jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Leandro Goulart de Araujo | Léa Vilcocq | Pascal Fongarland | Yves Schuurman
Source: Chemical Engineering Journal (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics • A bibliometric and systematic review of catalysis and machine learning is provided. • Explains secondary data, new descriptors & feature engineering in ML applications. • Covers ML model trade-offs and factors influencing model selection in catalysis. • Discusses future directions for ML in optimizing catalytic systems. • Examines both theory-driven and data-driven approaches in catalysis. A thorough grasp of the underlying mechanisms of catalytic reactions is indispensable for furthering our understanding of chemical kinetics. However, traditional phenomenological models present certain difficulties, including the tendency to converge to local minima and a reliance on parameters that are difficult to measure, particularly in complex catalytic systems. These systems frequently comprise intricate feedstock compositions or catalyst structures that are challenging to anticipate through theory-driven approaches. This often results in the utilization of unrealistic models or the allocation of considerable computational resources. While traditional methods offer valuable insights, they are constrained by these challenges and the lack of robust uncertainty assessments. In view of these limitations, data-driven modeling, in particular through machine learning (ML), has emerged as a promising alternative in catalysis in the last five years. This review examines recent advancements in ML applications within the field of catalysis, encompassing a broad range of applications, including data generation, descriptor identification, and feature engineering. While the review takes a general perspective on ML in catalysis, particular attention is given to applications in chemical kinetics wherever relevant, recognizing the interconnection between reaction kinetics, catalyst design, reaction conditions, and reactor configurations. The discussion includes various ML models, including interpretable yet less flexible models and more complex black-box models, and considers their applications in catalysis. It also examines key factors in model selection, such as generalizability, computational efficiency, data quality, and interpretability. Finally, it outlines future directions for ML in catalysis, emphasizing how these technologies can further enhance the optimization, design, and improvement of catalytic systems. Perspective (graphical) ; Kinetics ; Nanotechnology ; Chemistry ; Biochemical engineering ; Management science ; Computer science ; Engineering ; Materials science ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Topic Modeling
Authors: Jianliang Zhang | Ru Jia | Kar Ban Tan | Jiaming Li | Shichong Xu | Guobing Ying | Wenjuan Han | Ming Lu
Source: Nano-Micro Letters (review, 2025-03-02)
Field: Materials Science — Materials Chemistry

A Review of MAX Series Materials: From Diversity, Synthesis, Prediction, Properties Oriented to Functions MAX series materials, as non-van der Waals layered multi-element compounds, contribute remarkable regulated properties and functional dimension, combining the features of metal and ceramic materials due to their inherently laminated crystal structure that M<sub>n+1</sub>X<sub>n</sub> slabs are intercalated with A element layers. Oriented to the functional requirements of information, intelligence, electrification, and aerospace in the new era, how to accelerate MAX series materials into new quality productive forces? The systematic enhancement of knowledge about MAX series materials is intrinsic to understanding its low-dimensional geometric structure characteristics, and physical and chemical properties, revealing the correlation of composition, structure, and function and further realizing rational design based on simulation and prediction. Diversity also brings complexity to MAX materials research. This review provides substantial tabular information on (I) MAX's research timeline from 1960 to the present, (II) structure diversity and classification convention, (III) synthesis route exploration, (IV) prediction based on theory and machine learning, (V) properties, and (VI) functional applications. Herein, the researchers can quickly locate research content and recognize connections and differences of MAX series materials. In addition, the research challenges for the future development of MAX series materials are highlighted. Series (stratigraphy) ; Diversity (politics) ; Computer science ; Timeline ; van der Waals force ; Materials science ; Nanotechnology ; Mathematics ; Chemistry ; Molecule ; Geology MXene and MAX Phase Materials ; 2D Materials and Applications ; Advanced Photocatalysis Techniques
Authors: Tao Zhang | Qitong Ye | Yipu Liu | Qingyi Liu | Zengyu Han | Dongshuang Wu | Zhiming Chen | Yue Li | Hong Jin Fan
Source: Nature Communications (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Data-driven discovery of biaxially strained single atoms array for hydrogen production The structure-performance relationship for single atom catalysts has remained unclear due to the averaged coordination information obtained from most single-atom catalysts. Periodic array of single atoms may provide a platform to tackle this inaccuracy. Here, we develop a data-driven approach by incorporating high-throughput density functional theory computations and machine learning to screen candidates based on a library of 1248 sites from single atoms array anchored on biaxial-strained transition metal dichalcogenides. Our screening results in Au atom anchored on biaxial-strained MoSe<sub>2</sub> surface via Au-Se<sub>3</sub> bonds. Machine learning analysis identifies four key structural features by classifying the ΔG<sub>H*</sub> data. We show that the average band center of the adsorption sites can be a predictor for hydrogen adsorption energy. This prediction is validated by experiments which show single-atom Au array anchored on biaxial-strained MoSe<sub>2</sub> archives 1000 hour-stability at 800 mA cm<sup>-2</sup> towards acidic hydrogen evolution. Moreover, active hotspot consisting of Au atoms array and the neighboring Se atoms is unraveled for enhanced activity. Materials science ; Hydrogen ; Production (economics) ; Nanotechnology ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Xingcheng Wang | Ji Zhang | Xiaohan Ma | Huajie Luo | Laijun Liu | Hui Liu | Jun Chen
Source: Nature Communications (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage The high-entropy strategy has emerged as a prevalent approach to boost capacitive energy-storage performance of relaxors for advanced electrical and electronic systems. However, exploring high-performance high-entropy systems poses challenges due to the extensive compositional space. Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm<sup>-3</sup> with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. Following basic experiments, a (Bi<sub>0.5</sub>Na<sub>0.5</sub>)TiO<sub>3</sub>-based high-entropy relaxor characterized by fine grains, weakly-coupled and small-sized polar clusters was identified. This resulted in a near-linear polarization behavior and an ultrahigh breakdown strength of 95 kV mm<sup>-1</sup>. Further, this high-entropy realxor presented a high discharge energy density of 7.7 J cm<sup>-3</sup> under discharge rate of about 27 ns, along with superior temperature and fatigue stability. Our results present the data-driven model for efficiently exploring high-performance high-entropy relaxors, demonstrating the potential of machine learning in developing relaxors. Entropy (arrow of time) ; Materials science ; Energy storage ; Computer science ; Statistical physics ; Thermodynamics ; Machine learning ; Physics Ferroelectric and Piezoelectric Materials ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Hai‐Yang Liu | Shanshan Zhu | Yongfeng Zhi | Huijuan Yue | Xiaoming Liu
Source: Chemical Science (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Donor–acceptor type covalent organic frameworks: design, optimization strategies and applications Covalent organic frameworks (COFs) have emerged as one of the hottest research topics in various applications due to their designed structures, adjustable pore sizes, and abundant active sites. However, the high electron-hole recombination rate and short carrier lifetime in non-donor-acceptor type COFs are inevitable issues. The electron donor-acceptor (D-A) type COFs, which are synthesized by introducing donor and acceptor units into the COF skeleton, combined with effective carrier separation, adjustable bandgap, and sensitive photoelectric response, are considered an effective strategy for improving exciton dissociation and optimizing carrier transport. In recent years, D-A type COFs have witnessed exponential expansion in applications spanning photocatalysis, energy storage, and photothermal therapy. Consequently, there exists an imperative necessity to comprehensively summarize and expound upon the challenges and prospects pertaining to the development of D-A type COFs. In this review, we first summarize the common connecting bonds as well as the building blocks for the synthesis of D-A type COFs. Several strategies for optimizing D-A type COFs and their recent progress in photocatalysis, photothermal therapy, and energy storage are then presented. Finally, we delineate the current challenges and impediments of D-A type COFs and offer a forward-looking perspective on the future development of D-A type COFs. This review is poised to encourage researchers with a more profound comprehension of the design strategies and applications of D-A type COFs, thereby inspiring them to conduct more incisive research into the challenges and developmental prospects of D-A type COFs. Covalent bond ; Acceptor ; Photothermal therapy ; Photocatalysis ; Nanotechnology ; Design elements and principles ; Electron acceptor ; Covalent organic framework ; Materials science ; Chemistry ; Photochemistry ; Organic chemistry ; Systems engineering ; Engineering ; Catalysis ; Physics Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Nouf Ahmed Althumairi | M. Hjiri | Abdullah M. Aldukhayel | Anouar Jbeli | Kais Iben Nassar
Source: Nanomaterials (review, 2025-08-29)
Field: Materials Science — Materials Chemistry

Recent Advances in Dielectric and Ferroelectric Behavior of Ceramic Nanocomposites: Structure Property Relationships and Processing Strategies In the race toward next-generation electronics and energy systems, ceramic nanocomposites have taken center stage due to their remarkable dielectric and ferroelectric functionalities. By pushing the boundaries of nanoscale engineering, recent studies have shown how microstructural control and interfacial design can unlock unprecedented levels of polarization, permittivity, and frequency stability. This review presents a critical and up-to-date synthesis of the last decade's progress in ceramic-based nanocomposites, with a special focus on the structure property processing nexus. Diverse processing techniques ranging from conventional sintering to advanced spark plasma sintering and scalable wet-chemical methods are analyzed for their influence on phase purity, grain boundary behavior, and interfacial polarization. The review also explores breakthroughs in lead-free and eco-friendly systems, flexible ferroelectric nanocomposites, and high-k dielectrics suitable for miniaturized devices. By identifying both the scientific opportunities and persistent challenges in this rapidly evolving field, this work aims to guide future innovations in material design, device integration, and sustainable performance. Materials science ; Ceramic ; Dielectric ; Nanocomposite ; Ferroelectricity ; Ferroelectric ceramics ; Composite material ; Nanotechnology ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Dielectric properties of ceramics
Authors: Anthony V. Powell | Paz Vaqueiro | Sahil Tippireddy | Jesús Prado‐Gonjal
Source: Nature Reviews Chemistry (review, 2025-03-25)
Field: Materials Science — Materials Chemistry

Exploiting chemical bonding principles to design high-performance thermoelectric materials Thermoelectric effect ; Materials science ; Engineering physics ; Nanotechnology ; Thermoelectric materials ; Computer science ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science
Authors: Jan de Witt | Tom Luthe | Johanna Wiechert | Kenneth Jensen | Tino Polen | Astrid Wirtz | Stephan Thies | Julia Frunzke | Benedikt Wynands | Nick Wierckx
Source: Nature Microbiology (article, 2025-02-10)
Field: Materials Science — Biomaterials

Upcycling of polyamides through chemical hydrolysis and engineered Pseudomonas putida Aliphatic polyamides, or nylons, are widely used in the textile and automotive industry due to their high durability and tensile strength, but recycling rates are below 5%. Chemical recycling of polyamides is possible but typically yields mixtures of monomers and oligomers which hinders downstream purification. Here, Pseudomonas putida KT2440 was engineered to metabolize C<sub>6</sub>-polyamide monomers such as 6-aminohexanoic acid, ε-caprolactam and 1,6-hexamethylenediamine, guided by adaptive laboratory evolution. Heterologous expression of nylonases also enabled P. putida to metabolize linear and cyclic nylon oligomers derived from chemical polyamide hydrolysis. RNA sequencing and reverse engineering revealed the metabolic pathways for these non-natural substrates. To demonstrate microbial upcycling, the phaCAB operon from Cupriavidus necator was heterologously expressed to enable production of polyhydroxybutyrate (PHB) from PA6 hydrolysates. This study presents a microbial host for the biological conversion, in combination with chemical hydrolysis, of polyamide monomers and mixed polyamids hydrolysates to a value-added product. Pseudomonas putida ; Hydrolysis ; Polyamide ; Chemistry ; Biochemistry ; Organic chemistry ; Enzyme biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Chemistry and Chemical Engineering
Authors: Hao-Yu Lan | Chih‐Pin Lin | Lina Liu | Jun Cai | Zheng Sun | Peng Wu | Yuanqiu Tan | Shao-Heng Yang | Tuo‐Hung Hou | Joerg Appenzeller | Zhihong Chen
Source: Nature Communications (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Uncovering the doping mechanism of nitric oxide in high-performance P-type WSe2 transistors Atomically thin two-dimensional (2D) semiconductors are promising candidates for beyond-silicon electronic devices. However, an excessive contact resistance due to ineffective or non-existent doping techniques hinders their technological readiness. Here, we unveil the doping mechanism of pure nitric oxide and demonstrate its effectiveness on wafer-scale grown monolayer and bilayer tungsten diselenide (1L- and 2L-WSe<sub>2</sub>) transistors, where doping bands induced by nitric oxide can realign the Schottky barrier and approach p-type unipolar transport. This doping approach, combined with a scaled high-κ dielectric, yields WSe<sub>2</sub> transistors with high performance metrics. For monolayer WSe<sub>2</sub>, we achieved an on-state current of 300 μA/μm (at a drain-to-source voltage of -1 V and overdrive voltage of -0.8 V), contact resistance of 875 Ω·μm, peak transconductance of 400 μS/μm, and a subthreshold swing of 70 mV/dec, while preserving on/off ratios >10<sup>9</sup>, minimal variability, and good stability over 24 days under moderate thermal conditions. For bilayer WSe<sub>2</sub>, the devices exhibit an on-state current of 448 μA/μm and contact resistance of 390 Ω·μm, further showcasing the scalability and effectiveness of the NO doping method. Our findings establish NO doping as a promising technique for realizing high-performance p-type 2D transistors and advancing next-generation ultra-scaled electronic devices. Nitric oxide ; Doping ; Mechanism (biology) ; Transistor ; Oxide ; Materials science ; Nanotechnology ; Chemistry ; Optoelectronics ; Physics ; Metallurgy 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Hongxin Yang | Yuan Zhang | Zhipeng Liu | Chunfang Hu | Junbiao Li | Hailong Liao | Minhua Shao | Meng Ni | Bin Chen | Zongping Shao | Heping Xie
Source: Nature Communications (article, 2025-04-02)
Field: Materials Science — Materials Chemistry

Hydration-induced stiffness enabling robust thermal cycling of high temperature fuel cells cathode Thermo-mechanics of cathode is closely related to the durability of high-temperature solid oxide fuel cells (SOFCs), with two main mechanical failures during thermal cycling: interface delamination and bulk cracking of cathode. Bulk cracking, caused by insufficient fracture strength/stiffness is a big concern but often overlooked. Here, we introduce chemical hydration to offset the thermal expansion, enhancing the cathodic mechanical stiffness and fracture strength, thus promoting the thermo-mechanical durability of cathode in proton ceramic fuel cells (PCFCs). Such chemical-induced expansion offset is achieved by strengthening intergranular bonding inside the bulk cathode after the hydration, preventing granule detachment during thermal shrinkage. As a demonstration, the stiffness-enhanced air electrode (BaCo<sub>0.7</sub>Ce<sub>0.15</sub>Y<sub>0.15</sub>O<sub>3</sub>, noted as s-BCC-Y) exhibits 86% enhancement of fracture strength, thus thermal cycling stability with almost no degradation after 35 harsh thermal cycles between 600 and 300 °C, surpassing pristine BaCo<sub>0.7</sub>Ce<sub>0.3</sub>O<sub>3</sub> and many cobalt-free PCFC cathodes. Benefitted from the improved stiffness of cathode, full cell with the s-BCC-Y electrode demonstrates enhanced power output. This work highlights the importance of bulk cathode thermo-mechanics in developing robust SOFCs for high temperature energy applications. Materials science ; Cathode ; Composite material ; Temperature cycling ; Oxide ; Durability ; Fracture mechanics ; Thermal expansion ; Thermal ; Metallurgy ; Electrical engineering Advancements in Solid Oxide Fuel Cells ; Electronic and Structural Properties of Oxides ; Fuel Cells and Related Materials
Authors: Kevin Eloff | Konstantinos Kalogeropoulos | Amandla Mabona | Oliver Morell | Rachel Catzel | Esperanza Rivera‐de‐Torre | Jakob Berg Jespersen | Wesley Williams | Sam P. B. van Beljouw | Marcin J. Skwark | Andreas H. Laustsen | Stan J. J. Brouns | Anne Ljungars | Erwin M. Schoof | Jeroen Van Goey | Ulrich auf dem Keller | Karim Beguir | Nicolás López Carranza | Timothy P. Jenkins
Source: Nature Machine Intelligence (article, 2025-03-31)
Field: Chemistry — Spectroscopy

InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments Mass spectrometry-based proteomics focuses on identifying the peptide that generates a tandem mass spectrum. Traditional methods rely on protein databases but are often limited or inapplicable in certain contexts. De novo peptide sequencing, which assigns peptide sequences to spectra without prior information, is valuable for diverse biological applications; however, owing to a lack of accuracy, it remains challenging to apply. Here we introduce InstaNovo, a transformer model that translates fragment ion peaks into peptide sequences. We demonstrate that InstaNovo outperforms state-of-the-art methods and showcase its utility in several applications. We also introduce InstaNovo+, a diffusion model that improves performance through iterative refinement of predicted sequences. Using these models, we achieve improved therapeutic sequencing coverage, discover novel peptides and detect unreported organisms in diverse datasets, thereby expanding the scope and detection rate of proteomics searches. Our models unlock opportunities across domains such as direct protein sequencing, immunopeptidomics and exploration of the dark proteome. Proteomics ; Proteome ; Computational biology ; Computer science ; Peptide ; Tandem mass spectrometry ; Scope (computer science) ; Mass spectrometry ; Bioinformatics ; Biology ; Chemistry ; Gene ; Genetics ; Biochemistry ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Genomics and Phylogenetic Studies
Authors: K. H. Hsiao | Ren‐Jei Chung | Pi-Pai Chang | Teh‐Hua Tsai
Source: Polymers (article, 2025-05-30)
Field: Chemistry — Analytical Chemistry

Identification of Hydroxyl and Polysiloxane Compounds via Infrared Absorption Spectroscopy with Targeted Noise Analysis This investigation of hydroxyl and polysiloxane absorption peaks in elastic polymer composites reveals significant spectral shifts within the fingerprint region of FTIR spectra. Using poly(vinyl butyral) (PVB) as the base polymer and poly(vinyl acetate) (PVAc) and poly(vinyl alcohol) (PVA) as reference materials, solvent effects on polymer-solvent interactions were systematically analyzed. Among the tested alcohol solvents, PEG 400 induced the most pronounced spectral changes, with the C=O stretching band shifting from 1740 to 1732 cm<sup>-1</sup> and the O-H band significantly broadening and downshifting to around 3300 cm<sup>-1</sup>, reflecting strong hydrogen-bonding interactions. Wavelet-based noise reduction effectively enhanced the signal-to-noise ratio, reducing the baseline standard deviation by over 90%. This study introduces a novel noise-enhanced FTIR recognition model that integrates baseline noise metrics to improve detection sensitivity. The model successfully uncovers subtle structural variations in polymer-solvent systems that are typically masked by conventional FTIR techniques, advancing materials analysis and providing a robust framework for future FTIR-based diagnostics and material characterization. Infrared spectroscopy ; Infrared ; Materials science ; Spectroscopy ; Absorption (acoustics) ; Analytical Chemistry (journal) ; Identification (biology) ; Absorption spectroscopy ; Photochemistry ; Organic chemistry ; Chemistry ; Optics ; Physics ; Composite material ; Astronomy Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies