jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Heather J. Kulik (1373331)
Source: Figshare (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Incorporating AnionicLigands in Chemical Space Explorationwith 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, and thus polyanionic, parent complex cannot be simulated readily. Here, I explore alternative approaches, first identifying the limits of stability of heteroleptic TMCs when successive Cl<sup>–</sup> anions are added in representative complexes formed with neutral H<sub>2</sub>O 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 ; Extrapolation ; Homoleptic ; Ternary operation ; Context (archaeology) ; Chemical space ; Affinities ; Adiabatic process ; Ligand (biochemistry) Machine Learning in Materials Science ; Metal-Catalyzed Oxygenation Mechanisms ; Magnetism in coordination complexes
Authors: Heather J. Kulik
Source: Journal of Chemical Information and Modeling (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

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, and thus polyanionic, parent complex cannot be simulated readily. Here, I explore alternative approaches, first identifying the limits of stability of heteroleptic TMCs when successive Cl<sup>-</sup> anions are added in representative complexes formed with neutral H<sub>2</sub>O 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. Chemical space ; Additive function ; Space (punctuation) ; Ligand (biochemistry) ; Chemistry ; Combinatorial chemistry ; Computer science ; Mathematics ; Drug discovery ; Biochemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: T. Jayakrishna1, Sandhyamadhuri Madala1*, Vainda Shivani1
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-09)
Field: Chemistry — Analytical Chemistry

A Review On Impurity Profiling Of Zidovudine & Abacavir Via Forced Degradation Study And Characterization Of Impurity By Analytical Techniques Impurity profiling ICH Q3A-Q3D and forced degradation studies ICH Q1A (R2) are critical in pharmaceutical analysis to ensure the safety, efficacy, and stability of drug substances, particularly antiviral drugs. Impurities, undesirable substances present in trace amounts, can arise during synthesis, formulation, or storage of Active Pharmaceutical Ingredients (APIs) and may exhibit toxicity or unanticipated pharmacological effects, posing significant challenges to the pharmaceutical industry. Impurity profiling involves advanced analytical techniques such as HPLC, UPLC, and LC-MS/MS, to identify, characterize, and quantify known and unknown impurities in FDA-approved antiretroviral drugs (1987–2021), including Zidovudine, Abacavir, and others. Forced degradation studies, simulating extreme conditions like hydrolysis, thermal stress, and oxidation, are essential for assessing drug stability, identifying degradation products, and developing stability-indicating methods. Despite the lack of standardized conditions for pH, temperature, or oxidant concentration, these studies provide critical insights into degradation mechanisms. Recent advances, including high-outturn UPLC, hyphenated ways like LC- NMR, and AI-driven chemometrics, enhance the perfection and effectiveness of contamination discovery. This review highlights the sources, brackets, and logical strategies for contamination operation, addressing nonsupervisory gaps, and offering robust methodologies for icing the quality of antiviral specifics. Forced degradation ; Impurity ; Abacavir ; Drug ; Zidovudine ; Active ingredient ; Profiling (computer programming) Analytical Methods in Pharmaceuticals ; HIV/AIDS drug development and treatment ; Pharmaceutical Quality and Counterfeiting
Authors: A. Karunamurthy | S. Priyanka
Source:  (article, 2025-05-27)
Field: Materials Science — Biomaterials

A Data-Driven Approach to Improving the Durability and Performance of Navol Polyzeinth Coatings This project focuses on the development and analysis of Navol Polyzeinth as a next-generation material for superior coating applications, leveraging Big Data techniques to optimize its performance. Navol Polyzeinth is engineered to enhance durability, resistance, and overall surface protection across a wide range of industrial uses. By integrating advanced data analytics, the system can examine experimental data, environmental factors, and performance outcomes to identify the key parameters influencing coating effectiveness. The project features a streamlined data processing framework that enables the precise evaluation of material behavior under varying conditions, reducing the need for extensive manual testing. The solution provides a user-friendly interface for visualizing results and gaining actionable insights into material optimization. The project is implemented using cutting-edge data analysis tools and aims to transform coating technology through data-driven innovation. Durability ; Materials science ; Composite material biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Polymer crystallization and properties
Authors: T. X. Zeng | Manqi Liu | Weitao Yang | Kunyuan Xu | Shuqin Wang | Jianping Guo
Source: Applied Physics Letters (article, 2025-08-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Realizing weakly supervised learning by physical information for rapidly and accurately predicting three-dimensional weak electromagnetic field of metallic metasurfaces and zero-shot inverse design This work introduces a separable, complex-valued 3D Maxwell loss function to physically constrain a 3D U-Net. This framework enables the prediction of complete vectorial electromagnetic fields for metallic metasurfaces across a broad spectral range, achieving a computational speedup exceeding two orders of magnitude (&amp;gt;100×) relative to the FDTD. A recognized limitation of purely data-driven networks is their inability to accurately predict weak field regions, which can even produce negative spectrum correlations. Incorporating Maxwell loss ensures accurate weak field predictions for absorptive materials. Additionally, this constraint allows effective weak supervision, reducing labeled points needed to just 12.5% of the total field points. More importantly, we have achieved zero-shot inverse design of meta-material structures for arbitrary materials and arbitrary geometries in three dimensions. Electromagnetic field ; Inverse ; Speedup ; Field (mathematics) ; Constraint (computer-aided design) ; Range (aeronautics) ; Inverse problem ; Separable space ; Physics ; Function (biology) ; Computer science ; Statistical physics ; Computational physics ; Mathematics ; Mathematical analysis ; Geometry ; Quantum mechanics ; Materials science Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Sonia Bhandari | Aarti Bhandari
Source: Solid State Communications (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Machine learning prediction of dielectric constant in NBT–KBT composites via frequency–temperature response Dielectric ; Capacitor ; Polarization (electrochemistry) ; Random forest ; Ceramic ; Relaxation (psychology) ; Dielectric spectroscopy ; Work (physics) ; Phase transition ; Random phase approximation Ferroelectric and Piezoelectric Materials ; Dielectric materials and actuators ; Dielectric properties of ceramics
Authors: Mohammad Forhad Hossain | Kazi Sirajul Islam | Mohsin Ahmed | Nasrin Sultana
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Determining the Consumption Coefficient of Raw Materials, Processing Chemicals, and Auxiliaries of Cotton, Polyester, and Blend (P/C) Based Knit Fabric in Industries in Bangladesh Polyester ; Raw material ; Consumption (sociology) ; Pulp and paper industry ; Materials science ; Polymer science ; Engineering ; Composite material ; Chemistry ; Organic chemistry ; Art Textile materials and evaluations
Authors: Mohammad Forhad Hossain | Kazi Sirajul Islam | Nasrin Sultana | Mohsin Ahmed
Source: Preprints.org (preprint, 2025-11-24)
Field: Materials Science — Polymers and Plastics

Consumption Coefficient of Raw Materials, Processing Chemicals, and Auxiliaries of Cotton, Polyester, and Poly-Cotton Based Knit Fabrics in Industries in Bangladesh: Determining the Degree of Sustainability Bangladesh&amp;#039;s textile sector, a major producer of knitwear, faces growing economic and environmental challenges due to its heavy reliance on chemicals and water. This study investigates chemical consumption during three key wet processing stages—pretreatment, dyeing, and finishing—across 15 knit factories processing cotton, polycotton, and polyester fabrics. The findings reveal significant variability in chemical usage among factories, with no consistent trends in quantity used. In some cases, the highest usage levels were several times greater than the lowest, highlighting inconsistency. These differences may be influenced by fabric type, variation in chemical types and grades, water quality, machinery condition, operational practices, buyer requirements, and the lack of standardized processes or sustainability strategies. Additionally, a persistent adherence to legacy practices and limited monitoring contribute to inefficiencies. The study underscores the pressing need for unified chemical management practices, process optimization, and transparent reporting to improve environmental outcomes. Policymakers and industry stakeholders can use these insights to promote more sustainable production practices within the textile sector. Establishing clear benchmarks for chemical use and encouraging continuous improvement efforts will be crucial in addressing the sector&amp;#039;s environmental footprint. This research contributes to the limited body of data-driven analysis on chemical use in the knitwear industry of Bangladesh and provides actionable guidance for fostering sustainability in textile wet process. Sustainability ; Consumption (sociology) ; Textile ; Production (economics) ; Textile industry ; Raw material ; Chemical industry ; Process (computing) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Microplastics and Plastic Pollution
Authors: Ербол Бактугулов
Source: Батыс Қазақстан инновациялық-технологиялық университетінің Хабаршысы (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

СОВРЕМЕННЫЕ МЕТОДЫ БОРЬБЫ С КОРРОЗИЕЙ НЕФТЕГАЗОВОГО ОБОРУДОВАНИЯ: НА СТРАЖЕ НАДЕЖНОСТИ И ДОЛГОВЕЧНОСТИ The article discusses modern methods and approaches to protecting oil and gas equipment from corrosion, one of the most serious threats to its reliability and durability. Corrosion in the oil and gas industry is a destructive process that can lead to accidents, leaks and significant financial losses. The authors emphasize that combating this phenomenon requires an integrated and multifactorial approach, including both preventive measures and modern technologies. The oil and gas industry faces the problem of corrosion, which poses a serious threat to the reliability and durability of equipment. The article discusses modern approaches to protecting oil and gas equipment from corrosion, analyzing the influence of various factors on the rate of corrosion processes and the consequences that may arise as a result. The text covers key aspects of corrosion in the oil and gas industry, including: Corrosion hazards: leaks, accidents, increased operating costs and safety hazards. Modern protection strategies: corrosion inhibitors, protective coatings, cathodic protection, selection of corrosion-resistant materials and condition monitoring. Innovative developments: smart and biodegradable inhibitors, nanomaterials, automated monitoring systems and engineering solutions to optimize operating conditions. The article highlights the importance of an integrated approach combining different protection methods and the need for regular monitoring to minimize corrosive threats. The issues of biocorrosion and the influence of microorganisms on corrosion processes are also considered. Future research should pay attention to the development of technologies, including "smart" materials and systems based on artificial intelligence, which can significantly increase the effectiveness of corrosion protection. The integration of new technologies and traditional corrosion control methods in the real conditions of the oil and gas industry can improve the reliability and safety of equipment. Corrosion ; Reliability (semiconductor) ; Cathodic protection ; Process (computing) ; Durability ; Petroleum industry ; Corrosion monitoring ; Fossil fuel Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Corrosion Behavior and Inhibition
Authors: Shih-Hung Cheng | Yi-Ruei Syu | Er-Feng Hsieh | Wen-Jeng Hsueh
Source: Journal of Alloys and Compounds (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

High-performance gate-tunable spin valves based on transition metal dichalcogenides in the presence of phonon scattering Reading (process) ; Spintronics ; Giant magnetoresistance ; Spin valve ; Magnetoresistance ; Quantum tunnelling ; Transition metal ; Spin (aerodynamics) ; Scattering 2D Materials and Applications ; Magnetic properties of thin films ; Advanced Memory and Neural Computing
Authors: Yash Dixit | Marlon M. Reis
Source: Advances in food and nutrition research (review, 2025-01-01)
Field: Chemistry — Analytical Chemistry

From farm to fork: Spectroscopy in meat quality and safety assurance Quality assurance ; Fork (system call) ; Spectroscopy ; Business ; Food science ; Environmental science ; Engineering ; Biology ; Physics ; Operations management ; Mechanical engineering ; External quality assessment Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Meat and Animal Product Quality
Authors: Y. P. Wang | Hsing-Yu Liu | Vito Lin | Andrew H. Kang
Source:  (article, 2025-10-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

RDL Structure Design of Large Size FOMCM Abstract The rapid development of high-performance computing (HPC) and artificial intelligence (AI) applications, Fan-Out Multi-Chip-Module (FOMCM) is one of the advanced heterogeneous integration platforms to achieve the requirements. However, the large package size and increment of RDL layer enhance the risk of RDL crack during thermal cycling TCG (−40°C to 125°C) reliability test. This study aims to demonstrate the wafer form warpage and a series design of RDL to improve the challenge of stress and apply the design to the multi-layer RDL FOMCM next generation. A 90 x 100 mm2 large size FOMCM test vehicle with 6/7/8 layers RDL is adopted in this study. Finite element method (FEM) simulation is carried out to evaluate the stress of RDL. It is found that the concentrated copper column results in maximum bending stress of RDL. The in-process wafer form warpages are simulated after underfilling, after molding, de-carrier, C4 implantation and after grinding. In addition, geometry and material optimizations of wafer form structure will be discussed, too. For RDL stress reduction, the simulation results indicate that a series method of RDL designs combined with discontinuous copper plane are able to reduce the stress significantly. The newly proposed RDL designs are well demonstrated in this study and these designs will be utilized for the more advanced FOMCM product in the near future. Wafer ; Stress (linguistics) ; Finite element method ; Temperature cycling ; Reliability (semiconductor) ; Bending ; Series (stratigraphy) ; Layer (electronics) Copper Interconnects and Reliability ; Electronic Packaging and Soldering Technologies ; 3D IC and TSV technologies
Authors: Tingxiong Xiao | Xinxin Song | Ziqian Wang | Boyang Zhang | Jinli Suo
Source: ArXiv.org (preprint, 2025-09-09)
Field: Materials Science — Materials Chemistry

A Unified Data-Driven Framework for Efficient Scientific Discovery Scientific discovery drives progress across disciplines, from fundamental physics to industrial applications. However, identifying physical laws automatically from gathered datasets requires identifying the structure and parameters of the formula underlying the data, which involves navigating a vast search space and consuming substantial computational resources. To address these issues, we build on the Buckingham $Π$ theorem and Taylor's theorem to create a unified representation of diverse formulas, which introduces latent variables to form a two-stage structure. To minimize the search space, we initially focus on determining the structure of the latent formula, including the relevant contributing inputs, the count of latent variables, and their interconnections. Following this, the process of parameter identification is expedited by enforcing dimensional constraints for physical relevance, favoring simplicity in the formulas, and employing strategic optimization techniques. Any overly complex outcomes are refined using symbolic regression for a compact form. These general strategic techniques drastically reduce search iterations from hundreds of millions to just tens, significantly enhancing the efficiency of data-driven formula discovery. We performed comprehensive validation to demonstrate FIND's effectiveness in discovering physical laws, dimensionless numbers, partial differential equations, and uniform critical system parameters across various fields, including astronomy, physics, chemistry, and electronics. The excellent performances across 11 distinct datasets position FIND as a powerful and versatile tool for advancing data-driven scientific discovery in multiple domains. Identification (biology) ; Representation (politics) ; Process (computing) ; Focus (optics) ; Simplicity ; Position (finance) ; Physical law ; Scientific discovery Machine Learning in Materials Science ; Big Data and Digital Economy ; Computational Physics and Python Applications
Authors: Varanavasi Nallasamy | Malarvizhi Seshiah
Source:  (article, 2025-08-06)
Field: Chemistry — Spectroscopy

Kernel Regressive Shuffling Shepherd Quantum Artificial Intelligence for Protein Structure Prediction Protein structure identification is the process of finding the protein structure from the amino acid sequence in biomedical field. Protein structure prediction helps to learn how the protein interacts with other molecules and practitioners for finding the treatment process. Many researchers carried out their research on different protein structure prediction methods. But the accuracy level was not improved, and time complexity and root mean square error was not reduced. To address these issues, a new method called Kernel Regressive Shuffling Shepherd Quantum Artificial Intelligence (KRSSQAI) method is introduced. The quantitatively analyzed results demonstrate that the proposed method achieves higher protein structure prediction accuracy with reduced time consumption compared to existing methods. Protein structure prediction ; Shuffling ; Kernel (algebra) ; Protein structure ; Sequence (biology) ; Protein sequencing ; Pattern recognition (psychology) Molecular spectroscopy and chirality ; Machine Learning in Bioinformatics ; Protein Structure and Dynamics
Authors: Haokun Ti | Yang Fu | Yujing Yue | Shuhan Jiang | Rongrong Zhu | Haiqing Zhao | Yang He | Li Shi
Source: Chemistry Letters (article, 2025-11-01)
Field: Chemistry — Inorganic Chemistry

Data-driven discovery of MOFs for benzene adsorption with transferable stacking model Abstract In this work, a regression framework based on multi-model stacking was constructed using the CoRE-MOF database, validated for transferability, and applied for large-scale prediction on the h-MOF library. The model exhibited strong generalization (R2 = 0.943) and effectively identified high-performance candidates, with the top 30 predicted MOFs showing superior adsorption capacities in GCMC simulations compared with synthesized CoRE-MOF structures. This screening strategy provides an efficient tool for discovering high-performance MOFs and a data-driven perspective for adsorption material design. Stacking ; Adsorption ; Benzene ; Generalization ; Benzene derivatives ; Metal-organic framework Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Catalysis and Hydrodesulfurization Studies