jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Konstantin S. Jakob | Aron Walsh | Karsten Reuter | Johannes T. Margraf
Source: Advanced Materials (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery Recent computational materials discovery efforts have led to an enormous number of predictions of previously unknown, potentially stable inorganic, crystalline compounds. In particular, both high-throughput screenings and generative models have benefited tremendously from recent advances in computational resources and available data. However, these efforts are currently limited to predicting pristine crystalline materials. As a consequence, many of these predictions cannot be realized in experiments, where kinetic effects, defects, and crystallographic disorder can be crucial. To address this shortcoming, the current work aims to introduce disorder into computational materials discovery with machine learning (ML) based classification models. Trained on the inorganic crystal structure database (ICSD), these classifiers capture the chemical trends of crystallographic disorder and estimate the prevalence of disorder in computational databases produced by the Materials Project or Graph Networks for Materials Science (GNoME) initiatives. This opens the door toward disorder-aware computational materials discovery workflows, bridging the gap between prediction and experiment. Bridging (networking) ; Generative grammar ; Crystal structure prediction ; Computational model ; Generative model ; Materials informatics ; Crystal structure Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Qi Xiao | Wei Zhai | Chen‐Xia Hu | Yong Huo | Xiangyang Li | Qiang Wang | Hua Zhang | Hao‐Li Zhang
Source: Advanced Functional Materials (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Liquid‐Phase Exfoliation of Non‐Van Der Waals Crystals Into Nanoflakes for Energy Applications Abstract Recently, 2D materials have emerged as a focal point in materials science research. Conventional 2D systems predominantly derive from layered van der Waals (vdW) crystals, where individual atomic planes are held together by weak interlayer interactions. However, groundbreaking developments have challenged this paradigm through the successful isolation of 2D materials from non‐van der Waals (non‐vdW) bulk crystals. Non‐vdW 2D materials resemble their vdW counterparts in atomically thin sheets with strong in‐plane covalent/ionic bonding but manifest distinguished structural characteristics, including large lattice distortions, abundant dangling bonds, and coordinatively unsaturated surface atoms. These intrinsic features endow them with enhanced surface reactivity and dynamic electronic states, which promote chemisorption of reactive species and accelerate interfacial charge transfer kinetics‐properties that are highly advantageous for energy applications. Nevertheless, the absence of weak interlayer vdW forces poses significant challenges in exfoliation processes, with fundamental mechanisms remaining poorly understood. This review systematically examines state‐of‐the‐art liquid‐phase exfoliation (LPE) methodologies for non‐vdW nanoflakes synthesis, critically analyzing their mechanistic foundations, process‐structure‐property relationships, and performance benchmarks in energy‐related technologies. Furthermore, key challenges are identified in improving nanoflakes quality, precise kinetic control, and advancing next‐generation artificial intelligence (AI) and smart energy systems, while proposing interdisciplinary strategies to advance this burgeoning field. van der Waals force ; Materials science ; Chemical physics ; Dangling bond ; Nanotechnology ; Exfoliation joint ; Graphene ; Ionic bonding ; Molecule ; Chemistry Graphene research and applications ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Mohamed M. H. Desoky | Gjylije Hoti | Arshak A. Tsaturyan | Claudio Cecone | Fabrizio Caldera | Francesco Trotta
Source: Green Chemistry (article, 2025-01-01)
Field: Materials Science — Biomaterials

Green synthesis of scalable non-soluble hydrogels: rapid transesterification of maltodextrin with dimethylcarbonate using DABCO/DMSO Green, scalable synthesis of maltodextrin hydrogels via DABCO/DMSO-catalyzed transesterification with dimethyl carbonate (∼80% yield). The hydrogels absorb 549% water, supporting sustainable materials. Maltodextrin ; Transesterification ; DABCO ; Self-healing hydrogels ; Dimethyl carbonate ; Chemistry ; Yield (engineering) ; Organic chemistry ; Chemical engineering ; Materials science ; Catalysis ; Composite material biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Hydrogels: synthesis, properties, applications
Authors: Chaoyi Li | Jingyuan Liao | Yuxiang Zhu | Jingyuan Zhu | Zhiqiang Gan | Renrong Zhang | Wei Zhao | Junrong Huang | Yinghe Zhang | Hengzhi You
Source: Cell Reports Physical Science (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Applying pretrained molecular representation for reaction prediction and efficient catalyst screening <h2>Summary</h2> Data-driven methods for catalyst screening and reaction optimization offer promising approaches to accelerate catalyst design. However, prior research in the chemical domain has been hindered by data scarcity, complex descriptor design, and high calculation cost. Here, we present a Uni-Mol-based framework that leverages pretraining on molecular conformations for rapid molecular representation and integrates machine learning for reaction prediction. This method is validated on two high-throughput experimentation reaction datasets and two asymmetric reaction datasets, showing performance comparable to property-based molecular descriptors. To further demonstrate efficiency, this approach was applied to a self-synthesized tetrapeptide library in asymmetric aldol reactions under high-throughput experiment (HTE) conditions. The classification model identified tetrapeptide catalysts with high yield and enantioselectivity, achieving up to 94% yield and 99% enantiomeric excess, while also predicting several unreported catalysts that were experimentally confirmed. This versatile prediction strategy is adaptable to diverse reaction scenarios and may inspire future advances in reaction prediction. Tetrapeptide ; Yield (engineering) ; Catalysis ; Representation (politics) ; Molecular descriptor ; Reaction conditions ; Interpretability ; Domain (mathematical analysis) Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Microbial Natural Products and Biosynthesis
Authors: Mukesh Shrivastava | G. Swarnabala | S. B. Khatavkar | G. S. Sadana | Anahita Bharadwaj
Source: Asian Journal of Chemical Sciences (article, 2025-04-09)
Field: Chemistry — Organic Chemistry

Synthesis and Characterization of Some Oximino Metal Complexes Using Magnetic and Spectral Data Predicting Anticipated Geometry This study presents the synthesis and characterization of four organic ligands: malon-di-anilide oxime (HINMAO), malon-di-(p-toluidide) oxime (HINM-p-TO), malon-di-(o-anisidide) oxime (HINM-o-ANISO), and malon-di-(p-anisidide) oxime (HINM-p-ANISO). Their molecular formulas were confirmed through elemental analysis, infrared (IR), and electronic spectral studies. These ligands were used to prepare solid-state metal complexes with Cu (II) acetate, Cu (II) nitrate, UO₂(II) nitrate, Mn (II) chloride, and Pb (II) acetate, which were further characterized for their structural and magnetic properties. The Cu (II) complexes exhibited subnormal magnetic moments following the Bleaney-Bowers equation, with ESR spectra (g = 2.015 – 2.150) confirming square planar geometry and oxygen-bridged dimeric structures. The UO₂(II) complexes displayed low magnetic moments due to ligand field effects and adopted pentagonal bipyramidal geometry, as confirmed by electronic and infrared spectral analysis. The Mn (II) complexes exhibited magnetic moments of 5.8–5.9 BM, indicative of tetrahedral coordination. The Pb (II) complexes were found to be diamagnetic, showing a single electronic absorption band at ~400 nm, suggesting a tetrahedral structure. All synthesized metal complexes were insoluble in water and most organic solvents, suggesting their polymeric nature. These findings provide valuable insights into the coordination chemistry of these novel ligands and their potential applications in materials science and catalysis. Characterization (materials science) ; Geometry ; Metal ; Geology ; Materials science ; Mineralogy ; Metallurgy ; Nanotechnology ; Mathematics Inorganic and Organometallic Chemistry ; Computational Drug Discovery Methods
Authors: Fakher Hcini | Mussad Mohammed Alzahrani | Sobhi Hcini | M. L. Bouazizi | K. Khirouni
Source: Preprints.org (preprint, 2025-01-08)
Field: Materials Science — Materials Chemistry

Structural, Elastic, Refractive Index and Energy Band Gap Properties of Li&lt;sub&gt;0.5&lt;/sub&gt;Co&lt;sub&gt;0.5&lt;/sub&gt;FeCrO&lt;sub&gt;4&lt;/sub&gt; Ferrite for Non-Linear Optical and Optoelectronic Applications This work examines the structural, elastic, and optical properties of the Li0.5Co0.5FeCrO4 spinel ferrite. Rietveld analysis of X-ray diffraction measurement confirms its cubic spinel structure. The structural investigation reveals strong consistency among calculated and refined parameters, validating the proposed cation distribution. Elastic moduli, including bulk, longitudinal, and rigidity moduli, were acquired by computing stiffness constants. Absorbance data and the Tauc method indicated a direct optical transition with a low band-gap energy, suggesting the sample's potential for optoelectronic applications. The Li0.5Co0.5FeCrO4 spinel showed absolutely low Urbach energy, suggesting little disorder and defects within its structure. The theoretical optical characteristics like reflection loss (RL), polaron radius (Rp), molar refractive index (Rm), molar electronic polarizability (&amp;alpha;m), and the non-linear optical features were examined for our compound. Our findings indicate that the Li0.5Co0.5FeCrO4 compound outperforms the undoped Li0.5Fe2.5O4 compound , featuring a lower band gap. This implies that adding Co and Cr into Li0.5Fe2.5O4 ferrite enhances their optical and optoelectronic applications. Refractive index ; Band gap ; Materials science ; Energy (signal processing) ; Physics ; Atomic physics ; Chemistry ; Optics ; Condensed matter physics ; Quantum mechanics Magnetic Properties and Synthesis of Ferrites ; Pigment Synthesis and Properties
Authors: Ankur K. Gupta | C. Hetherington | Wibe A. de Jong
Source: Digital Discovery (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Toward accelerating rare-earth metal extraction using equivariant neural networks A high-throughput workflow leveraging equivariant GNNs and a diverse dataset of rare-earth complexes to predict binding affinities and accelerate critical metal separation. Equivariant map ; Artificial neural network ; Stability (learning theory) ; Pattern recognition (psychology) ; Workflow ; Extraction (chemistry) Machine Learning in Materials Science ; Extraction and Separation Processes ; Computational Drug Discovery Methods
Authors: Dalia A. Elsherbiny | Noha Omer | Fahad Abdulaziz | Abdulaziz M. Alanazi
Source: Research Square (preprint, 2025-02-19)
Field: Materials Science — Materials Chemistry

Biobased Tricomponent (carboxymethyl chitosan/ cerium dioxide/iron III oxide) nanocomposite flakes for multifarious environmental decontaminations: magnetically separable and antimicrobial photocatalyst Chitosan ; Nanocomposite ; Cerium oxide ; Cerium ; Photocatalysis ; Antimicrobial ; Materials science ; Chemical engineering ; Oxide ; Chemistry ; Nuclear chemistry ; Nanotechnology ; Inorganic chemistry ; Organic chemistry ; Metallurgy ; Catalysis Advanced Nanomaterials in Catalysis ; Advanced Photocatalysis Techniques ; Nanoparticles: synthesis and applications
Authors: Yujia Cheng | Guang Yu
Source: Molecules (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Power Frequency Breakdown Properties of LDPE-Doped Inorganic Nanoparticles Although polyethylene is widely used in electrical insulation, it does not possess dielectric properties. It is therefore desirable to develop insulation materials with excellent dielectric properties. In this study, low-density polyethylene (LDPE) was used as a matrix resin, while MgO, wollastonite, and montmorillonite (MMT) were employed as inorganic nano-additives. Three composites were prepared using the boiling-melt blending approach. Power frequency breakdown tests were performed on the original LDPE and on the prepared nanoparticle/LDPE composites. Upon combination with the Weibull distribution, the breakdown test results revealed that the addition of these nano-additive particles to the LDPE matrix increased the breakdown field strength of the material. The highest breakdown field strength for the nano-MgO/LDPE composite was obtained using a MgO loading of 0.5%. Notably, the obtained value was 1.8% higher than that of the pure LDPE. In addition, the highest breakdown field strength for the nano-wollastonite/LDPE composite was obtained using a wollastonite loading of 1% (7.48% higher than that of pure LDPE). Similarly, the highest breakdown field strength of the nano-MMT/LDPE composite was obtained using an MMT loading of 3%, giving a value that was 6.67% higher than that of the pure LDPE. Low-density polyethylene ; Materials science ; Composite material ; Polyethylene ; Wollastonite ; Dielectric ; Dielectric strength ; Chemistry High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Electromagnetic wave absorption materials
Authors: Elaheh Valizadehkakhki | Farrokhzad Mohammadi Zonoz | Amir Sh. Saljooghi | Maryam Moghaddam Matin | Zahra Sadat Ahmadi Koopaee
Source: Cellular Molecular and Biomedical Reports (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Toxicity comparison of two nanocarriers, magnetic mesoporous silica and polyoxometalate, using in vitro methods in PC-3 cells Nanocarriers have garnered significant global attention in pharmaceutical research due to their potential to revolutionize drug transportation and formulation. These systems enhance therapeutic efficacy, minimize off-target effects, and enable controlled release, making the identification of safer, more efficient nano-delivery platforms critical for advancing in vitro and clinical applications. In this study, we investigated the cytotoxicity of two novel drug delivery systems—superparamagnetic iron oxide nanoparticles coated with silica (SPION@silica) and iron-containing polyoxometalate (FeMo6)—against prostate cancer (PC-3) and normal Chinese hamster ovary (CHO) cell lines. SPION@silica and FeMo6 were synthesized via co-precipitation and solvothermal methods, respectively, yielding uniform particle sizes of 20 nm and 50 nm, as confirmed by transmission electron microscopy (TEM). Structural and compositional integrity were validated using Fourier-transform infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). Cytotoxicity assessments via MTT assay revealed distinct profiles for the two systems. FeMo6 demonstrated potent tumor-killing activity, reducing PC-3 cell viability to 35% at 48 hours post-treatment, compared to 65% viability with SPION@silica. However, FeMo6 also exhibited moderate cytotoxicity toward CHO cells (55% viability), suggesting non-selective mechanisms such as reactive oxygen species generation or mitochondrial disruption. In contrast, SPION@silica showed minimal toxicity to healthy cells (85% CHO viability), attributed to the biocompatible silica shell shielding the magnetic core. These findings highlight a critical trade-off: while FeMo6’s polyoxometalate structure enables robust anticancer activity, its broader cytotoxicity necessitates caution in therapeutic applications. SPION@silica, with its favorable safety profile, emerges as a promising candidate for targeted drug delivery, though its lower tumor suppression efficacy warrants further optimization. Nanocarriers ; Polyoxometalate ; Mesoporous silica ; Mesoporous material ; Toxicity ; Nanotechnology ; In vitro ; Chemistry ; Materials science ; Nanoparticle ; Biochemistry ; Organic chemistry ; Catalysis Polyoxometalates: Synthesis and Applications ; biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials
Authors: Yujie Yan | Jingwei Li | Yue Yu | Zhongyang Ren | Yue Huang | Zhanming Li
Source: Food Science and Human Wellness (article, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Machine learning-driven optical nanomaterials typical applications in food quality assessment: Present obstacles and future horizons Complex and variable food systems contain numerous interfering substances that produce intricate analytical signals within the relevant matrices. Analytical detection utilizing optical nanosensing often generates an extensive array of data points, characterized by high dimensionality or complex imaging maps. Consequently, the extraction of meaningful data from these vast datasets has emerged as a significant challenge. The integration of machine learning with optical nanomaterials has exhibited remarkable predictive capabilities and accuracy in the processing, analysis, and extraction of valuable information from large and complex food-related datasets. This review systematically elucidates the synergistic relationship between optical nanosensing and machine learning, encompassing: (1) the signal transduction mechanisms and data acquisition utilizing optical nanomaterials; (2) the design and optimization of nanomaterials driven by machine learning; (3) the applications of machine learning algorithms for food data processing and predictive modeling; and (4) the advancements in collaborative applications of machine learning and optical nanomaterials in food quality analysis. Building on this foundation, the paper addresses the challenges currently confronting research on the application of machine learning-driven optical nanomaterials in food quality analysis, constructs a research framework, and outlines potential directions for future exploration. This review offers novel insights into the integrated application of optical nanomaterials and machine learning, presents innovative solutions for the rapid and real-time detection of food quality, and significantly enhances the depth of research into food quality assessment. New horizons ; Nanomaterials ; Quality (philosophy) ; Nanotechnology ; Computer science ; Environmental science ; Materials science ; Engineering ; Physics ; Aerospace engineering Dye analysis and toxicity ; Advanced Nanomaterials in Catalysis
Authors: W. B. Li | Xavier F. Cadet | David Medina-Ortiz | Mehdi D. Davari | Ramanathan Sowdhamini | Cédric Damour | Li Yu | Alain Miranville | Frédéric Cadet
Source: arXiv (Cornell University) (preprint, 2025-01-05)
Field: Chemistry — Physical and Theoretical Chemistry

From thermodynamics to protein design: Diffusion models for biomolecule generation towards autonomous protein engineering Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models and Score-based Generative Models, where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance on the E(3) group can keep the physical stability of the frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation. Biomolecule ; Diffusion ; Thermodynamics ; Protein engineering ; Chemistry ; Statistical physics ; Materials science ; Nanotechnology ; Physics ; Biochemistry thermodynamics and calorimetric analyses ; Monoclonal and Polyclonal Antibodies Research ; Microbial Metabolic Engineering and Bioproduction
Authors: Valter Costa | José Manuel Oliveira | Patrícia Ramos
Source: Preprints.org (preprint, 2025-01-28)
Field: Materials Science — Materials Chemistry

Deep Learning-Driven Integration of Multimodal Data for Material Property Predictions This study investigates the integration of deep learning for single-modality and multimodal data within materials science. Traditional methods for materials discovery are often resource-intensive and slow, prompting the exploration of machine learning to streamline the prediction of material properties. While single-modality models have been effective, they often miss the complexities inherent in material data. The paper explores multimodal data integration—combining text, images, and tabular data—and demonstrates its potential to improve predictive accuracy. Utilizing the Alexandria dataset, the research introduces a custom methodology involving multimodal data creation, model tuning with AutoGluon framework, and evaluation through targeted fusion techniques. Results reveal that multimodal approaches enhance predictive accuracy and efficiency, particularly when text and image data are integrated. However, challenges remain in predicting complex features like band gaps. Future directions include incorporating new data types and refining specialized models to improve materials discovery and innovation. Computer science ; Artificial intelligence ; Deep learning ; Modality (human–computer interaction) ; Machine learning ; Property (philosophy) ; Data integration ; Sensor fusion ; Data science ; Data mining Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Corrosion Behavior and Inhibition
Authors: Yangxin Fan | Yinghui Wu | Roger H. French | Danny Pérez | Michael G. Taylor | Ping Yang
Source: Digital Discovery (article, 2025-12-15)
Field: Materials Science — Materials Chemistry

Hierarchical attention graph learning with LLM enhancement for molecular solubility prediction Solubility quantifies the concentration of a molecule that can dissolve in a given solvent. Graph ; Molecular graph ; Solubility ; Pattern recognition (psychology) ; Graph theory Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallization and Solubility Studies