Machine learning techniques offer tremendous potential for optimizing resource allocation in solving real-world problems. However, the emergence of multi-fidelity data introduces new challenges. This paper offers an overview of the definition, applications, data preprocessing methodologies, and learning approaches associated with multi-fidelity data. To validate the algorithms, we examine three widely-used learning methods relevant to multi-fidelity data through the design of multi-fidelity datasets that encompass various types of noise. As we expected, employing multi-fidelity data learning methods yields better results compared to solely using high-fidelity data learning methods. Additionally, considering the inherent various types of noise within datasets, the comprehensive correction strategy proves to be the most effective. Moreover, multi-fidelity learning methods facilitate effective decision-making processes by enabling the combination of datasets from various sources. They extract knowledge from lower fidelity data, improving model accuracy compared to models solely relying on high-fidelity data.

The technology of military equipment entity extraction, a crucial component in constructing military knowledge bases, holds significant research value and theoretical importance for guiding the development and improvement of equipment support forces. In the military domain, equipment entities exhibit a phenomenon of nesting, where one entity is contained within another, and abbreviations or codes are frequently used to represent these entities. To address this complexity, this paper proposes a method named CoTNER for extracting entities. Initially, a large-scale language model is used to perform data augmentation with chain-of-thought on the original dataset, providing additional semantic and contextual information. Subsequently, the augmented dataset is fine-tuned on a small-scale language model to adapt it to the task of military equipment entity extraction and to enhance its ability to learn complex rules specific to the domain of military equipment. Additionally, a high-quality data filtering strategy based on instruction-following difficulty scoring is proposed to address the catastrophic forgetting issue that may occur during the fine-tuning of large language models. The experimental results demonstrate that the proposed military equipment entity extraction method outperforms mainstream traditional deep learning methods, validating the effectiveness of CoTNER.

CH_n is the precursor unit for graphene synthesis. We have theoretically predicated a series of CH_n structures with n = 1, 2, 4, 6, 8, 10, and 12 at elevated pressures (ambient pressure, 50, 100, 200, 300, 350, and 400 GPa) using evolutionary algorithms. The predicted CH and CH₂ structures are graphane-type and polyethylene over the whole considered pressure range, respectively. The molecular crystalline methane is predicted for the stoichiometry of CH₄. The combination of methane and H₂ for CH₆, CH₈, CH₁₀, and CH₁₂ up to 300 GPa are obtained. At 400 GPa, the mixture of polymer and H₂ for CH₆, CH₁₀, and CH₁₂ comes into play. From the computed enthalpy, higher pressure and more hydrogen concentration contributed to the decomposition (to carbon and H₂) of CH_n systems. The total density of states for these CH_n structures show that only the CH₁₂ phase is metallic above 300 GPa. The rotational properties are traced in H₂ and the CH_n structures. The CH₄ rotation is more sensitive to the pressure. The H₂ units are nearly freely rotational. Other structures of CH_n, including fcc-type and experimentally known structures, are not competitive with the structures predicted by evolutionary algorithms under high pressure region. Our results suggest that the CH_n (n > 4) system is a potential candidate for hydrogen storage where H₂ could be released by controlling the pressure.

The emergence of large language models (LLMs) has spurred numerous applications across various domains, including material design. In this field, an increasing number of generative models focus on directly generating materials with desired properties more accurately, moving away from the traditional approach of enumerating vast numbers of candidates and relying on computationally intensive screening algorithms. However, we assert that without accurate prediction capabilities, effective material design is unattainable, as generating necessary structures becomes futile if their quality cannot be reliably evaluated. Matbench provides an excellent foundation for predictive tasks, yet prior efforts with LLMs have primarily focused on composition-related tasks using models such as GPT or LLaMA. In this study, we revisit BERT, a relatively small LLM with 110 million parameters, which is significantly smaller than GPT or LLaMA models containing billions of parameters. Remarkably, we demonstrate that BERT-base achieves comparable performance to these larger models in material property prediction tasks. Extending beyond composition tasks, we introduce BERT’s application in structure prediction using CIF (Crystallographic Information File) data and natural language descriptions of structures. Our results rival state-of-the-art composition models such as CrabNet and, in several tasks across datasets ranging from a few hundred to over a hundred thousand samples, even surpass traditional structure-based and knowledge-driven models. Additionally, on the latest Matbench test task, Matbench-Discovery, our model outperformed the Voronoi RF model and achieved MAE results comparable to other models that rely solely on energy predictions. Our findings provide a new reference point for future LLM applications in material design, offering valuable insights for leveraging language model in this domain and emphasizing natural language descriptions over conventional model-centric design. We term this application of BERT in material design AlchemBERT, signifying its novel role in bridging natural language and structural representations.

The emergence of large language models (LLMs) has spurred numerous applications across various domains, including material design. In this field, an increasing number of generative models focus on directly generating materials with desired properties more accurately, moving away from the traditional approach of enumerating vast numbers of candidates and relying on computationally intensive screening algorithms. However, we assert that without accurate prediction capabilities, effective material design is unattainable, as generating necessary structures becomes futile if their quality cannot be reliably evaluated by language models. Matbench provides an excellent foundation for predictive tasks, yet prior efforts with LLMs have primarily focused on composition-related tasks using models such as GPT or LLaMA. In this study, we revisit BERT, a relatively small LLM with 110 million parameters, which is significantly smaller than GPT or LLaMA models containing billions of parameters. Remarkably, we demonstrate that BERT-base achieves comparable performance to these larger models in material property prediction tasks. Beyond composition tasks, we introduce BERT’s application in structure prediction using CIF (Crystallographic Information File) data and natural language descriptions of structures, with natural language outperforming CIF by an average of 40.3\% across all tasks. Our results rival state-of-the-art composition models such as CrabNet and, in several tasks across datasets ranging from a few hundred to over a hundred thousand samples, even surpass traditional structure-based and knowledge-driven models. Additionally, on the latest Matbench test task, Matbench-Discovery, our model outperformed the Voronoi-RF model and achieved MAE results comparable to other models that rely solely on energy predictions. Our findings provide a new reference point for future LLM applications in material design, offering valuable insights for leveraging language model in this domain and emphasizing natural language descriptions over conventional model-centric design. We term this application of BERT in material design AlchemBERT, signifying its novel role in bridging natural language and structural representations.