jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vipra Kamboj | Brij Bhushan | Manvandra Kumar Singh | Arunima Nayak | Chandresh Kumar Rastogi | Bindu Mangla | Gopal Ji
Source: Surface Science and Technology (article, 2026-03-20)
Field: Materials Science — Materials Chemistry

Investigation on the working mechanisms of sustainable corrosion inhibitors for mild steel in acid solutions: a review based on experimental and computational analysis Corrosion of metals in diverse media degrades their functional and operational properties. After corrosion, metals are not as useful as they are in pristine conditions. Mild steel (MS) is a highly exploited metal in engineering works due to its supreme properties. Several methods have been adopted by the scientists to prevent the corrosion of MS; however, the prevention by natural products as corrosion inhibitors is more appreciated and needed due to environmental concerns. However, it is very important to know the inhibition mechanism of inhibitors before using them in prevention applications, especially in acids. This review gives a brief review of corrosion inhibitors and focuses on their working mechanism for MS in strong acids. The investigation has been done by analysing the articles published on the use of natural materials for MS’s protection in acid media. The results of weight loss measurements (WLM), electrochemical measurements (ECM), surface analysis, and computational techniques in those articles were analysed and the inhibition mechanism has been discussed. This review article provides a clear and broad view of corrosion inhibition mechanisms and also gives suggestions to enhance the performance, which could be beneficial for the scientists and researchers working in this area. Corrosion ; Mechanism (biology) ; Natural (archaeology) ; Corrosion prevention ; Human health ; Corrosion inhibitor Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: N. Punitha | A. Sivakumar | A. Parvathi Priya | M. Anitha | A. Thamizhanban
Source: Asian Journal of Chemistry (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

A Comprehensive Review on Pyrazole Derivatives as Inhibitors for Iron Protection in Acidic Media: From Molecular Interactions to Corrosion Control Corrosion in industrial systems represents a major economic and technical challenge, resulting in annual global losses amounting to several billions of dollars. To mitigate the severe economic and structural impacts of corrosion, researchers are developing efficient and environmentally acceptable inhibitors for metallic materials. Both organic and inorganic inhibitors have been extensively studied for their ability to retard metal degradation in aggressive environments through surface adsorption or passive film formation. Laboratory research had already employed various methods, together with weight loss (WL), potentiodynamic polarisation (PDP) and electrochemical impedance (EIS), to address the issue by 1995. These techniques were implemented through experiments. Atomic force spectroscopy (AFM), scanning electron microscope (SEM) and energy dispersive spectrum (EDX) also utilised as supportive evidence. A focus was placed on organic inhibitors containing heteroatoms as well as extracts/oils from plants. This comprehensive review aims to summarize the recent research status of the compounds containing pyrazole moiety as corrosion protectors for Fe metal in a hydrochloric acid (HCl) environment. There are literature data on corrosion inhibitors that contain these pyrazole derivatives that are used in acid solutions to protect steel. In addition to organic-metal ion complexes, compounds with pyrazole moieties adsorb on metallic surfaces by physical or chemical processes. In general, pyrazole molecules are capable of suppressing both anodic and cathodic corrosion. In the majority of the literature, researchers used the Gaussian 09 program to compare DFT theoretical results with experimental findings. Pyrazole ; Corrosion ; Heteroatom ; Moiety ; Adsorption ; Metal ; Molecule ; Hydrochloric acid Corrosion Behavior and Inhibition ; Synthesis of Tetrazole Derivatives ; Concrete Corrosion and Durability
Authors: Inés Pita Miguélez | Ahmad Rashed Labanieh | Soulat Damien
Source: International Journal for Numerical Methods in Biomedical Engineering (article, 2026-02-01)
Field: Materials Science — Polymers and Plastics

Hybrid Finite Element Model for Predicting the Interface Pressure of Medical Compression Stockings Accurate prediction of interface pressure is essential for optimising product design and ensuring the therapeutic effectiveness of medical compression stockings. Finite-element (FE) methods are commonly used for this purpose, allowing for the integration of specific mechanical and dimensional properties of both fabric and body. However, existing models rely on homogenisation approaches to model the fabric, neglecting its mesoscale architecture and the individual mechanical contributions of yarn systems. This study presents a hybrid FE model that combines discrete and continuous elements to represent local fabric architecture and yarn behaviour. A mesoscale unit cell couples 1-D connectors for the inlay yarn with 3-D shell elements for the loop structure, with mechanical parameters identified from uniaxial tensile tests. Two compression zones (ankle and calf) of a class-II stocking were modelled with zone-specific course densities, directly linking local architecture to macroscopic response without homogenisation. Interface pressure was predicted in several in-use configurations by simulating garment placement over rigid leg cylinders. The model was validated against Laplace's law and PicoPress sensor data. Predicted average pressures differed by 5.1%-20.5% from Laplace estimates, and by ≤ 20% from sensor measurements on 3-D-printed legs. Local pressure profiles reproduced the therapeutic gradient (ankle > calf) and remained numerically stable after mesh-ratio optimisation (8:1). The proposed framework enables precise integration of structural and mechanical parameters and represents a significant improvement over homogenised models for pressure prediction and garment design. Finite element method ; Interface (matter) ; Compression (physics) ; Pressure sensor ; Ultimate tensile strength ; Yarn Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Body Contouring and Surgery
Authors: Xirui Lv | Yiming Lei | Jinyu Shi | Jie Zhang | Jingyang Wang
Source: Journal of Advanced Ceramics (article, 2026-01-15)
Field: Materials Science — Ceramics and Composites

High-throughput screening of Ta <sub> 1− <i>x</i> </sub> Hf <sub> <i>x</i> </sub> C <sub> <i>y</i> </sub> ceramics for mechanical and oxidation performance optimization for ultra-high-temperature applications Ta<sub>1-x</sub>Hf<sub>x</sub>C<sub>y</sub> ternary ceramics are highly valued in hypersonic vehicles and a precise composition design is promising to simultaneously reduce their intrinsic brittleness and enhance protective capability during oxidation. Herein, the composition-dependent mechanical properties and oxidation behaviors of Ta<sub>1-x</sub>Hf<sub>x</sub>C<sub>y</sub> ternary ceramics over a broad composition range (x=0.22 to 0.78, y=0.66 to 1.00) are efficiently investigated through combinatorial and high-throughput experimentation to pave the way for targeted development of novel carbide candidates. Evolution trends in hardness and modulus reveal that the composition range with x=0.22 to 0.60, y= 0.80 to 1.00 is promising to reach an optimal balance between hardness and toughness, which results from competing effects between solid solution strengthening and bonding characteristic transition. High-throughput oxidation elucidates the phase constitution and compactness of oxidation products with various Ta/Hf distributions and temperatures. The accurate compositional range for the formation of a single-phase dense Hf-Ta-O compound layer shifts to a Ta-rich region (x=0.50 to 0.60) due to the preferential formation of Ta-doped HfO<sub>2</sub> and the structural characteristics of Hf-Ta-O compounds that accommodate compositional deviations. Regarding the broad compositional space of Ta<sub>1-x</sub>Hf<sub>x</sub>C<sub>y</sub>, the effects of composition on both mechanical properties and oxidation behavior are systematically investigated, providing fundamental design guidelines and an optimal composition range that holds significant promise for application in subsequent research. MAX phases ; Ceramic ; Ternary operation ; Structural material ; Brittleness ; Carbide ; Range (aeronautics) ; Composition (language) Advanced ceramic materials synthesis ; Advanced materials and composites ; Metal and Thin Film Mechanics
Authors: Qianqian Xiang | Yanping Wu | Yongfang Li | Shaoxiang Li | Xuexiu Chang
Source: Frontiers in Microbiology (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Ti3C2Tx (MXene) disrupts growth and development in Daphnia magna by suppressing related genes and inducing gut microbiome dysbiosis The potential risks of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> (MXene) nanomaterials to the ecological environment and human health have drawn increasing attention due to their widespread applications in the fields of biomedicine and environmental remediation. Although the aquatic ecotoxicity of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> has been reported, little is known about how Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> disrupts the physiological processes that regulate growth and development in zooplankton. This study investigated the toxic effects and mechanisms of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> exposure on the growth and development of <i>Daphnia magna</i> through gene expression and gut microbiome analyses. Results show that Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> exposure significantly reduced moulting frequency, body length, body width, and absolute growth rate in <i>D. magna</i>. Exposure to Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> led to a significant decrease in the expression of growth and development-related genes (<i>cyp18a1</i>, <i>ecra</i>, <i>usp, hr3</i>, and <i>cpa1</i>) in <i>D. magna</i>. Microbiome analysis revealed that exposure to Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> resulted in a decrease in Proteobacteria and an increase in Bacteroidota in the microbial community of <i>D. magna</i>. Meanwhile, Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> induced reduced abundances of <i>Pseudomonas</i> and <i>Aeromonas</i>, as well as increased abundances of <i>Bacillus</i> and <i>Phascolarctobacterium</i>. These microbial functions primarily contribute to energy acquisition and metabolism. This study indicated that Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> can inhibit the growth and development of <i>D. magna</i> by inhibiting the expression of growth and development-related genes and inducing intestinal microbial community dysbiosis. This study provides new insights into understanding the mechanisms of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> toxicity on the growth and development of zooplankton in aquatic ecosystems. Daphnia magna ; Microbiome ; Dysbiosis ; Daphnia ; Gene ; Moulting ; Proteobacteria ; Ecotoxicity ; Gene expression ; Microbial population biology MXene and MAX Phase Materials ; Advanced Nanomaterials in Catalysis ; Graphene and Nanomaterials Applications
Authors: Khaled Barakat | Michael Jordy Rojas Ruiz | Hazem Radwan Ahmed | Ehsan Shahini | Yomna Barakat
Source: Journal of Chemical Information and Modeling (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

AI-MedCraft: A Strategy-Driven AI Platform for Multi-Objective Molecular Design Small-molecule drug discovery requires balancing potency, selectivity, solubility, safety, and synthetic feasibility, among other interdependent properties. Yet most computational workflows address these properties sequentially or collapse them into fixed weighted scores. Although artificial intelligence has accelerated molecular design, many platforms struggle to handle explicit multiobjective trade-offs. Here, we present AI-MedCraft, a strategy-driven molecular design framework that applies adaptive, Pareto-guided reinforcement learning to optimize multiple objectives concurrently within a unified workflow. When structural information is available, physics-aware scoring is incorporated to support binding-competent designs. In a structure-based benchmark on the solubility rescue of the BTK inhibitor GDC-0834, AI-MedCraft achieves broader Pareto-front coverage than the scalar-reward reinforcement learning framework REINVENT 4 under matched objectives and computational cost. In a second case study, AI-MedCraft redesigns Efavirenz to retain HIV-1 reverse transcriptase engagement while reducing predicted 5-HT2A off-target binding, demonstrating its use in multiconstraint molecular optimization. Key (lock) ; Big data ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Design Education and Practice
Authors: Haihan Liu | Xiaoli Yan | Hao Fang | Hu Ge | Xuben Hou
Source: Journal of Chemical Information and Modeling (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

WeMol: A Cloud-Based and Zero-Code Platform for AI-Driven Molecular Design and Simulation Artificial intelligence (AI) has demonstrated remarkable potential in reshaping modern drug discovery, yet its widespread adoption is hindered by fragmented tools, high technical barriers, and the lack of user-friendly interfaces. Here, we present WeMol, an AI-driven one-stop molecular computing platform designed to streamline early-stage drug discovery. WeMol integrates a series of modules, covering molecular similarity search, structure-based and AI-enhanced docking, ADMET prediction, molecular generation, and molecular dynamics simulations. The platform features a zero-code, cloud-based interface that enables researchers without programming expertise to construct and execute comprehensive computational workflows. By integrating advanced AI algorithms with practical applications, WeMol lowers the entry barrier for nonexperts and provides a versatile, accessible, and reproducible solution to accelerate early drug design and discovery. Construct (python library) ; Interface (matter) ; Molecular dynamics ; Similarity (geometry) ; Drug discovery ; User interface Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Emily Zhou | Charles Zhou
Source: Open MIND (preprint, 2026-01-28)
Field: Materials Science — Materials Chemistry

Molecular Sonification: A Multi-Modal Approach for Enhanced AI in Drug Discovery Artificial Intelligence (AI) has achieved remarkable success in the molecular sciences; however, a critical constraint has emerged: prediction without mechanistic understanding. To bridge this gap, we present a multi-modal molecular AI framework based on our patented molecular sonification technology (USP 9,018,506). This approach unifies three critical applications: (1) mapping chemical structures to sound for intuitive human interpretation, (2) transforming spectroscopic data into audio streams for mechanistic AI training, and (3) encoding reaction dynamics for real-time monitoring. Critically, our method is modality-agnostic, providing a universal encoding scheme applicable to diverse systems including small molecules, protein sequences, and crystalline materials. By mapping molecular data to the human audible range, we enable high-efficiency transfer learning from pre-trained voice AI models (such as Wav2Vec 2.0), achieving greater computational efficiency compared to training from scratch. Validation on standard benchmarks demonstrates that this multi-modal spatial intelligence achieves competitive accuracy with a dramatically reduced computational footprint, offering a new paradigm for both global science education and accelerated discovery across chemistry, biology, and materials informatics. 1 Encoding (memory) ; Drug discovery ; Constraint (computer-aided design) ; Training set ; Bridge (graph theory) ; Computational model ; Data stream mining ; Scheme (mathematics) Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Emily Zhou | Charles Zhou
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-01-28)
Field: Materials Science — Materials Chemistry

Molecular Sonification: A Multi-Modal Approach for Enhanced AI in Drug Discovery Artificial Intelligence (AI) has achieved remarkable success in the molecular sciences; however, a critical constraint has emerged: prediction without mechanistic understanding. To bridge this gap, we present a multi-modal molecular AI framework based on our patented molecular sonification technology (USP 9,018,506). This approach unifies three critical applications: (1) mapping chemical structures to sound for intuitive human interpretation, (2) transforming spectroscopic data into audio streams for mechanistic AI training, and (3) encoding reaction dynamics for real-time monitoring. Critically, our method is modality-agnostic, providing a universal encoding scheme applicable to diverse systems including small molecules, protein sequences, and crystalline materials. By mapping molecular data to the human audible range, we enable high-efficiency transfer learning from pre-trained voice AI models (such as Wav2Vec 2.0), achieving greater computational efficiency compared to training from scratch. Validation on standard benchmarks demonstrates that this multi-modal spatial intelligence achieves competitive accuracy with a dramatically reduced computational footprint, offering a new paradigm for both global science education and accelerated discovery across chemistry, biology, and materials informatics. 1 Encoding (memory) ; Drug discovery ; Constraint (computer-aided design) ; Training set ; Bridge (graph theory) ; Computational model ; Data stream mining ; Scheme (mathematics) Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Masoud Amiri | Zahra Nasirinia
Source: Bioinformatics (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

HybridMolGen: A Unified Framework for Goal-Directed Molecular Generation via Multi-Objective Reinforcement Learning Supplementary data are available at Bioinformatics online. Reinforcement learning ; Benchmarking ; Probabilistic logic ; Novelty ; Component (thermodynamics) ; Molecular graph ; Benchmark (surveying) ; Graph Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Igor Peres | Evna Pereira | Cecília  R.C. Calado | Pedro Sampaio
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Chemistry — Analytical Chemistry

FT-MIR Spectroscopy Coupled with Machine Learning for the Identification of Antimicrobial Spectral Markers in Marine Drug Discovery Antimicrobial ; Drug discovery ; Identification (biology) ; Spectroscopy ; Principal component analysis ; Fourier transform infrared spectroscopy ; Support vector machine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Seaweed-derived Bioactive Compounds
Authors: Jacob Ede Levine | Yun Lyan Luo | Sai Kosaraju
Source: arXiv (Cornell University) (preprint, 2026-01-08)
Field: Materials Science — Materials Chemistry

TSSR: Two-Stage Swap-Reward-Driven Reinforcement Learning for Character-Level SMILES Generation The design of reliable, valid, and diverse molecules is fundamental to modern drug discovery, as improved molecular generation supports efficient exploration of the chemical space for potential drug candidates and reduces the cost of early design efforts. Despite these needs, current chemical language models that generate molecules as SMILES strings are vulnerable to compounding token errors: many samples are unparseable or chemically implausible, and hard constraints meant to prevent failure can restrict exploration. To address this gap, we introduce TSSR, a Two-Stage, Swap-Reward-driven reinforcement learning (RL) framework for character-level SMILES generation. Stage one rewards local token swaps that repair syntax, promoting transitions from invalid to parseable strings. Stage two provides chemistry-aware feedback from RDKit diagnostics, rewarding reductions in valence, aromaticity, and connectivity issues. The reward decomposes into interpretable terms (swap efficiency, error reduction, distance to validity), is model agnostic, and requires no task-specific labels or hand-crafted grammars. We evaluated TSSR on the MOSES benchmark using a GRU policy trained with PPO in both pure RL (P-RL) from random initialization and fine-tuning RL (F-RL) starting from a pretrained chemical language model, assessing 10,000 generated SMILES per run. In P-RL, TSSR significantly improves syntactic validity, chemical validity, and novelty. In F-RL, TSSR preserves drug-likeness and synthesizability while increasing validity and novelty. Token-level analysis shows that syntax edits and chemistry fixes act jointly to reduce RDKit detected errors. TSSR converts a sparse terminal objective into a denser and more interpretable reward, improving both syntactic and chemical quality without reducing diversity. TSSR is dataset-agnostic and can be adapted to various reinforcement learning approaches. Security token ; Initialization ; Chemical space ; Reinforcement learning ; Syntax ; Benchmark (surveying) ; Quality (philosophy) ; Reinforcement Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Jiří Navrátil | Jarret Ross | Payel Das | Youssef Mroueh | Samuel C Hoffman | Vijil Chenthamarakshan | Brian Belgodere
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

GP-MoLFormer-Sim: Test Time Molecular Optimization Through Contextual Similarity Guidance The ability to design molecules while preserving similarity to a target molecule and/or property is crucial for various applications in drug discovery, chemical design, and biology. We introduce in this paper an efficient training-free method for navigating and sampling from the molecular space with a generative Chemical Language Model (CLM), while using the molecular similarity to the target as a guide. Our method leverages the contextual representations learned from the CLM itself to estimate the molecular similarity, which is then used to adjust the autoregressive sampling strategy of the CLM. At each step of the decoding process, the method tracks the distance of the current generations from the target and updates the logits to encourage the preservation of similarity in generations. We implement the method using a recently proposed ~47M parameter SMILES-based CLM, GP-MoLFormer, and therefore refer to the method as GP-MoLFormer-Sim, which enables a test-time update of the deep generative policy to reflect the contextual similarity to a set of guide molecules. The method is further integrated into a genetic algorithm (GA) and tested on a set of standard molecular optimization benchmarks involving property optimization, molecular rediscovery, and structure-based drug design. Results show that, GP-MoLFormer-Sim, combined with GA (GP-MoLFormer-Sim+GA) outperforms existing training-free baseline methods, when the oracle remains black-box. The findings in this work are a step forward in understanding and guiding the generative mechanisms of CLMs. Similarity (geometry) ; Oracle ; Set (abstract data type) ; Property (philosophy) ; Sampling (signal processing) ; Generative model ; Generative grammar ; Test set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics