jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Baoyu Wang | Wenyu Chen | Liang Zou | Tao Wang | Zongwen Li | Zhongyi Wang | Haoran Xu | Lijun Xu | Shangui Lan | Pu Feng | Yinchang Ma | Dongxing Zheng | Xin He | Yang Xu | Zheng‐Dong Luo | Zhenhua Wu | Yan Liu | Genquan Han | Xixiang Zhang | Bin Yu | Fei Xue
Source: Nano Letters (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

A Programmable Nonvolatile Schottky Diode Based on van der Waals Ferroelectric Junction Programmable and nonvolatile Schottky junctions are highly desirable for next-generation electronic and neuromorphic systems. However, conventional metal-semiconductor and even van der Waals (vdW) Schottky diodes often suffer from fixed rectifying behaviors or limited tunability. Here, we report a programmable nonvolatile ferroelectric Schottky diode based on a vdW heterojunction between semimetallic 1T'-MoTe<sub>2</sub> and ferroelectric α-In<sub>2</sub>Se<sub>3</sub>. The diode exhibits near-ideal performance, including a rectification ratio exceeding 10<sup>4</sup>, a leakage current down to 1 pA, and an ideality factor as low as 1.38. By switching ferroelectric polarization, the Schottky barrier can be modulated in a programmable manner, enabling reversible, nonvolatile, and multilevel rectification states. The device demonstrates polarization-dependent photoresponse and transient integrate-and-leak dynamics, closely resembling biological spiking neurons. A spiking neural network is implemented based on this behavior, achieving image recognition accuracy up to 98.4%. This work establishes programmable ferroelectric Schottky diodes as promising candidates for low-power memory, reconfigurable logic, and neuromorphic vision. van der Waals force ; Ferroelectricity ; Schottky diode ; Optoelectronics ; Materials science ; Schottky barrier ; Diode ; Nanotechnology ; Physics ; Dielectric ; Molecule ; Quantum mechanics 2D Materials and Applications ; Acoustic Wave Resonator Technologies ; Semiconductor Quantum Structures and Devices
Authors: Uzair Sajjad | Ahsan Ali | Hafız Muhammad Ali | Khalid Hamid
Source: Energy Conversion and Management X (review, 2025-05-31)
Field: Materials Science — Materials Chemistry

A review on machine learning driven next generation thermoelectric generators The integration of machine learning (ML) methods such as ANN, DNN, GA, cGAN, Bi-LSTM, and SHAP for optimizing various thermoelectric generator (TEG) types, including segmented, annular, hybrid, and conventional systems, is presented. ML enables significant improvements in power output, efficiency, and geometry optimization while addressing challenges like data limitations, interpretability, and implementation. • ML integration into thermoelectric generators (TEGs) is explored. • The effectiveness of various ML approaches is discussed. • ML can predict the performance of various TEGs, including segmented, hybrid, annular, and photovoltaic-based. • ML driven design optimization for various TEGs is also elaborated. • Challenges and future research directions of ML-TEG integration are highlighted. Thermoelectric (TE) materials and devices have emerged as promising technologies for energy conversion and waste heat recovery. Yet, challenges remain in improving TE efficiency due to the complicated relationship between TE material properties, geometrical designs, and performance optimization. Machine learning (ML) on the other hand has played a great role in discovery and optimization of TEMs, offering new insights and accelerating the development of high-performance TE generators (TEG). This review provides a comprehensive overview of the applications of ML in TEG research, highlighting the methodologies and techniques employed for performance prediction and optimization of various TEGs including TEGs, segmented (STEGs), annular (ATEGs), photovoltaic (PVTEGs), and other hybrid (HTEGs) along with their maximum power point tracking (MPPT) and integration of TEGs with other systems. Key advancements include the use of regression and optimization as well as physics informed methods. Also, the potential benefits of integrating ML methods with other computational schemes such as COMSOL and CFD are highlighted. This review also addresses the challenges of limited datasets, model interpretability, and experimental validation. Future research directions are proposed, focusing on integrating ML with experimental and computational approaches to unlock new pathways for thermoelectric generators’ design. In general, this review provides valuable insights and potential of AI/ML into the advancement of TEG technology. Thermoelectric generator ; Thermoelectric effect ; Computer science ; Materials science ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning and ELM ; Advanced Thermodynamics and Statistical Mechanics
Authors: Najmin Mursal | Anjali R. Shelake | Shubham A. Ahir | Pooja P. Sonawane | Manickam Selvaraj | Mohammed A. Assiri | S.B. Kulkarni | Santosh S. Sutar | Rutuja U. Amate | Chan‐Wook Jeon | Tukaram D. Dongale | Prashant Shah
Source: Colloids and Surfaces A Physicochemical and Engineering Aspects (article, 2025-09-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

From data to device: Machine learning-driven optimization, predictions, and experimental insights for engineering high-performance MnO2 supercapacitors Supercapacitor ; Capacitance ; Random forest ; Stability (learning theory) ; Key (lock) ; Decision tree ; Electrode Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Advanced Memory and Neural Computing
Authors: Qi Ren | Bonan Zhu | Gang Tang | Jiawang Hong
Source: Small (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Highly Deformable Van der Waals Chalcogenides with Superior Thermoelectric Performance from Interpretable Machine Learning Van der Waals (vdW) chalcogenide-based flexible thermoelectric devices hold great promise for wearable electronics. However, intrinsic vdW chalcogenides that combine high flexibility with superior thermoelectric figures of merit (ZT) remain extremely rare. Consequently, there is an urgent need to develop methods capable of high-throughput screening to identify potential vdW chalcogenides with both robust flexibility and favorable ZT value. In this study, over 1000 vdW chalcogenides are high-throughput screened for their flexibility and ZT values. Flexibility is evaluated using the previously developed deformability factor, while ZT values are predicted using a machine learning model. Several candidates with large deformability and high ZT are successfully identified. Among these, NbSe<sub>2</sub>Br<sub>2</sub> emerges as the top-performing material. Further first-principles calculations reveal that it achieves a maximum ZT value of 1.35 at 1000 K, the highest reported so far among flexible inorganic thermoelectric materials. Its power factor value of 8.1 µW cm<sup>-1</sup>K<sup>-2</sup> at 300 K also surpasses most organic and inorganic flexible thermoelectric materials. The high ZT<sub>max</sub> is mainly contributed by the extremely low thermal conductivity and the high Seebeck coefficient along the out-of-plane direction at high temperatures. The study offers new material options for the development and application of flexible thermoelectric devices based on layered chalcogenides. Thermoelectric effect ; van der Waals force ; Materials science ; Thermoelectric materials ; Seebeck coefficient ; Chalcogenide ; Figure of merit ; Flexibility (engineering) ; Engineering physics ; Thermal conductivity ; Nanotechnology ; Optoelectronics ; Condensed matter physics ; Thermodynamics ; Composite material ; Physics ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Muhammad Tayyab Iqbal | Saman Saeeda | Tehleel Zahra | Zeeshan Umar | Waheed Zaman Khan | Muhammad Adnan | Hasan Raza | Ghafar Ali Shah | Muhammad Toffique
Source: Scholars Journal of Engineering and Technology (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Next-Generation Materials Discovery Using DFT: Functional Innovation, Solar Energy, Catalysis, and Eco Toxicity Modelling Providing calculations with high precision and on the atom scale of materials’ electronic, optical and structural characteristics, DFT has fundamentally changed the field of computational materials science. This review provides an extensive review of DFT’s development from early local and semi-local functionals (LDA, GGA) through more sophisticated hybrid (HSE06, B3LYP) and meta-GGA (SCAN, MVS) methods. In addition, the review includes state-of-the-art beyond-DFT methods such as TDDFT, the GW approximation, and the Bethe–Salpeter Equation (BSE) necessary for excited states and optoelectronic behavior understanding and predictions. The combination of machine learning techniques and DFT is studied as a promising method of accelerating the discovery of new materials. A major emphasis is placed on modelling perovskite, silicon-based, and thin-film solar cells (CIGS, CZTS), using DFT to manage band gaps. The importance of the theory in increasing charge transport, reducing the rates of recombination and increasing power conversion efficiency are emphasized. In the field of environmental sustainability, DFT is applied to design efficient photocatalysts for wastewater purification and hydrogen production to gain knowledge on the process of pollutants decomposition and the toxicity of emerging compounds. DFT-derived data supports lifecycle assessments to shape eco-design in photovoltaic systems, as well as decisions about photovoltaic recyclability, energy payback, and environmental sustainability. This review brings forth DFT’s key role in pushing for breakthrough development for energy, environment, and nanotechnological sectors. Indeed, based on quantum simulations, as well as on the evidence and data-backed approaches, DFT continues to widen the field of sustainable material science and clean energy design. Photovoltaic system ; Field (mathematics) ; Process (computing) ; Key (lock) ; Density functional theory ; Energy transformation ; Renewable energy ; Scale (ratio) ; Efficient energy use Machine Learning in Materials Science ; Chemical and Physical Properties of Materials ; Inorganic Fluorides and Related Compounds
Authors: M. Gonzalo Claros | Amanda Bullones | Antonio Castro | Elena Lima‐Cabello | M.A. Viruel | María F. Suárez | R. Romero‐Aranda | Noé Fernández‐Pozo | Francisco J. Veredas | Andrés Belver | Juan de Dios Alché Ramírez
Source: Biology (review, 2025-03-11)
Field: Chemistry — Organic Chemistry

Multi-Omic Advances in Olive Tree (Olea europaea subsp. europaea L.) Under Salinity: Stepping Towards ‘Smart Oliviculture’ Soil salinisation is threatening crop sustainability worldwide, mainly due to anthropogenic climate change. Molecular mechanisms developed to counteract salinity have been intensely studied in model plants. Nevertheless, the economically relevant olive tree (<i>Olea europaea</i> subsp. <i>europaea</i> L.), being highly exposed to soil salinisation, deserves a specific review to extract the recent genomic advances that support the known morphological and biochemical mechanisms that make it a relative salt-tolerant crop. A comprehensive list of 98 olive cultivars classified by salt tolerance is provided, together with the list of available olive tree genomes and genes known to be involved in salt response. Na<sup>+</sup> and Cl<sup>-</sup> exclusion in leaves and retention in roots seem to be the most prominent adaptations, but cell wall thickening and antioxidant changes are also required for a tolerant response. Several post-translational modifications of proteins are emerging as key factors, together with microbiota amendments, making treatments with biostimulants and chemical compounds a promising approach to enable cultivation in already salinised soils. Low and high-throughput transcriptomics and metagenomics results obtained from salt-sensitive and -tolerant cultivars, and the future advantages of engineering specific metacaspases involved in programmed cell death and autophagy pathways to rapidly raise salt-tolerant cultivars or rootstocks are also discussed. The overview of bioinformatic tools focused on olive tree, combined with machine learning approaches for studying plant stress from a multi-omics perspective, indicates that the development of salt-tolerant cultivars or rootstocks adapted to soil salinisation is progressing. This could pave the way for 'smart oliviculture', promoting more productive and sustainable practices under salt stress. Olea ; Biology ; Cultivar ; Oleaceae ; Soil salinity ; Salinity ; Olive trees ; Botany ; Biotechnology ; Ecology Edible Oils Quality and Analysis ; Plant Stress Responses and Tolerance ; Potato Plant Research
Authors: C Andreoli | Mária Dušinská | Cecilia Bossa | Chiara Laura Battistelli | Maria João Silva | Henriqueta Louro
Source: Regulatory Toxicology and Pharmacology (review, 2025-06-05)
Field: Materials Science — Materials Chemistry

Regulatory practices on the genotoxicity testing of nanomaterials and outlook for the future The toxicity of nanomaterials(NMs) is closely tied to their physicochemical properties, such as size, shape, surface chemistry, stability in biological medium, and state of agglomeration as well to their uptake by cells. Key deficiencies in standardized testing approaches have been identified and tackled in recent years. Within the landscape of new approach methods (NAMs), the aim of this work is to review existing approaches for genotoxicity testing of the NMs under different regulatory domains, with a perspective on the development of NAMs that can solve longstanding difficulties in NMs' risk assessment. It critically examines international and European Union guidelines, highlighting the need for harmonization and the potential of NAMs to drive next-generation risk assessment. However, further collaboration, research and validation are essential to gain wider acceptance and applicability. The contribution of innovative technological approaches based on big data, artificial intelligence and machine learning, may pave powerful comparisons among different sectors and grouping strategies that will furtherance innovation in the nanotoxicology research. The future outlook for the genotoxicity testing of NMs will depend on increased cooperation between regulatory agencies, researchers, and industry stakeholders. Key steps toward overcoming current obstacles include establishing clearer pathways for data sharing, standardizing testing protocols, and fostering greater international collaboration. Genotoxicity ; Risk analysis (engineering) ; Nanomaterials ; Business ; Chemistry ; Nanotechnology ; Materials science ; Toxicity Nanoparticles: synthesis and applications ; Chemistry and Chemical Engineering ; Microplastics and Plastic Pollution
Authors: Kian P. Abdolazizi | Roland C. Aydin | Christian J. Cyron | Kevin Linka
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Constitutive Kolmogorov–Arnold Networks (Ckans): Combining Accuracy and Interpretability in Data-Driven Material Modeling Interpretability ; Artificial intelligence ; Computer science ; Data mining ; Machine learning Machine Learning in Materials Science
Authors: Mohammad Behnamnia | Hossein Sarvi | Abolfazl Dehghan Monfared
Source: Renewable Energy (article, 2025-05-12)
Field: Materials Science — Materials Chemistry

Leveraging AI for accurate prediction of hydrogen density (in pure/mixed Form): Implications for hydrogen energy transition processes Hydrogen ; Energy (signal processing) ; Computer science ; Materials science ; Chemistry ; Physics ; Organic chemistry ; Quantum mechanics Machine Learning in Materials Science ; Hybrid Renewable Energy Systems ; Nuclear Physics and Applications
Authors: Shihu Ding | Minghua Zhou | Abdulgalim B. Isaev | Nuo Meng | Xiguo Zhang | Lin Ma | Wei Wang
Source: Coordination Chemistry Reviews (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Recent advances in carbon dots interface engineering for altering charge transfer: From elaborate construction to multiple applications Chemistry ; Interface (matter) ; Charge (physics) ; Nanotechnology ; Carbon fibers ; Engineering physics ; Organic chemistry ; Molecule Carbon and Quantum Dots Applications ; Quantum Dots Synthesis And Properties ; Electrochemical sensors and biosensors
Authors: Shuning Cai | Joakim S. Jestilä | Peter Liljeroth | Adam S. Foster
Source: Journal of the American Chemical Society (article, 2025-03-06)
Field: Chemistry — Spectroscopy

Direct Imaging of Chirality Transfer Induced by Glycosidic Bond Stereochemistry in Carbohydrate Self-Assemblies Carbohydrates, essential biological building blocks, exhibit functional mechanisms tied to their intricate stereochemistry. Subtle stereochemical differences, such as those between the anomers maltose and cellobiose, lead to distinct properties due to their differing glycosidic bonds; the former is digestible by humans, while the latter is not. This underscores the importance of precise structural determination of individual carbohydrate molecules for deeper functional insights. However, their structural complexity and conformational flexibility, combined with the high spatial resolution needed, have hindered direct imaging of carbohydrate stereochemistry. Here, we employ noncontact atomic force microscopy integrated with a data-efficient, multifidelity structure search approach accelerated by machine learning integration to determine the precise 3D atomic coordinates of two carbohydrate anomers on Au(111). We observe that the stereochemistry of the glycosidic bond regulates on-surface chiral selection in carbohydrate self-assemblies. The reconstructed models, validated against experimental data, provide reliable atomic-scale structural evidence, uncovering the origin of the on-surface chirality from carbohydrate anomerism. Our study confirms that nc-AFM is a reliable technique for real-space discrimination of carbohydrate stereochemistry at the single-molecule level, providing a pathway for bottom-up investigations into the structure-property relationships of carbohydrates in biological research and materials science. Chemistry ; Glycosidic bond ; Chirality (physics) ; Carbohydrate ; Stereochemistry ; Organic chemistry ; Enzyme Molecular spectroscopy and chirality ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: Yong Ju Lee | Do Young Lee | Tai-Ju Lee | Hyoung Jin Kim
Source: Carbohydrate Polymers (article, 2025-08-09)
Field: Materials Science — Biomaterials

Cellulose I crystallinity estimation using a combination of infrared spectroscopy and machine learning approaches Crystallinity ; Infrared spectroscopy ; Cellulose ; Infrared ; Spectroscopy ; Materials science ; Polymer science ; Analytical Chemistry (journal) ; Computer science ; Biological system ; Chemistry ; Chemical engineering ; Composite material ; Engineering ; Optics ; Physics ; Organic chemistry ; Biology Advanced Cellulose Research Studies ; Biofuel production and bioconversion ; Material Properties and Processing
Authors: Madappa C. Maridevaru | Aiswarya Konchery | Sambandam Anandan | Vetriselvi Thirunavukarasu | Abdullah Al Souwaileh | Jerry J. Wu
Source: Journal of Physics and Chemistry of Solids (article, 2025-01-21)
Field: Chemistry — Inorganic Chemistry

Lanthanum-based metal-organic framework nanorods for removal of dye pollutants with machine language predictions Lanthanum ; Pollutant ; Nanorod ; Metal ; Materials science ; Environmental chemistry ; Environmental science ; Chemistry ; Nuclear chemistry ; Inorganic chemistry ; Nanotechnology ; Metallurgy ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Machine Learning and ELM
Authors: Selorm Yao‐Say Solomon Adade | Hao Lin | Nana Adwoa Nkuma Johnson | Xorlali Nunekpeku | John‐Nelson Ekumah | Bridget Ama Kwadzokpui | Ernest Teye | Waqas Ahmad | Quansheng Chen
Source: Food Control (article, 2025-04-11)
Field: Chemistry — Analytical Chemistry

Spectroscopic techniques for edible oil evaluation - Technology overview and recent applications from lab to industry Biochemical engineering ; Edible oil ; Petroleum industry ; Nanotechnology ; Process engineering ; Environmental science ; Engineering ethics ; Engineering ; Materials science ; Chemistry ; Food science ; Environmental engineering Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Analytical chemistry methods development
Authors: Karim Ali Shah | Rodrigo Q. Albuquerque | Christian Brütting | Marcel Dippold | Holger Ruckdäschel
Source: Polymer (article, 2025-02-01)
Field: Materials Science — Polymers and Plastics

Low-density polyamide 12 foams using Bayesian optimization and inverse design This study introduces a novel, comprehensive approach to optimizing and designing batch foaming of low-density polyamide 12 (PA-12) using advanced machine learning (ML) techniques. Bayesian optimization was used to minimize the foam density, which decreased from approximately 900 to 150 kg/m 3 in a single new experiment. A PA-12 foam density of 50 kg/m 3 , the lowest achieved, was recorded. In addition, an inverse design approach was used to check the robustness of the model by identifying the specific processing parameters required to achieve the desired foam density. Finally, PA-12 foams with similar densities but different processing parameters were obtained using ML. The study highlights the effectiveness of integrating these ML methodologies in the development of lightweight, high-performance polymer foams, which is much more sustainable than traditional methods for achieving low-density foams. • PA 12 foam with a density of approximately 50 kg/m 3 has been achieved. • Density is minimized using Bayesian optimization in the PA12 autoclave foaming. • Processing parameters are identified using inverse design to achieve the desired PA 12 foam density. • GBR model shows the best result for predicting the foam density. Polyamide ; Inverse ; Bayesian probability ; Materials science ; Bayesian optimization ; Composite material ; Mathematical optimization ; Computer science ; Mathematics ; Artificial intelligence Polymer Foaming and Composites ; Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies
Authors: Yanjing He | Zhi Fang | Weijiang Xue | Tongan Yan | Shitong Zhang | Zhengqing Zhang | Hongliang Huang | Chongli Zhong
Source: Separation and Purification Technology (article, 2025-03-12)
Field: Chemistry — Inorganic Chemistry

Discovery of metal-organic frameworks for efficient NF3/N2 separation by integrating high-throughput computational screening, machine learning, and experimental validation Throughput ; Separation (statistics) ; High-throughput screening ; Metal-organic framework ; Biochemical engineering ; Computer science ; Chemistry ; Process engineering ; Machine learning ; Engineering ; Telecommunications ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; Zeolite Catalysis and Synthesis