Papers with materials science
SEED: Semantic Knowledge Transfer for Language Model Adaptation to Materials Science (2024.emnlp-industry)
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| Challenge: | Existing methods to adapt pre-trained language models to materials science rely on frequency information from limited downstream datasets. |
| Approach: | They propose a vocabulary expansion method to adapt pre-trained language models to materials science by incorporating latent materials knowledge of lightweight embeddings into PLMs. |
| Outcome: | The proposed method mitigates the limitations of existing adaptation methods and can be used in materials science. |
The SOFC-Exp Corpus and Neural Approaches to Information Extraction in the Materials Science Domain (2020.acl-main)
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Annemarie Friedrich, Heike Adel, Federico Tomazic, Johannes Hingerl, Renou Benteau, Anika Marusczyk, Lukas Lange
| Challenge: | Using BERT embeddings leads to large performance gains, but with increasing task complexity, adding a recurrent neural network seems beneficial. |
| Approach: | They propose an annotation scheme for marking information on publications related to solid oxide fuel cells . they propose to use a recurrent neural network to solve a variety of tasks . |
| Outcome: | The proposed scheme is based on a corpus of 45 open-access scholarly articles and a neural network for a variety of tasks. |
MatViX: Multimodal Information Extraction from Visually Rich Articles (2025.naacl-long)
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Ghazal Khalighinejad, Sharon Scott, Ollie Liu, Kelly L. Anderson, Rickard Stureborg, Aman Tyagi, Bhuwan Dhingra
| Challenge: | Existing methods for multimodal information extraction are limited due to the multimodal nature of scientific articles and complex interconnections between data points. |
| Approach: | They propose a benchmark to extract structured information from scientific articles . they use curated JSON files extracted from text, tables, and figures . |
| Outcome: | The proposed benchmark is based on 324 full-length research articles and 1,688 complex structured JSON files curated by experts in polymer nanocomposites and biodegradation. |
HoneyComb: A Flexible LLM-Based Agent System for Materials Science (2024.findings-emnlp)
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| Challenge: | specialized large language models (LLMs) have shown promise in materials science but often struggle with the distinct complexities of materials science tasks. |
| Approach: | They propose a new LLM-based agent system specifically designed for materials science that leverages a reliable materials science knowledge base and a sophisticated tool hub. |
| Outcome: | The proposed system outperforms baseline models across tasks in materials science while ensuring accuracy and relevance. |
MatSci-NLP: Evaluating Scientific Language Models on Materials Science Language Tasks Using Text-to-Schema Modeling (2023.acl-long)
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| Challenge: | Using publicly available materials science text data, we construct a benchmark for evaluating the performance of natural language processing (NLP) models on materials science texts. |
| Approach: | They propose a natural language benchmark for evaluating the performance of natural language processing (NLP) models on materials science text. |
| Outcome: | The proposed model outperforms BERT-based models on scientific text and a model pretrained on materials science journals. |
Can Multimodal LLMs See Materials Clearly? A Multimodal Benchmark on Materials Characterization (2025.findings-emnlp)
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Zhengzhao Lai, Youbin Zheng, Zhenyang Cai, Haonan Lyu, Jingpu Yang, Hong-Qing Liang, Yan Hu, Benyou Wang
| Challenge: | characterization imaging data is fundamental to acquiring materials information. |
| Approach: | a team of researchers develop a benchmark for materials characterization imaging data . the goal is to bridge this gap by addressing 1,500 questions that require expert-level expertise. |
| Outcome: | a new benchmark for materials characterization imaging data is presented . the benchmark reveals that MLLMs perform poorly when addressing higher-level questions . |
HoneyBee: Progressive Instruction Finetuning of Large Language Models for Materials Science (2023.findings-emnlp)
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| Challenge: | LLaMa-based language model for materials science is first of its kind in the world . |
| Approach: | They propose an instruction-based process for trustworthy data curation in materials science (MatSci-Instruct) they then apply this process to finetune a LLaMa-based language model targeted for materials science. |
| Outcome: | The proposed model outperforms existing language models on materials science tasks and improves in successive stages of refinement. |
Incorporating Domain Knowledge into Materials Tokenization (2025.acl-long)
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| Challenge: | Recent advances in language models have expanded their applications in materials science, but they often produce excessive fragmentation and semantic loss. |
| Approach: | They propose a frequency-centric tokenization approach that integrates material knowledge into tokenization. |
| Outcome: | The proposed tokenization approach outperforms existing tokenization methods and achieves an average performance gain of 4% and 2% in the generation and classification tasks. |
WildSci: Advancing Scientific Reasoning from In-the-Wild Literature (2026.findings-acl)
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| Challenge: | Recent advances in large language model reasoning focus on mathematics and coding domains, but scientific reasoning remains limited in other domains due to limited dataset coverage. |
| Approach: | They propose a framework for sustainable scientific reasoning QA generation by synthesizing a new dataset of domain-specific science questions from peer-reviewed literature. |
| Outcome: | The proposed framework and dataset enable scalable and sustainable research in scientific reasoning. |
MELT: Materials-aware Continued Pre-training for Language Model Adaptation to Materials Science (2024.findings-emnlp)
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| Challenge: | Existing methods focused on constructing domain-specific corpus focus on a limited and scarce nature of datasets in materials science poses significant challenges for developing models that generalize well across a broad range of materials entities. |
| Approach: | They propose a method to adapt pre-trained language models for materials science by continuously pre-training them on a materials science corpus. |
| Outcome: | The proposed method is able to adapt pre-trained language models for materials science tasks. |
polyBART: A Chemical Linguist for Polymer Property Prediction and Generative Design (2025.findings-emnlp)
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| Challenge: | a new language model for polymer design is needed to accurately predict polymers' properties. |
| Approach: | They propose a language model-driven polymer discovery capability that enables rapid and accurate exploration of the polymer design space. |
| Outcome: | The proposed polyBART is the first language model capable of bidirectional translation between polymer structures and properties. |
DiSCoMaT: Distantly Supervised Composition Extraction from Tables in Materials Science Articles (2023.acl-long)
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| Challenge: | Advanced knowledge of a science or engineering domain is typically found in domain-specific research papers. |
| Approach: | They propose a task of extracting compositions of materials from tables in materials science papers to facilitate research in this direction. |
| Outcome: | The proposed model outperforms previous table processing architectures by significant margins. |
CrystalICL: Enabling In-Context Learning for Crystal Generation (2025.emnlp-main)
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| Challenge: | Existing methods for crystal generation are limited to zero-shot scenarios and are unable to benefit from few-shot situations. |
| Approach: | They propose a model designed for few-shot crystal generation that exploits in-context learning by capturing structure-property relationships from limited data. |
| Outcome: | The proposed model reduces complexity of modeling crystal symmetry in LLMs and exploits ICL by capturing structure-property relationships from limited data. |
ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) are increasingly used to solve the entity recognition task. |
| Approach: | They propose a framework to select the most informative and representative samples for LLM in-context learning. |
| Outcome: | The proposed framework outperforms baselines on three specialized domain datasets. |
How to Make Large Language Models Generate 100% Valid Molecules? (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can learn to perform a wide range of tasks, but generating valid molecules using representations like SMILES is challenging in few-shot settings. |
| Approach: | They propose a language framework that converts invalid SMILES to SELFIES and LLMs as post-hoc correctors to ensure that the molecules generated by LLM are 100% valid. |
| Outcome: | The proposed model performs worse with SELFIES than with SMILES and improves on other metrics. |