標(biāo)題: Titlebook: Artificial Intelligence for Materials Science; Yuan Cheng,Tian Wang,Gang Zhang Book 2021 The Editor(s) (if applicable) and The Author(s), [打印本頁(yè)] 作者: Mosquito 時(shí)間: 2025-3-21 16:34
書目名稱Artificial Intelligence for Materials Science影響因子(影響力)
書目名稱Artificial Intelligence for Materials Science影響因子(影響力)學(xué)科排名
書目名稱Artificial Intelligence for Materials Science網(wǎng)絡(luò)公開度
書目名稱Artificial Intelligence for Materials Science網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Artificial Intelligence for Materials Science被引頻次
書目名稱Artificial Intelligence for Materials Science被引頻次學(xué)科排名
書目名稱Artificial Intelligence for Materials Science年度引用
書目名稱Artificial Intelligence for Materials Science年度引用學(xué)科排名
書目名稱Artificial Intelligence for Materials Science讀者反饋
書目名稱Artificial Intelligence for Materials Science讀者反饋學(xué)科排名
作者: arcane 時(shí)間: 2025-3-21 21:30 作者: Ardent 時(shí)間: 2025-3-22 01:04
Accelerated Discovery of Thermoelectric Materials Using Machine Learning,s, which has accelerated the discovery of highly efficient thermoelectric materials. Details of commonly used strategies and methods to select a relevant descriptor set for developing the prediction models will be covered. A new approach for selecting descriptors by analyzing the high-throughput pro作者: 善變 時(shí)間: 2025-3-22 06:57 作者: Harridan 時(shí)間: 2025-3-22 12:34 作者: Robust 時(shí)間: 2025-3-22 15:23
Status quo 2015 – Rahmenbedingungenxpensive, highly efficient, and easily transferable, have been employed to accelerate HEA development. This chapter will give an overview of HEAs (fundamentals, preparations, and properties) and introduce recent progress in ML-assisted design of HEAs (microstructure and property predictions).作者: 拋棄的貨物 時(shí)間: 2025-3-22 18:38 作者: Pelvic-Floor 時(shí)間: 2025-3-22 22:04 作者: MIR 時(shí)間: 2025-3-23 02:42
Tote Zonen in den Meeren – der P/N-Kreislauf, this chapter will introduce well-established ML models widely used in perovskite-related studies from both the construction of data and material representation aspects. The approaches of data sets will be discussed including the high-throughput (HT) computations and experimentations. The material 作者: Rankle 時(shí)間: 2025-3-23 09:32 作者: 輕快來(lái)事 時(shí)間: 2025-3-23 13:36 作者: Cirrhosis 時(shí)間: 2025-3-23 17:13
Thermal Nanostructure Design by Materials Informatics,ng from heat conduction through Si/Ge and GaAs/AlAs superlattices, graphene nanoribbons, to thermal emission for radiative cooling, ultranarrow emission, thermophotovoltaic system, and thermal camouflage. The remaining challenges and opportunities in this field are outlined and prospected.作者: animated 時(shí)間: 2025-3-23 18:38 作者: osteocytes 時(shí)間: 2025-3-24 01:58 作者: 整潔漂亮 時(shí)間: 2025-3-24 04:53 作者: 混雜人 時(shí)間: 2025-3-24 10:26
Waldverlust – Abholzung der Regenw?lderony, particle swarm optimization, and differential evolution. The evolution mechanism, current research status, and applications of different genetic algorithm have been investigated in detail for the users to choose the most appropriate strategy.作者: 包裹 時(shí)間: 2025-3-24 14:03
0933-033X computational material science.Features applications of mach.Machine learning methods have lowered the cost of exploring new structures of unknown compounds, and can be used to predict reasonable expectations and subsequently validated by experimental results. As new insights and several elaborative作者: blister 時(shí)間: 2025-3-24 15:18
Drei Ziele der Energiewende – AnalyseGI remains challenging. The machine learning methods, which have been adopted in the MGI, developed with big data and artificial intelligence. This chapter provides a brief overview of the machine learning methods adopted in the materials studies.作者: 含糊其辭 時(shí)間: 2025-3-24 21:53
Brief Introduction of the Machine Learning Method,GI remains challenging. The machine learning methods, which have been adopted in the MGI, developed with big data and artificial intelligence. This chapter provides a brief overview of the machine learning methods adopted in the materials studies.作者: Small-Intestine 時(shí)間: 2025-3-25 00:55
Book 2021nd subsequently validated by experimental results. As new insights and several elaborative tools have been developed for materials science and engineering in recent years, it is an appropriate time to present a book covering recent progress in this field..Searchable and interactive databases can pro作者: 逃避現(xiàn)實(shí) 時(shí)間: 2025-3-25 06:56 作者: 沒有希望 時(shí)間: 2025-3-25 08:09 作者: 音樂學(xué)者 時(shí)間: 2025-3-25 12:22
Status quo 2015 – Rahmenbedingungen04, tremendous progresses and profound developments have been made in both the fundamental investigations and engineering applications. Unlike the conventional metallic alloys that typically only consist of one or two principal elements, HEA is composed of multi-principal elements in equimolar or ne作者: 過多 時(shí)間: 2025-3-25 19:44 作者: 相同 時(shí)間: 2025-3-25 22:39
Drei Ziele der Energiewende – Beschreibungional cost, and transferability. In this mini review, we first summarize the disadvantages of traditional force field and the unique advantages of ML-based force field for molecular dynamics simulations. Then the basic workflow to develop the ML atomic force field is discussed in each step. Furtherm作者: reaching 時(shí)間: 2025-3-26 00:52 作者: nominal 時(shí)間: 2025-3-26 05:02
Tote Zonen in den Meeren – der P/N-Kreislaufomplex interdependence, simultaneous optimization of these properties is a non-trivial and challenging task, especially if one wants to explore the large available search space of materials. With the advent of statistical high-throughput and machine learning based approaches, several of these challe作者: larder 時(shí)間: 2025-3-26 10:23 作者: 秘傳 時(shí)間: 2025-3-26 14:09
Tote Zonen in den Meeren – der P/N-Kreislauf still are obstacles for commercial application. These challenges have motivated significant efforts to search nontoxic and stable alternatives which could achieve comparable high performance with low-cost and facile fabrication methods. With continuing increasing computation powers, first-principle作者: 自作多情 時(shí)間: 2025-3-26 17:09 作者: 是剝皮 時(shí)間: 2025-3-26 21:17 作者: 預(yù)知 時(shí)間: 2025-3-27 02:02 作者: 吹牛需要藝術(shù) 時(shí)間: 2025-3-27 06:15 作者: Pudendal-Nerve 時(shí)間: 2025-3-27 11:54
Brief Introduction of the Machine Learning Method,operties, which are critical for developing advanced materials. As big data involved in the simulations and the experiment, the understanding of the MGI remains challenging. The machine learning methods, which have been adopted in the MGI, developed with big data and artificial intelligence. This ch作者: stroke 時(shí)間: 2025-3-27 14:10
Machine Learning for High-Entropy Alloys,04, tremendous progresses and profound developments have been made in both the fundamental investigations and engineering applications. Unlike the conventional metallic alloys that typically only consist of one or two principal elements, HEA is composed of multi-principal elements in equimolar or ne作者: 緩和 時(shí)間: 2025-3-27 18:20
Two-Way TrumpetNets and TubeNets for Identification of Material Parameters,r identification of material constants. An idealized case of laminated composites is considered that may have a large number of material constants need to be determined, including Young’s modulus, Poisson’s ratio, and shear modulus for different plies in the laminate. The TrumpetNets (or TubeNets) c作者: modest 時(shí)間: 2025-3-28 00:12
Machine Learning Interatomic Force Fields for Carbon Allotropic Materials,ional cost, and transferability. In this mini review, we first summarize the disadvantages of traditional force field and the unique advantages of ML-based force field for molecular dynamics simulations. Then the basic workflow to develop the ML atomic force field is discussed in each step. Furtherm作者: ANIM 時(shí)間: 2025-3-28 04:02 作者: ASSAY 時(shí)間: 2025-3-28 07:10 作者: indenture 時(shí)間: 2025-3-28 12:30
Thermal Nanostructure Design by Materials Informatics,of great use in a wide range of applications like thermal management, thermal barriers, and thermoelectrics. Due to the superhigh degree of freedoms in terms of atom types and structural configurations, traditional searching algorithm may be powerless to find the optimal nanostructures with limited 作者: DEMUR 時(shí)間: 2025-3-28 17:39 作者: 可耕種 時(shí)間: 2025-3-28 19:57
Computer Vision and Mathematical Methods in Medical and Biomedical Image AnalysisECCV 2004 Workshops
