLMatDesign: Autonomous Materials Discovery with Large Language Models [5.481299708562135]
  New materials can have significant scientific and technological implications. Recent advances in machine learning have enabled data-driven methods to rapidly screen or generate promising materials. We introduce LLMatDesign, a novel framework for interpretable materials design powered by large language models.
  arXiv  Detail & Related papers  (2024-06-19T02:35:02Z)
• MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization [86.61052121715689]
  MatPlotAgent is a model-agnostic framework designed to automate scientific data visualization tasks. MatPlotBench is a high-quality benchmark consisting of 100 human-verified test cases.
  arXiv  Detail & Related papers  (2024-02-18T04:28:28Z)
• C-ICL: Contrastive In-context Learning for Information Extraction [54.39470114243744]
  c-ICL is a novel few-shot technique that leverages both correct and incorrect sample constructions to create in-context learning demonstrations. Our experiments on various datasets indicate that c-ICL outperforms previous few-shot in-context learning methods.
  arXiv  Detail & Related papers  (2024-02-17T11:28:08Z)
• Mining experimental data from Materials Science literature with Large Language Models: an evaluation study [1.9849264945671101]
  This study is dedicated to assessing the capabilities of large language models (LLMs) in extracting structured information from scientific documents in materials science. We focus on two critical tasks of information extraction: (i) a named entity recognition (NER) of studied materials and physical properties and (ii) a relation extraction (RE) between these entities. The performance of LLMs in executing these tasks is benchmarked against traditional models based on the BERT architecture and rule-based approaches (baseline)
  arXiv  Detail & Related papers  (2024-01-19T23:00:31Z)
• Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes [57.62036621319563]
  We introduce CLLM, which leverages the prior knowledge of Large Language Models (LLMs) for data augmentation in the low-data regime. We demonstrate the superior performance of CLLM in the low-data regime compared to conventional generators.
  arXiv  Detail & Related papers  (2023-12-19T12:34:46Z)
• Multimodal Learning for Materials [7.167520424757711]
  We introduce Multimodal Learning for Materials (MultiMat), which enables self-supervised multi-modality training of foundation models for materials. We demonstrate our framework's potential using data from the Materials Project database on multiple axes.
  arXiv  Detail & Related papers  (2023-11-30T18:35:29Z)
• MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling [7.142619575624596]
  MatSci ML is a benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods. MatSci ML provides a diverse set of materials systems and properties data for model training and evaluation. In the multi-dataset learning setting, MatSci ML enables researchers to combine observations from multiple datasets to perform joint prediction of common properties.
  arXiv  Detail & Related papers  (2023-09-12T03:08:37Z)
• Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport [49.237577649802034]
  We develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances.
  arXiv  Detail & Related papers  (2022-02-13T04:56:18Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)
• Intelligent multiscale simulation based on process-guided composite database [0.0]
  We present an integrated data-driven modeling framework based on process modeling, material homogenization, and machine learning. We are interested in the injection-molded short fiber reinforced composites, which have been identified as key material systems in automotive, aerospace, and electronics industries.
  arXiv  Detail & Related papers  (2020-03-20T20:39:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.