Current Opinion in Solid State & Materials Science最新文献

{"title":"A practical guide to machine learning interatomic potentials – Status and future","authors":"Ryan Jacobs ,&nbsp;Dane Morgan ,&nbsp;Siamak Attarian ,&nbsp;Jun Meng ,&nbsp;Chen Shen ,&nbsp;Zhenghao Wu ,&nbsp;Clare Yijia Xie ,&nbsp;Julia H. Yang ,&nbsp;Nongnuch Artrith ,&nbsp;Ben Blaiszik ,&nbsp;Gerbrand Ceder ,&nbsp;Kamal Choudhary ,&nbsp;Gabor Csanyi ,&nbsp;Ekin Dogus Cubuk ,&nbsp;Bowen Deng ,&nbsp;Ralf Drautz ,&nbsp;Xiang Fu ,&nbsp;Jonathan Godwin ,&nbsp;Vasant Honavar ,&nbsp;Olexandr Isayev ,&nbsp;Boris Kozinsky","doi":"10.1016/j.cossms.2025.101214","DOIUrl":"10.1016/j.cossms.2025.101214","url":null,"abstract":"<div><div>The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is to help such researchers by serving as a practical, accessible guide to the state-of-the-art in MLIPs. This review paper covers a broad range of topics related to MLIPs, including (i) central aspects of how and why MLIPs are enablers of many exciting advancements in molecular modeling, (ii) the main underpinnings of different types of MLIPs, including their basic structure and formalism, (iii) the potentially transformative impact of universal MLIPs for both organic and inorganic systems, including an overview of the most recent advances, capabilities, downsides, and potential applications of this nascent class of MLIPs, (iv) a practical guide for estimating and understanding the execution speed of MLIPs, including guidance for users based on hardware availability, type of MLIP used, and prospective simulation size and time, (v) a manual for what MLIP a user should choose for a given application by considering hardware resources, speed requirements, energy and force accuracy requirements, as well as guidance for choosing pre-trained potentials or fitting a new potential from scratch, (vi) discussion around MLIP infrastructure, including sources of training data, pre-trained potentials, and hardware resources for training, (vii) summary of some key limitations of present MLIPs and current approaches to mitigate such limitations, including methods of including long-range interactions, handling magnetic systems, and treatment of excited states, and finally (viii) we finish with some more speculative thoughts on what the future holds for the development and application of MLIPs over the next 3–10+ years.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"35 ","pages":"Article 101214"},"PeriodicalIF":12.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning for inverse design of acoustic and elastic metamaterials","authors":"Krupali Donda ,&nbsp;Pankit Brahmkhatri ,&nbsp;Yifan Zhu ,&nbsp;Bishwajit Dey ,&nbsp;Viacheslav Slesarenko","doi":"10.1016/j.cossms.2025.101218","DOIUrl":"10.1016/j.cossms.2025.101218","url":null,"abstract":"<div><div>Recent rapid developments in machine learning (ML) models have revolutionized the generation of images and texts. Simultaneously, generative models are beginning to permeate other fields, where they are being applied to the effective design of various structures. In the field of metamaterials, in particular, machine learning has enabled the creation of sophisticated architectures with unconventional behavior and unique properties. In this article, we review recent advancements in the ML-driven design of a particular class of artificial materials — phononic metamaterials — that are capable of programming the propagation of acoustic and elastic waves. This review includes an in-depth discussion of the challenges and future prospects, aiming to inspire the phononic community to advance this research field collectively. We hope this article will help readers understand the recent developments in generative design and build a solid foundation for addressing specific research problems that could benefit from the application of machine learning models.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"35 ","pages":"Article 101218"},"PeriodicalIF":12.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring photosensitive nanomaterials and optoelectronic synapses for neuromorphic artificial vision","authors":"Hyun-Haeng Lee ,&nbsp;Jun-Seok Ro ,&nbsp;Kwan-Nyeong Kim ,&nbsp;Hea-Lim Park ,&nbsp;Tae-Woo Lee","doi":"10.1016/j.cossms.2025.101215","DOIUrl":"10.1016/j.cossms.2025.101215","url":null,"abstract":"<div><div>Artificial vision systems will be essential in intelligent machine-vision applications such as autonomous vehicles, bionic eyes, and humanoid robot eyes. However, conventional digital electronics in these systems face limitations in system complexity, processing speed, and energy consumption. These challenges have been addressed by biomimetic approaches utilizing optoelectronic synapses inspired by the biological synapses in the eye. Nanomaterials can confine photogenerated charge carriers within nano-sized regions, and thus offer significant potential for optoelectronic synapses to perform in-sensor image-processing tasks, such as classifying static multicolor images and detecting dynamic object movements. We introduce recent developments in optoelectronic synapses, focusing on use of photosensitive nanomaterials. We also explore applications of these synapses in recognizing static and dynamic optical information. Finally, we suggest future directions for research on optoelectronic synapses to implement neuromorphic artificial vision.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"35 ","pages":"Article 101215"},"PeriodicalIF":12.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143348423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conductive Hydrogels: Bioelectronics and Environmental Applications","authors":"Seyedeh-Arefeh Safavi-Mirmahalleh ,&nbsp;Mohsen Khodadadi Yazdi ,&nbsp;Mohammad Reza Saeb ,&nbsp;Mehdi Salami-Kalajahi","doi":"10.1016/j.cossms.2024.101213","DOIUrl":"10.1016/j.cossms.2024.101213","url":null,"abstract":"<div><div>Flexible multimodal sensors have garnered continued attention due to their tunable structural performance and sensitivity to electric signals, adaptability to various environments, and outstanding mechanical properties. However, the limited self-healing capabilities, degradation, and reversible self-adhesion of sensors made from rubbers, elastomers, and other polymers have hindered their widespread application. Flexible sensors based on hydrogels, which offer exceptional stretchability, flexibility, and biocompatibility, could provide a solution. However, their reliance on external energy sources limits their potential. Thus, efforts have been made to develop conductive hydrogels by incorporating functional groups, additives, or nanofillers into the hydrogel network, which has led to multifunctional wearable sensing capabilities. This review discusses recent advancements in the use of hydrogels in self-powered sensors, including strain/pressure sensors, electronic skin sensors, pressure/strain sensors, temperature monitoring and humidity monitoring applications. Moreover, it focuses on the mechanisms of energy conversion in self-powered sensors. It also provides a concise overview of the various synthesis methods used in developing conductive hydrogels. The current review also outlines the present challenges, besides suggesting potential pathways ahead for future advancement.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"34 ","pages":"Article 101213"},"PeriodicalIF":12.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The path towards plasma facing components: A review of state-of-the-art in W-based refractory high-entropy alloys","authors":"Caleb Hatler ,&nbsp;Ishtiaque Robin ,&nbsp;Hyosim Kim ,&nbsp;Nathan Curtis ,&nbsp;Bochuan Sun ,&nbsp;Eda Aydogan ,&nbsp;Saryu Fensin ,&nbsp;Adrien Couet ,&nbsp;Enrique Martinez ,&nbsp;Dan J. Thoma ,&nbsp;Osman El Atwani","doi":"10.1016/j.cossms.2024.101201","DOIUrl":"10.1016/j.cossms.2024.101201","url":null,"abstract":"<div><div>Developing advanced materials for plasma-facing components (PFCs) in fusion reactors is a crucial aspect for achieving sustained energy production. Tungsten (W) − based refractory high-entropy alloys (RHEAs) have emerged as promising candidates due to their superior radiation tolerance and high-temperature strength. This review paper will focus on recent advancements in W-based RHEA research, with particular emphasis on: predictive modelling with machine learning (ML) to expedite the identification of optimal RHEA compositions; additive manufacturing (AM) techniques, highlighting their advantages for rapid prototyping and high-throughput multi-compositional sample production; mechanical properties relevant to PFC applications, including hardness, high-temperature strength, and ductility; and the radiation tolerance of W-based RHEAs under irradiated conditions. Finally, the key challenges and opportunities for future research, particularly the holistic analysis of candidate compositions as well as the role of radiation activation and oxidation are identified. This review aims to provide a comprehensive overview of W-based RHEAs for fusion applications and their potential to guide the development and validation of advanced refractory high entropy alloys.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"34 ","pages":"Article 101201"},"PeriodicalIF":12.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence and Machine Learning for materials","authors":"Yuebing Zheng","doi":"10.1016/j.cossms.2024.101202","DOIUrl":"10.1016/j.cossms.2024.101202","url":null,"abstract":"","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"34 ","pages":"Article 101202"},"PeriodicalIF":12.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142423126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimisation and material considerations of piezoelectric implants for cardiac applications","authors":"Yuan Wang,&nbsp;Matthew S. Dargusch","doi":"10.1016/j.cossms.2024.101211","DOIUrl":"10.1016/j.cossms.2024.101211","url":null,"abstract":"<div><div>The capacity of piezoelectric materials for mechanoelectrical transduction has led to a variety of piezoelectric cardiac implants that could revolutionise cardiac-related healthcare delivery. To advance their clinical translation, critical factors including energy output, biocompatibility, biodegradability/durability, and flexibility need to be collectively assessed to ensure successful medical implantation. This review aims to systematically discuss these critical factors, providing insights into corresponding progress and covering relevant mechanisms and strategies in a clinical setting. The concept of additive-free output optimisation has been proposed which focuses on enhancing piezoelectric output based on existing material systems so that biosafety risks and the time-consuming examination processes induced by introducing additional components can be minimised. Critical discussions regarding the biocompatibility and biodegradability of piezoelectric implants were subsequently conducted. This involved reviewing the biocompatibility of material systems associated with piezoelectric implants and introducing biodegradability mechanisms and potential manipulation strategies. The flexibility of implants was also discussed in conjunction with fabrication methods. Current novel piezoelectric cardiac treatments were summarised covering <em>in vivo</em> energy harvesting, hemodynamic sensing, and cardiac tissue regeneration and stimulation. Lastly, challenges and future perspectives were proposed to inspire future work focused on the translation of reliable piezoelectric implants for addressing cardiac diseases.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"34 ","pages":"Article 101211"},"PeriodicalIF":12.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-affinity peptide biomaterials","authors":"Myriel Kim,&nbsp;Rebecca Avrutin,&nbsp;Sean Chryz Iranzo,&nbsp;Honggang Cui","doi":"10.1016/j.cossms.2024.101212","DOIUrl":"10.1016/j.cossms.2024.101212","url":null,"abstract":"<div><div>High-affinity binding is a crucial aspect in the design of advanced biomaterials, enabling the creation of materials that can specifically and effectively interact with target objects such as tissues, cells, or biomolecules, mimicking the sophisticated yet well-controlled interactions found in nature. Peptide-based high-affinity biomaterials have emerged as a promising class due to their versatility in chemical design, simplicity in synthesis and formulation, intrinsic ability to mediate biological communication, and key materials features such as tunable biodegradability and modulable biocompatibility. This Opinion article highlights the critical factors to consider in the development of high-affinity peptide materials, including the selection of appropriate peptide ligands, ensuring conformational stability, and optimizing ligand density and conjugation strategies. It also explores how these design considerations have been successfully employed in various applications, including regenerative medicine, drug delivery, and molecular purification.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"34 ","pages":"Article 101212"},"PeriodicalIF":12.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning in materials research: Developments over the last decade and challenges for the future","authors":"Anubhav Jain","doi":"10.1016/j.cossms.2024.101189","DOIUrl":"10.1016/j.cossms.2024.101189","url":null,"abstract":"<div><p>The number of studies that apply machine learning (ML) to materials science has been growing at a rate of approximately 1.67 times per year over the past decade. In this review, I examine this growth in various contexts. First, I present an analysis of the most commonly used tools (software, databases, materials science methods, and ML methods) used within papers that apply ML to materials science. The analysis demonstrates that despite the growth of deep learning techniques, the use of classical machine learning is still dominant as a whole. It also demonstrates how new research can effectively build upon past research, particular in the domain of ML models trained on density functional theory calculation data. Next, I present the progression of best scores as a function of time on the matbench materials science benchmark for formation enthalpy prediction. In particular, a dramatic improvement of 7 times reduction in error is obtained when progressing from feature-based methods that use conventional ML (random forest, support vector regression, <em>etc.</em>) to the use of graph neural network techniques. Finally, I provide views on future challenges and opportunities, focusing on data size and complexity, extrapolation, interpretation, access, and relevance.</p></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"33 ","pages":"Article 101189"},"PeriodicalIF":12.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S135902862400055X/pdfft?md5=daf1f5860dd3d81b7ae5c13746fc62e9&pid=1-s2.0-S135902862400055X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142168392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Grain refinement and morphological control of intermetallic compounds: A comprehensive review","authors":"Amrit Raj Paul ,&nbsp;Jayshri Dumbre ,&nbsp;Dong Qiu ,&nbsp;Mark Easton ,&nbsp;Maciej Mazur ,&nbsp;Manidipto Mukherjee","doi":"10.1016/j.cossms.2024.101200","DOIUrl":"10.1016/j.cossms.2024.101200","url":null,"abstract":"<div><div>Intermetallic compounds (IMCs) are ordered solid-state compounds formed from chemical reactions between two or more metals exhibiting distinctive crystal arrangements and precise stoichiometric ratios, setting them apart from the matrix of the alloys. In general, IMCs are formed in three configurations: In the form of secondary phase precipitates distributed within the matrix phase, in the form of an IMC alloy, and at the bimetallic interfaces of functionally/transitionally graded structures. However, the IMCs as precipitates in the matrix phase, do not possess many challenges and are often desirable to improve the strength by imparting precipitation hardening. But, in the case of IMC alloys and bimetallic structures, the grain size and morphology of IMCs directly influence the integrity and durability of the developed structure. Given the inherent brittleness of most IMCs, the utilisation of IMCs in critical applications is substantially restricted. In response to this long-standing challenge, there has been extensive research into methods for improving the ductility of IMCs. This review emphasises two key methodologies: solidification-based and non-solidification-based approaches, both aiming to enhance IMC’s mechanical properties either by transitioning large to smaller grain microstructure or dendritic to equiaxed morphology. Solidification-based strategies, including heterogeneous nucleation and external-field-induced morphological alteration like the use of ultrasonic vibration, magnetic, and electric fields, are meticulously evaluated, uncovering research gaps. Non-solidification-based methods like severe plastic deformation and mechanical alloying are critically examined on the suitability of modern manufacturing techniques such as additive manufacturing. Among these, ultrasonic vibration emerges as the most promising for IMCs morphological transformation. Although static magnetic and electric fields exhibit potential, further investigation is required. Despite knowledge gaps, these techniques hold the potential to elevate IMC-containing alloy characteristics. Future research, especially for specific IMC groups and emerging manufacturing processes, is encouraged to propel metallurgical grain refinement or morphological transformation. In addition, the current and emerging application of various IMCs are thoroughly discussed to identify the importance of IMCs in various science and engineering domains. This comprehensive review enhances comprehension of IMC-based grain alteration, paving the way to design advanced materials across various applications.</div></div>","PeriodicalId":295,"journal":{"name":"Current Opinion in Solid State & Materials Science","volume":"33 ","pages":"Article 101200"},"PeriodicalIF":12.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142427355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}