Printability in Multi-material Projection-Based 3-Dimensional Bioprinting by Chao-fan He, Tian-hong Qiao, Xu-chao Ren, Mingjun Xie, Qing Gao, Chao-qi Xie, Peng Wang, Yuan Sun, Huayong Yang, Yong He

“…The current research gap in this area substantively hinders the widespread application and rapid development of multi-material projection-based 3D bioprinting. To bridge this critical gap, we developed a multi-material projection-based 3D bioprinter capable of simultaneous printing with 6 materials. …”
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Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

“…A dataset of 1053 double perovskites was extracted from the Materials Project database, with 50 feature descriptors generated. …”
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Accurate classification of materials with elEmBERT: Element embeddings for chemical benchmarks by Shokirbek Shermukhamedov, Dilorom Mamurjonova, Michael Probst

“…In particular, we developed and tested the model using the Materials Project and MoleculeNet benchmarks, which include crystal properties and drug design-related benchmarks. …”
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Geographic-style maps with a local novelty distance help navigate in the materials space by Daniel Widdowson, Vitaliy Kurlin

“…This real-time novelty check is demonstrated by finding near-duplicates of the 43 materials produced by Berkeley’s A-lab in the world’s largest collections of inorganic structures, the Inorganic Crystal Structure Database and the Materials Project. To help future self-driving labs successfully identify novel materials, we propose navigation maps of the materials space where any new structure can be quickly located by its invariant descriptors similar to a geographic location on Earth.…”
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InteIrcultural Competence Development for International Business Environment by Garazi Azanza Martínez de Luco, José Antonio Campos Granados, Paulina Mizerska, Remigiusz Mazur

“…The final results of the project were achieved through the examination of training courses dedicated to developing intercultural competences that are available on the European market, consultations with representatives of international enterprises, gaps identification, preparation of the training program as well as pilot testing of the developed materials. Project results and the work that has been done in the transnational partnership of the Erasmus+ project highlight the importance of developing intercultural competences in the context of managing multicultural organisations.…”
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Dielectric tensor prediction for inorganic materials using latent information from preferred potential by Zetian Mao, WenWen Li, Jethro Tan

“…Virtual screening of thermodynamically stable materials from Materials Project for two discovery tasks, high-dielectric and highly anisotropic materials, identifies promising candidates including Cs2Ti(WO4)3 (band gap E g = 2.93eV, dielectric constant ε = 180.90) and CsZrCuSe3 (anisotropic ratio α r = 121.89). …”
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Uncertainty quantification for misspecified machine learned interatomic potentials by Danny Perez, Aparna P. A. Subramanyam, Ivan Maliyov, Thomas D. Swinburne

“…We demonstrate application to recent foundational machine learning interatomic potentials, accurately predicting and bounding errors in MACE-MPA-0 energy predictions across the diverse materials project database.…”
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Trends in changes in competency expectations towards employees in the copper sector by Jolanta Religa, Malwina Kobylańska, Luis Lopes

“…The analyses were conducted within the project ‘SkiComCu-Lifelong Learning Course for skills & competences in the Copper sector’, financed by EIT RawMaterials (Project Agreement No. 23043)The research results were showcased as the sets of skills and social competences essential for this sector, with particular emphasis on challenges related to innovation (industries 4.0 & 5.0), clean transition, and the circular economy. …”
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Predicting ionic conductivity in solids from the machine-learned potential energy landscape by Artem Maevskiy, Alexandra Carvalho, Emil Sataev, Volha Turchyna, Keian Noori, Aleksandr Rodin, A. H. Castro Neto, Andrey Ustyuzhanin

“…Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. …”
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Transformer-generated atomic embeddings to enhance prediction accuracy of crystal properties with machine learning by Luozhijie Jin, Zijian Du, Le Shu, Yan Cen, Yuanfeng Xu, Yongfeng Mei, Hao Zhang

“…By performing experiments on widely-used materials database, our CrystalTransformer-based UAEs (ct-UAEs) are shown to accurately capture complex atomic features, leading to a 14% improvement in prediction accuracy on CGCNN and 18% on ALIGNN when using formation energies as the target, based on the Materials Project database. We also demonstrated the good transferability of ct-UAEs across various databases. …”
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Mapping cation-eutaxy ternary with a phenomenological model by Jongbum Won, Taeyoung Kim, Minwoo Lee, Daniel W. Davies, Giyeok Lee, Aron Walsh, Aloysius Soon, Jong-Young Kim, Wooyoung Shim

“…Validation through computational high-throughput screening and the Materials Project database demonstrated its accuracy, successfully classifying 35 known cation-eutaxy ABX compounds and identifying 9 previously unreported candidates. …”
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Accelerating spin Hall conductivity predictions via machine learning by Jinbin Zhao, Junwen Lai, Jiantao Wang, Yi‐Chi Zhang, Junlin Li, Xing‐Qiu Chen, Peitao Liu

“…Additionally, we utilized Res‐CGCNN to conduct high‐throughput screenings of materials in the Materials Project database that were absent in the training set. …”
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High-throughput screening and machine learning classification of van der Waals dielectrics for 2D nanoelectronics by Yuhui Li, Guolin Wan, Yongqian Zhu, Jingyu Yang, Yan-Fang Zhang, Jinbo Pan, Shixuan Du

“…Here, we employed a topology-scale algorithm to screen vdW materials consisting of zero-dimensional (0D), one-dimensional (1D), and 2D motifs from Materials Project database. High-throughput first-principles calculations yielded bandgaps and dielectric properties of 189 0D, 81 1D and 252 2D vdW materials. …”
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A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods by Xinhua Liu, Kaiyi Yang, Lisheng Zhang, Wentao Wang, Sida Zhou, Billy Wu, Mengyu Xiong, Shichun Yang, Rui Tan

“…In this paper, we propose a forward prediction and screening framework for functional materials, which includes database selection, attributes, descriptors, machine learning models, and prediction and screening. Based on the Materials Project database, auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks, which finally screen 12 lithium-ion, 6 zinc-ion, and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials. …”
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DenseGNN: universal and scalable deeper graph neural networks for high-performance property prediction in crystals and molecules by Hongwei Du, Jiamin Wang, Jian Hui, Lanting Zhang, Hong Wang

“…We have achieved state-of-the-art performance (SOAT) on several datasets, including JARVIS-DFT, Materials Project, QM9, Lipop, FreeSolv, ESOL, and OC22, demonstrating the generality and scalability of our approach. …”
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Critical Raw Material Resource Potentials in Europe by Antje Wittenberg, Daniel de Oliveira

“…A sustainable change in the supply situation requires the targeted exploration of raw materials precisely within the framework of national geological research of suitable detail and in advance of entrepreneurial raw material projects. EU projects like GeoERA assist in shaping the tailor-made exploration programs fit for purpose. …”
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Teaching Materials Using the Project Based Learning Model on Learning Outcomes of Elementary School Students by Tukiyo, Triyono, Purwo Haryono, Tasari, Sri Suwartini

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The Assessment of the Effectiveness of the Implementation of Infrastructural Investments within the Regional Operational Programme for the Podkarpackie Province for the years 2007-... by Войцех Ліхота

“…The conducted analysis shows that most assumed material project indicators, i.e. output and result indicators, were achieved. …”
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Ancestral Environmental Technology: Pre-hispanic Foundations for Regenerative Sustainability by Itzel Cardoso-Hernández, Josemanuel Luna-Nemecio, Víctor Manuel Arribalzaga Tobón

“… Purpose: This contribution has two objectives: 1) to define in context, how the word téchnē and technology are linked to the Nahuatl notion in context; and 2) to characterize pre-hispanic environmental technologies in order to subjectively and materially project the practical utility of ancestral environmental technologies, precisely because they are capable of halting and even reversing the socio-environmental consequences derived from the current climate and ecosystem crisis. …”
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Validation of the vertical canopy cover profile products derived from GEDI over selected forest sites by Yu Li, Hongliang Fang, Yao Wang, Sijia Li, Tian Ma, Yunjia Wu, Hao Tang

“…Canopy cover (CC) quantifies the proportion of canopy materials projected vertically onto the ground surface. …”
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