jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhiren Xiao | Jinshang Deng | Huizhu Xie | Wenjuan Hou | Yonghong Pan
Source:  (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Application of Advanced Computing and Intelligent Robotics in Autonomous Laboratory Systems: A Case Study in the Coatings Industry This article takes coating science as an example to explore the inclusion of advanced computing and intelligent robot technologies in developing a fully autonomous platform for chemical experiments. First, the cons of the old school manual laboratory workflow were reviewed, including inefficiencies, low reproducibility, and delayed data providing the groundwork for the need for automation and intelligence thereunder. Following the workflow evaluation, the formulation preparation work station is able to conduct high precision dispensing - of raw materials, weighing, and mixing, compliance to the standardized weighing protocols, the formulated coating samples would then be delivered to the fully-automated film application work station where coating thickness and application speed would be controlled and monitored; thereafter these coated samples would be sent to the fully-automated drying/curing work station, wherein purposefully-directed treatments would be provided in one or more of the means of UV, IR, or forced air, air instructed would be to reach pre-determined target temperatures; finally, the completely cured samples would be sent to fully-automated performance test work station to be characterized. Between all workstations, real-time data interactions would be allowed to prevent workflow bottlenecks while integrating the complete experimental workflow from the perspective of raw material management to final performance characterization. The intelligence and innovative science will be sustainably established on the deep technology integration of two-pronged technology foci across High-Throughput Experimentation (HTE), to achieve the generation of large-scale structured datasets, premised through parallelization and miniaturization approach, and Artificial Intelligence (AI) as the core of intelligent decision making, and the control of processes, in order to analyze, understand, and optimize formulations underlying complex data patterns. The paper further highlights the indispensable role of the Laboratory Information Management Systems (LIMS), as the digital hub of distributed systems, to ensure, among others that data integrity is preserved, to ensure that the workflow is properly coordinated, and cybersecurity is maintained. Quantitative metrics demonstrated through industrial case study data, showcase the system performance; one such performance metric is reporting as much as a 200% increase in maximum experimental throughput, with operational accuracy rates above 99% and improved costeffectiveness. Workflow ; Automation ; Robotics ; Robot ; Raw data ; Work (physics) ; Coating ; Control (management) Machine Learning in Materials Science ; Robotic Process Automation Applications ; Scientific Computing and Data Management
Authors: Tianchi Li | Qi Chen | Jia Zhou | Yandong Sun | Yan Jia | Yang Bai | Jianhua Guo | Qiuyue Zhang | Xiaojin Wang | Fengrui Yao | Zhi Cao | Wentao Wang | F. H. Liu | Taihong Yan | Weifang Zheng
Source: Nuclear Engineering and Technology (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Potential applications of artificial intelligence in spent nuclear fuel reprocessing research: A review Spent nuclear fuel reprocessing plays a pivotal role in achieving a closed nuclear fuel cycle. However, traditional research approaches face significant obstacles due to the highly radioactive environment, limited availability of experimental data, and the inherent complexity of the underlying mechanisms. Artificial intelligence (AI), with its strength in extracting intricate patterns and making predictions from sparse data, offers promising solutions to these longstanding challenges. Recent advancements have demonstrated the potential of AI in diverse domains, including image recognition, process modeling, materials discovery, process control, data analytics, and safety assessment. By addressing the limitations of conventional methods in data handling and mechanistic modeling, AI enhances system safety and operational efficiency under extreme conditions. Furthermore, it complements traditional physics-based approaches, paving the way for transformative research paradigms. As the volume of accessible data grows and algorithms continue to evolve, AI applications in spent fuel reprocessing are expected to expand—enabling fully integrated, self-optimizing control systems and proactive safety monitoring. These developments promise to significantly reshape the landscape of nuclear fuel reprocessing technologies. Spent nuclear fuel ; Process (computing) ; Nuclear fuel ; Nuclear material ; Nuclear fuel cycle ; PUREX ; Radioactive waste Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Mei Bie | Ting Wang | Zhichao Yang | Shiwei Yuan | Yinghui Gu | Chong Liu | Wei Zhao | Kai Song
Source: Sustainability (article, 2025-12-10)
Field: Materials Science — Biomaterials

Metaheuristic-Optimized Cassava Starch/CNF/SiO2 Bio-Nanocomposite Films for Sustainable Food Packaging: A Data-Driven Approach Addressing the urgent challenges of plastic pollution and food waste, this study develops a high-performance, fully biodegradable bio-nanocomposite film from renewable agricultural resources through a data-driven optimization approach. The ternary system combines cassava starch (matrix), cellulose nanofibrils (CNFs for reinforcement), and nano-silica materials (SiO2-NPs as barrier enhancer). Response Surface Methodology synergistically coupled with the Firefly Algorithm—a metaheuristic optimization technique—systematically determined the optimal formulation (1.99% w/v starch, 1.38% w/v CNF, 0.30% w/v SiO2-NPs). The optimized film achieved exceptional performance: tensile strength of 5.813 MPa, elongation at break of 12.37%, and water vapor permeability of 5.395 × 10−6 g·cm−1·s−1·Pa−1. Critically, the film demonstrated over 80% biodegradation within 60 days and superior UV-shielding capabilities (&gt;90%), effectively extending food shelf-life while minimizing environmental impact. This work establishes a robust strategy for designing sustainable packaging materials through intelligent optimization, valorizing agricultural by-products, and contributing to circular economy principles and UN Sustainable Development Goals. The integration of renewable resources with metaheuristic algorithms represents a significant advancement toward sustainable food packaging solutions. Firefly algorithm ; Renewable energy ; Renewable resource ; Sustainable development ; Environmental pollution ; Sustainable agriculture ; Food packaging ; Sustainable design Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies ; Cassava research and cyanide
Authors: Bakhtiyar Ismailov | A. S. Shambilova | Ali Can Yılmaz
Source: International Journal of Refrigeration (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Real-time AI-optimized elastocaloric cooling: Enhancing efficiency and durability in compression-mode Ni-Ti systems Coefficient of performance ; Cooling capacity ; SMA* ; Durability ; Cooling load ; Transient (computer programming) ; Water cooling ; Phase-change material Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Metal and Thin Film Mechanics
Authors: Tim Gould (1322793) | Stefan Vuckovic (2811898)
Source: Figshare (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

“Slim”Benchmark Sets for Faster MethodDevelopment The construction of large benchmark sets has accelerated advancement of quantum chemistry methods, especially in density functional theory and lower-cost methods. However, these large benchmark sets can be unsuitable for cutting-edge method development, because research codes developed for fundamentally new approaches are often inefficient and may consequently struggle to handle large molecules. Here, we introduce Slim benchmark sets that are designed to ‘summarize’ the statistics of larger (in number and size of molecules) counterparts, but have the advantage that molecules are restricted in size (to 5, 16, and 20 atoms) and may therefore be treated by inefficient implementations. Remarkably, our 16 and 20 atom Slim sets effectively summarize reactions involving much larger numbers of atoms. They thereby allow data-driven methodologies to be exploited in the early stages of cutting-edge method development. Benchmark (surveying) ; Development (topology) ; Set (abstract data type) ; Atom (system on chip) ; Quantum Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Tadakatsu Ohkubo
Source: IEEJ Transactions on Fundamentals and Materials (article, 2025-06-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Development of Permanent Magnets for the Reduction of Heavy Rare Earth Elements We have investigated the mechanism of coercivity evolution in magnets through multi-scale microstructural characterization. Based on these findings, our goal is to enhance the coercivity of heavy rare-earth-lean magnets by optimizing their microstructure. We have developed several new processes and demonstrated improved magnet properties. This review paper describes (1) coercivity enhancement by the eutectic alloy diffusion process, (2) the development of light rare earth magnets, and (3) the development of high electrical resistance magnets. Recently, there has been an increasing demand for highly efficient magnet development that not only improves properties but also meets multiple requirements. Accordingly, (4) data-driven methods are being employed in ongoing magnet development. Rare earth ; Magnet ; Reduction (mathematics) ; Materials science ; Neodymium magnet ; Metallurgy ; Electrical engineering ; Engineering ; Geometry ; Mathematics Magnetic Properties of Alloys ; Hydrogen Storage and Materials ; Advancements in Battery Materials
Authors: Heather J. Kulik
Source: ChemRxiv (preprint, 2025-03-25)
Field: Chemistry — Spectroscopy

Incorporating anionic ligands in chemical space exploration with new ligand additivity relationships Chemical space exploration motivates the development of data-driven models that bypass explicit computation or experiment. Cost-efficient strategies include the concept of additivity via the many-body expansion that treats a molecule as the sum of its parts. In the context of transition metal chemistry, ligand-wise additivity has been established as a powerful tool to infer the properties of heteroleptic transition metal complexes (TMCs) from homoleptic TMCs to excellent accuracy, including spin-splitting, orbital energies, and reaction energies. Nevertheless, this framework is incompatible with anionic ligands, because a stable homoleptic, polyanionic parent complex cannot be simulated readily. Here, I explore alternative approaches, first identifying the limits of stability of heteroleptic TMCs when successive Cl- anions are added in representative complexes formed with neutral H2O and CO ligands. I establish that expected linear relationships are preserved, albeit not as strongly as in complexes with neutral ligands. I propose data-efficient interpolation and extrapolation schemes for TMCs that achieve root mean square errors as low as 0.15–0.36 eV on HOMO/LUMO levels and gaps or ionization potentials and electron affinities and 4 kcal/mol on adiabatic spin-splitting energies for Fe(II) complexes. I show that this approach generalizes well across TMCs with 14 other 3d, 4d, and 5d metals. Finally, I extend this approach to predict properties of thousands of binary and ternary Fe(II) or Zn(II) complexes involving a single neutral ligand and up to two unique anionic ligands by leveraging a handful of calculations. I show how this interpolated space can be used to infer the limits of stable and valid complexes and to discover complexes with novel properties. Additive function ; Chemical space ; Space (punctuation) ; Ligand (biochemistry) ; Chemistry ; Stereochemistry ; Mathematics ; Computer science ; Biochemistry ; Drug discovery ; Receptor ; Mathematical analysis Analytical Chemistry and Chromatography ; Various Chemistry Research Topics ; History and advancements in chemistry
Authors: Chenwei Zheng | Thomas F. Headen | Camilla Di Mino | Daniel T. Bowron | Huanzhi Hu | Neal T. Skipper | Marta Falkowska
Source: ChemRxiv (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

The Structure of Nanoconfined THF-water Mixtures: From Local Hydrogen Bonding to Microphase Separation and Crystallization Liquid behaviour can be significantly different when confined in nanopores compared to the bulk, for example miscibility and phase transitions. A central, application-relevant, regime in confined-liquids study is water and its mixtures with organic aprotic solvents. Yet it remains unclear how nanoconfinement affects the interfacial molecular structure and properties of such solutions. Here we combine total neutron scattering with data-driven atomistic simulations to analyse the liquid structure and crystallization of tetrahydrofuran (THF)-water mixtures in hydrophilic mesoporous silicas, with cylindrical pore-diameters of ~4 and 10 nm. We focus on the low-concentration regime, 5 mol % THF, which in the bulk is fully miscible and crystallizes to form a clathrate-hydrate. Using a multiscale model refined simultaneously to small- and wide-angle scattering data, we find a shell region of tetrahydrofuran-rich domains close to the silanol surfaces, leaving a core of pure water. In spite of this dramatic microphase separation, water’s nearest-neighbour tetrahedral motif is largely undisturbed, while longer-range interactions are reshaped by confinement. On freezing the confined liquids, we observe the formation of pure ice, with the anticipated clathrate-hydrate phase being completely absent. These findings demonstrate the profound impact of nanoconfinement on the structure and crystallization of aqueous-organic solutions, with far-reaching implications for fields as diverse as green chemistry, chromatography and pharmaceuticals. Crystallization ; Miscibility ; Neutron scattering ; Spinodal decomposition ; Nanopore ; Silanol ; Molecular dynamics ; Phase (matter) ; Hydrogen bond Mesoporous Materials and Catalysis ; Zeolite Catalysis and Synthesis ; Phase Equilibria and Thermodynamics
Authors: Rose Martinus Mujur | Susanti Eka | Ginting Bersiap | Nurdin Ali | Rahmadita Nova
Source: Teknika Jurnal Sains dan Teknologi (article, 2025-11-29)
Field: Chemistry — Inorganic Chemistry

Smart diabetes care: Multi-sensor detection system with data control and 5G communication for precision healthcare services Diabetes mellitus (DM) is a non-communicable disease with a continuously increasing prevalence globally, including in Indonesia. Early detection is an important step in preventing serious complications from diabetes. This study aims to design and build an Artificial Intelligence (AI)-based diabetes detection device using image processing methods with Raspberry Pi. The test medium used is a urine strip, which is analyzed automatically through a process of dipping the strip into a urine sample by a servo motor and transferring the sample by a stepper motor. A webcam captures images of the chemical reaction on the strip, then the Raspberry Pi processes the images to detect glucose and protein levels in the urine. The test results show that all components, such as the stepper motor, servo motor, webcam, and LCD, work in an integrated manner. The system is capable of dipping, scanning, and displaying the measurement results automatically. The output data consists of glucose and protein level information displayed in quantitative numbers on the LCD screen, which can also be saved in digital format for documentation or further analysis. Thus, this device not only provides direct detection results but also has the potential to support the recording of patient examination history. Sample (material) ; Process (computing) ; Raspberry pi ; Documentation ; Test (biology) ; Stepper motor ; Control (management) ; Test data Coconut Research and Applications ; Non-Invasive Vital Sign Monitoring ; Pharmacology and Nanomedicine Research
Authors: Yecheng Leng (16265394) | Wenguang Tu (1862926) | Zhigang Zou (1465141) | Xi Zhu (551361)
Source: Figshare (article, 2025-08-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Wave–ParticleDuality in Photocatalysis: ATheoretical Study with AI Understanding the mechanism of photocatalysis has long been a challenge, particularly its implications for photothermal reactions. The role of the particle nature of light in this field is well-recognized, while the other property involved in the duality of light remains neglected. This study bridges the gap between the classical and quantum mechanical perspectives of light’s interaction with matter, unveiling the critical role of its wave properties. Through high-throughput experiments on pigment decomposition under varied temperatures and light wavelengths, combined with AI-driven analysis, we identify nonclassical kinetic behavior that deviates from the Arrhenius model. Our findings demonstrate that light’s wave properties facilitate quantum tunneling, enabling chemical reactions below conventional energy barriers. This breakthrough highlights an independent, noncoupled influence of light and heat on catalytic processes. A novel theoretical framework integrating tunneling dynamics is introduced, offering superior predictive accuracy for reaction rates across experimental and literature data. This paradigm shifts the fundamental understanding of photocatalysis, paving the way for innovative catalytic systems leveraging light’s full spectrum to enhance efficiency and selectivity. Duality (order theory) ; Quantum ; Property (philosophy) ; Decomposition ; Field (mathematics) ; Arrhenius equation ; Kinetic energy ; Quantum tunnelling Gold and Silver Nanoparticles Synthesis and Applications ; TiO2 Photocatalysis and Solar Cells ; Photochromic and Fluorescence Chemistry
Authors: Lisa M. Boatner (10009910) | Jerome Eberhardt (6311801) | Flowreen Shikwana (17027516) | Matthew Holcomb (4049725) | Peiyuan Lee (21622657) | Kendall N. Houk (1316100) | Stefano Forli (652121) | Keriann M. Backus (2174107)
Source: Figshare (article, 2025-06-30)
Field: Chemistry — Organic Chemistry

CIAA: IntegratedProteomics and Structural Modelingfor Understanding Cysteine Reactivity with Iodoacetamide Alkyne Cysteine residues play key roles in protein structure and function and can serve as targets for chemical probes and even drugs. Chemoproteomic studies have revealed that heightened cysteine reactivity toward electrophilic probes, such as iodoacetamide alkyne (IAA), is indicative of likely residue functionality. However, while the cysteine coverage of chemoproteomic studies has increased substantially, these methods still provide only a partial assessment of proteome-wide cysteine reactivity, with cysteines from low-abundance proteins and tough-to-detect peptides still largely refractory to chemoproteomic analysis. Here, we integrate cysteine chemoproteomic reactivity data sets with structure-guided computational analysis to delineate key structural features of proteins that favor elevated cysteine reactivity toward IAA. We first generated and aggregated multiple descriptors of cysteine microenvironment, including amino acid content, solvent accessibility, residue proximity, secondary structure, and predicted p<i>K</i><sub>a</sub>. We find that no single feature is sufficient to accurately predict the reactivity. Therefore, we developed the CIAA (Cysteine reactivity toward IodoAcetamide Alkyne) method, which utilizes a Random Forest model to assess cysteine reactivity by incorporating descriptors that characterize the three-dimensional (3D) structural properties of thiol microenvironments. We trained the CIAA model on existing and newly generated cysteine chemoproteomic reactivity data paired with high-resolution crystal structures from the Protein Data Bank (PDB), with cross-validation against an external data set. CIAA analysis reveals key features driving cysteine reactivity, such as backbone hydrogen bond donor atoms, and reveals still underserved needs in the area of computational predictions of cysteine reactivity, including challenges surrounding protein structure selection data set curation. Thus, our work provides a strong foundation for deploying artificial intelligence (AI) on cysteine chemoproteomic data sets. Cysteine ; Iodoacetamide ; Proteomics ; Chemoselectivity ; Protein structure ; Reactivity (psychology) ; Amino acid Click Chemistry and Applications ; Biochemical and Structural Characterization ; Chemical Synthesis and Analysis
Authors: Austin M. Mroz | Benjamin D. Egleston | James Sherwood | Ruby Morel | Kim E. Jelfs | Rebecca L. Greenaway
Source: ChemRxiv (preprint, 2025-03-11)
Field: Materials Science — Materials Chemistry

Predicting Solubility and Size-Exclusivity Towards the Rational Design of Porous Liquid Solutions Porous liquids are a sub-class of porous materials that combine permanent porosity, typically associated with solids, with the fluidity and fast mass-transfer capabilities of liquids, making them ideal candidates for gas storage and separation applications. One strategy to form porous liquids is the dissolution of discrete and permanently porous molecular species at relatively high concentrations in cavity-excluded solvents, thus introducing permanent porosity into the liquid in which it is dissolved and ensuring a solution of reasonable porosity is obtained. To access high-performance porous liquids for target applications, the selection of both the porous molecular species and the cavity-excluded solvent is key to ensuring the solvent is permanently excluded and the pore carrier is highly soluble. Finding new solvents that fit both these requirements is challenging, often resulting in a trial-and-error approach. While predictive data-driven models may be attractive, the youth of the porous liquid field currently limits the availability of data necessary to train robust models. Here, we present a computational workflow for the discovery of new porous liquid solutions combining solubility prediction software and a size-exclusivity prediction algorithm that correctly predicts size-exclusivity; this is followed by experimental validation with a representative system. Our workflow yielded size-excluded solvent and soluble porous organic cage pairs, leading to the realisation of a new porous liquid with enhanced methane uptake compared to previous systems discovered in a purely experimental high-throughput brute-force manner, highlighting the advantages of incorporating a computational workflow in the discovery of new porous liquids. Solubility ; Porosity ; Rational design ; Order (exchange) ; Process engineering ; Chemical engineering ; Materials science ; Chemistry ; Business ; Nanotechnology ; Engineering ; Composite material ; Organic chemistry Crystallization and Solubility Studies ; Process Optimization and Integration