Hasil untuk "Materials Science"

R. W. Cahn

Jonathan Schmidt, Mário R. G. Marques, S. Botti et al.

One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

K. Butler, D. Davies, H. Cartwright et al.

A. Togo, I. Tanaka

Abstract Phonon plays essential roles in dynamical behaviors and thermal properties, which are central topics in fundamental issues of materials science. The importance of first principles phonon calculations cannot be overly emphasized. Phonopy is an open source code for such calculations launched by the present authors, which has been world-widely used. Here we demonstrate phonon properties with fundamental equations and show examples how the phonon calculations are applied in materials science.

Patrick Reiser, Marlen Neubert, Andr'e Eberhard et al.

Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs. Graph neural networks are machine learning models that directly access the structural representation of molecules and materials. This Review discusses state-of-the-art architectures and applications of graph neural networks in materials science and chemistry, indicating a possible road-map for their further development.

Pengcheng Xu, Xiaobo Ji, Minjie Li et al.

This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced. Next, the methods of dealing with small data were introduced, including data extraction from publications, materials database construction, high-throughput computations and experiments from the data source level; modeling algorithms for small data and imbalanced learning from the algorithm level; active learning and transfer learning from the machine learning strategy level. Finally, the future directions for small data machine learning in materials science were proposed.

A. Stukowski

Gary Tom, Stefan P Schmid, Sterling G. Baird et al.

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.

D. Raabe

Production of metals stands for 40% of all industrial greenhouse gas emissions, 10% of the global energy consumption, 3.2 billion tonnes of minerals mined, and several billion tonnes of by-products every year. Therefore, metals must become more sustainable. A circular economy model does not work, because market demand exceeds the available scrap currently by about two-thirds. Even under optimal conditions, at least one-third of the metals will also in the future come from primary production, creating huge emissions. Although the influence of metals on global warming has been discussed with respect to mitigation strategies and socio-economic factors, the fundamental materials science to make the metallurgical sector more sustainable has been less addressed. This may be attributed to the fact that the field of sustainable metals describes a global challenge, but not yet a homogeneous research field. However, the sheer magnitude of this challenge and its huge environmental effects, caused by more than 2 billion tonnes of metals produced every year, make its sustainability an essential research topic not only from a technological point of view but also from a basic materials research perspective. Therefore, this paper aims to identify and discuss the most pressing scientific bottleneck questions and key mechanisms, considering metal synthesis from primary (minerals), secondary (scrap), and tertiary (re-mined) sources as well as the energy-intensive downstream processing. Focus is placed on materials science aspects, particularly on those that help reduce CO2 emissions, and less on process engineering or economy. The paper does not describe the devastating influence of metal-related greenhouse gas emissions on climate, but scientific approaches how to solve this problem, through research that can render metallurgy fossil-free. The content is considering only direct measures to metallurgical sustainability (production) and not indirect measures that materials leverage through their properties (strength, weight, longevity, functionality).

Xiaoting Zhong, Brian Gallagher, Shusen Liu et al.

Machine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.

Matthew K. Horton, Patrick Huck, Ruo Xi Yang et al.

K. Jablonka, Qianxiang Ai, Alexander Al-Feghali et al.

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

Yue Liu, Zhengwei Yang, Xinxin Zou et al.

ABSTRACT Data-driven machine learning (ML) is widely employed in the analysis of materials structure–activity relationships, performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction. However, because of the laborious process of materials data acquisition, ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size (for traditional ML models) or the mismatch between model parameters and sample size (for deep-learning models), usually resulting in terrible performance. Here, we review the efforts for tackling this issue via feature reduction, sample augmentation and specific ML approaches, and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance. Following this, we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge. After summarizing the approaches to incorporating materials domain knowledge into the process of ML, we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications. The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML.

Xiaopeng Bai, Xingcai Zhang

A detailed exploration is provided of how artificial intelligence (AI) and machine learning techniques are applied across various aspects of materials science. Major challenges in AI-driven materials science are evaluated. Novel case studies are incorporated, demonstrating their impact on accelerating material development and discovery. A detailed exploration is provided of how artificial intelligence (AI) and machine learning techniques are applied across various aspects of materials science. Major challenges in AI-driven materials science are evaluated. Novel case studies are incorporated, demonstrating their impact on accelerating material development and discovery. The advancement of materials has played a pivotal role in the advancement of human civilization, and the emergence of artificial intelligence (AI)-empowered materials science heralds a new era with substantial potential to tackle the escalating challenges related to energy, environment, and biomedical concerns in a sustainable manner. The exploration and development of sustainable materials are poised to assume a critical role in attaining technologically advanced solutions that are environmentally friendly, energy-efficient, and conducive to human well-being. This review provides a comprehensive overview of the current scholarly progress in artificial intelligence-powered materials science and its cutting-edge applications. We anticipate that AI technology will be extensively utilized in material research and development, thereby expediting the growth and implementation of novel materials. AI will serve as a catalyst for materials innovation, and in turn, advancements in materials innovation will further enhance the capabilities of AI and AI-powered materials science. Through the synergistic collaboration between AI and materials science, we stand to realize a future propelled by advanced AI-powered materials.

Yuri Alexeev, Maximilian Amsler, Paul G. Baity et al.

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

Huan Zhang, Yu Song, Ziyu Hou et al.

The emergence of specialized large language models (LLMs) has shown promise in addressing complex tasks for materials science. Many LLMs, however, often struggle with distinct complexities of material science tasks, such as materials science computational tasks, and often rely heavily on outdated implicit knowledge, leading to inaccuracies and hallucinations. To address these challenges, we introduce HoneyComb, the first LLM-based agent system specifically designed for materials science. HoneyComb leverages a novel, high-quality materials science knowledge base (MatSciKB) and a sophisticated tool hub (ToolHub) to enhance its reasoning and computational capabilities tailored to materials science. MatSciKB is a curated, structured knowledge collection based on reliable literature, while ToolHub employs an Inductive Tool Construction method to generate, decompose, and refine API tools for materials science. Additionally, HoneyComb leverages a retriever module that adaptively selects the appropriate knowledge source or tools for specific tasks, thereby ensuring accuracy and relevance. Our results demonstrate that HoneyComb significantly outperforms baseline models across various tasks in materials science, effectively bridging the gap between current LLM capabilities and the specialized needs of this domain. Furthermore, our adaptable framework can be easily extended to other scientific domains, highlighting its potential for broad applicability in advancing scientific research and applications.

T. Gallmeyer, S. Moorthy, B. Kappes et al.

Dislocation structures, chemical segregation, {\gamma ^{\prime}, {\gamma ^{\prime \prime}}, {\delta} precipitates and Laves phase were quantified within the microstructures of Inconel 718 (IN718) produced by laser powder bed fusion additive manufacturing (AM) and subjected to standard, direct aging, and modified multi-step heat treatments. Additionally, heat-treated samples still attached to the build plates vs. those removed were also documented for a standard heat treatment. The effects of the different resulting microstructures on room temperature strengths and elongations to failure is revealed. Knowledge derived from these process structure property relationships was used to engineer a super solvus solution anneal at 1020 degC for 15 minutes, followed by aging at 720 degC for 24 hours heat treatment for AM-IN718 that eliminates Laves and {\delta} phases, preserves AM specific dislocation cells that are shown to be stabilized by MC carbide particles, and precipitates dense {\gamma ^{\prime} and {\gamma ^{\prime \prime}} nanoparticle populations. This 'optimized for AM-IN718 heat treatment' results in superior properties relative to wrought/additively manufactured, then industry standard heat treated IN718: relative increases of 7/10 percent in yield strength, 2/7 percent in ultimate strength, and 23/57 percent in elongation to failure are realized, respectively, regardless of as-built vs. machined surface finishes.

Junchen Li, Qiushuang Wang, Swee Leong Sing et al.

Conventional manufacturing of 430 ferritic stainless steel faces significant challenges, including severe work hardening, rapid tool wear, and limited strengthening via heat treatment. To overcome these limitations, this study employs laser powder bed fusion (LPBF) technology to fabricate high-density (99.93%) 430 stainless steel samples through systematic process parameter optimisation. The as-built samples exhibit a unique microstructure characterised by columnar ferrite grains with a <100 >  fibre texture along the building direction and a few oxides enriched in aluminium, oxygen, and nitrogen. In terms of mechanical properties, the as-built sample exhibits high yield strength of approximately 747.5 MPa, nearly double that of the conventionally hot rolled counterpart, while maintaining considerable ductility (∼29.2%). This significant strengthening is primarily attributed to the high-density dislocations generated during the LPBF forming process. This work demonstrates the potential of LPBF for producing high-performance ferritic stainless steels with enhanced mechanical properties.

S. Chong, Yi Sheng Ng, Hui‐Qiong Wang et al.

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties; ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software; The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.

B.V. Nikolaev, N.A. Pavlova

Background. Domestic diplomacy and international legal science played a leading role in the formation of the institution of peaceful resolution of international disputes. It was Russia that initiated two Hague Peace Conferences in 1899 and 1907. However, this issue has not received sufficient attention from domestic and foreign researchers, the latter, however, actively studied the role of the United States, Great Britain, France and other countries in terms of the development of the institution of peaceful resolution of international disputes. In this regard, the study of the content and results of the Second Peace Conference and its historical significance seems relevant and scientifically significant. The purpose of the work is to identify the main directions and achievements of the Hague Peace Conference of 1907 in the context of the development of the institution of peaceful resolution of international disputes. Materials and methods. These objectives are achieved by analyzing the official materials of the 1907 Hague Peace Conference, official acts of the Russian Ministry of Foreign Affairs, assessments of the conference's achievements given by its participants themselves, as well as international treaties and scientific literature. Results. The work analyzes the work and results of the 1907 forum from the point of view of the development of the institution of peaceful resolution of international disputes. Conclu-sions. The study allows us to draw a conclusion about the special role of Russian diplomacy and international legal science in the codification of the institution of peaceful resolution of international disputes and the progressive development of international law in general.