jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: MINTESENOT ADMASU ESHETU | Alexander S. Novikov
Source: International Journal of Scientific Development and Research (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

APPLICATION OF MACHINE LEARINING AND COMPUTER MODELING IN METALLURGY COMPHERENSIVE REVIEW Machine learning (ML) and computational modeling have transformed metallurgy into a predictive and data-driven field with accelerated alloy design, optimized processing pathways, and improved microstructural properties. This review discusses the emerging trends in atomistic, mesoscale, and continuum modeling studies, such as Density Functional Theory, molecular dynamics, Monte Carlo simulations, phase-field modeling, crystal plasticity, and finite element analysis. More physics-based models are being complemented by ML models, such as classical statistical learning, deep neural networks, and physics-informed neural networks (PINNs). The combination of models has made it possible enabled significant advancements in designing inverse alloys, predicting microstructures, characterizing defects, and controlling processes in real time in casting, thermomechanical processing, heat treatment, and additive manufacturing. Based on a wide body of peer-reviewed literature, this review critically evaluates the strengths, limitations, and strategies for integrating ML into metallurgical systems. High-performance aluminum alloys, energy-relevant materials, and new ideas for digital twins have been emphasized. Major challenges, such as data quality, multiscale coupling, quantification of uncertainty, and interpretability of the model, are addressed, and future research directions based on autonomous and mechanism-aware metallurgical systems are described. Interpretability ; Artificial neural network ; Field (mathematics) ; Multiscale modeling ; Finite element method ; Monte Carlo method ; Experimental data ; Inverse problem Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Model Reduction and Neural Networks
Authors: Feiyang Cai | Guijuan He | Yi Hu | Jingjing Wang | Joshua Luo | Tianyu Zhu | Srikanth Pilla | Gang Li | Ling Liu | Feng Luo
Source: arXiv (Cornell University) (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

A Large-Scale Dataset for Molecular Structure-Language Description via a Rule-Regularized Method Molecular function is largely determined by structure. Accurately aligning molecular structure with natural language is therefore essential for enabling large language models (LLMs) to reason about downstream chemical tasks. However, the substantial cost of human annotation makes it infeasible to construct large-scale, high-quality datasets of structure-grounded descriptions. In this work, we propose a fully automated annotation framework for generating precise molecular structure descriptions at scale. Our approach builds upon and extends a rule-based chemical nomenclature parser to interpret IUPAC names and construct enriched, structured XML metadata that explicitly encodes molecular structure. This metadata is then used to guide LLMs in producing accurate natural-language descriptions. Using this framework, we curate a large-scale dataset of approximately $163$k molecule-description pairs. A rigorous validation protocol combining LLM-based and expert human evaluation on a subset of $2,000$ molecules demonstrates a high description precision of $98.6\%$. The resulting dataset provides a reliable foundation for future molecule-language alignment, and the proposed annotation method is readily extensible to larger datasets and broader chemical tasks that rely on structural descriptions. Annotation ; Metadata ; Construct (python library) ; Parsing ; XML ; Natural language ; Function (biology) ; Interface (matter) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Chenlei Xu | Tianhao Su | Jie Xiong | YUE WU | Shuya Dong | Tian Jiang | Mengwei He | Shuai Chen | Tong-Yi Zhang
Source: npj Computational Materials (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

KAN-enhanced contrastive learning: the accelerator of crystal structure identification from XRD patterns Accurate crystal structure determination underpins materials discovery, yet powder X-ray diffraction (XRD) analysis still depends on expert-driven, iterative fitting that limits scalability for high-throughput and autonomous experiments. We introduce XRD-Crystal Contrastive Pretraining (XCCP), a physics-guided contrastive learning framework that aligns PXRD patterns with candidate crystal structures in a shared embedding space to enable efficient structure retrieval and symmetry inference. XCCP employs a dual-expert XRD encoder with a Kolmogorov-Arnold Network (KAN) projection head. A low-angle branch captures long-length-scale signatures, while a wide-angle branch encodes dense, symmetry-governed fingerprints. Attribution and perturbation analyses show that the KAN head concentrates evidence on physically meaningful Bragg reflections rather than background-dominated regions, improving robustness to peak-shape variations. We further introduce similarity-based confidence scores to flag potentially unreliable predictions in open-set settings. Without elemental priors, XCCP achieves 46.42% top-1 accuracy for structure retrieval and 60.85% accuracy for space-group identification. When chemical composition is available for elemental pre-screening, performance increases to 88.98% and 93.39%, respectively. XCCP also generalizes to compositionally similar multi-principal element alloys and enables zero-shot transfer to experimental patterns. These results establish XCCP as an interpretable, confidence-aware, and scalable paradigm for XRD analysis, enabling high-throughput screening, rapid candidate shortlisting, and integration with autonomous laboratory workflows. Robustness (evolution) ; Powder diffraction ; Scalability ; Encoder ; Diffraction ; Crystal structure ; Embedding Machine Learning in Materials Science ; Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications
Authors: Yuhui Wu | Cong Wang | Xintian Shen | Yan Chen | H. Wang | Bocheng Xu | Zihao Zhu | Yi Chen | W.W.-M. Dai | Yue Huang | Lingyun Zou | Jian Ji | Peng Zhang
Source: Nature Communications (article, 2026-01-21)
Field: Chemistry — Organic Chemistry

Iterative discovery of potent polymeric antibiotics via multi-stage and multi-task learning against antimicrobial resistance Drug-resistant bacterial infections pose a serious threat to global health, driving the development of antibacterial strategies beyond classic antibiotics. Host defense peptide mimetic polymeric antibiotics have emerged as promising candidates to combat drug resistance, however, navigating the vast chemical space of polymers remains a significant challenge due to complex structure-activity relationships, while data-driven approaches are further constrained by polymer complexity and scarce labeled data. To address this, we develop PolyCLOVER, a framework that integrates multi-stage self-supervised learning, active learning, and high-throughput experimentation to iteratively discover polymeric antibiotics with potent antibacterial activity and low toxicity. Applied to a combinatorial library of ~100,000 poly(β-amino ester)s, the framework uncovers three lead compounds that self-assemble into stable nanoparticles (SANPs) with minimum inhibitory concentrations of 4 μg/mL and 8 μg/mL against multidrug-resistant S. aureus and A. baumannii, respectively. These SANPs also serve as adjuvant antibiotic carriers, restoring bacterial sensitivity to penicillin G. In vivo studies demonstrate their therapeutic efficacy both as monotherapies and in combination therapies with antibiotics. PolyCLOVER may become a powerful framework for discovery of new polymeric biomaterials without reliance on external datasets. Antibiotics ; Antimicrobial ; Antibiotic resistance ; Drug discovery ; Drug ; Penicillin ; Drug resistance Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Orthopedic Infections and Treatments
Authors: Qian Lai | De Li | Jun Bian | Jun Jun Wu | Ke Cheng Yang | Hai Lan Lin | Dai Qiang Chen
Source: Journal of Applied Polymer Science (article, 2026-01-06)
Field: Materials Science — Materials Chemistry

Review of Progress in Polymer‐Based Interfacial Thermal Management Composites ABSTRACT Contemporary electronic devices are evolving toward higher integration and power density, which has intensified the conflict between rapid heat accumulation and limited dissipation space. This issue has become a major constraint on device performance and operational safety. Thermal interface materials (TIMs) play a vital role in thermal management systems. However, incomplete thermal pathways formed during the manufacturing process can compromise thermal conductivity and even introduce safety risks. Therefore, the development of high‐performance TIMs holds significant practical importance. This review examines the mechanisms of interfacial thermal management and evaluates the effectiveness of various structural design strategies. It further explores the selection and modification of diverse functional fillers, interfacial engineering strategies, composite fabrication methods, and machine learning‐assisted design, highlighting their contributions to enhancing interfacial thermal management performance. In addition, the article covers a range of processing techniques used in the production of these advanced materials, highlighting the versatility and scalability potential of polymer‐based interfacial thermal management composites. The key strength of this review lies in its consolidation of current knowledge, along with its discussion of future applications, existing challenges, and emerging directions for polymer‐based interfacial thermal management composites. Thermal management of electronic devices and systems ; Thermal ; Fabrication ; Consolidation (business) ; Thermal conductivity ; Thermal grease ; Composite number ; Constraint (computer-aided design) Thermal properties of materials ; Aerogels and thermal insulation ; Thermal Radiation and Cooling Technologies
Authors: Mrs.Wandhekar S.B | Tangade R.S | Mrs Khandagale J.K
Source: International Journal of Engineering Research and Science & Technology (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

A Review on Artificial Intelligence and Machine Learning in Green Chemistry Green chemistry aims to design chemical products and processes that reduce or eliminate the use and generation of hazardous substances. With the rapid growth of data-driven technologies, Artificial Intelligence (AI) and Machine Learning (ML) are emerging as transformative tools in chemical research and industry. These technologies enable prediction of chemical reactions, optimization of process conditions, design of safer molecules, and reduction of waste and energy consumption. This paper discusses the role of AI and ML in advancing green chemistry, explains key concepts in simple language, examines current challenges, and explores future prospects. The integration of AI and ML into green chemistry provides a promising pathway toward sustainable industrial development and environmental protection. Transformative learning ; SAFER ; Process (computing) ; Hazardous waste ; Green chemistry ; Key (lock) Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Smart Systems and Machine Learning
Authors: Shruti Gunjal | Ranbir Kumar | Sanjay D. Mhaske
Source: Journal of Research in Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Direct covalent linkage of organic functional group to carbon nanotube: A review article Carbon nanotubes (CNTs) have proven to be very promising nanocarbons and have found many applications in various realms like chemistry, agriculture, polymers, and biomedicine since their discovery. The nanoscale dimensionality, high aspect ratio, superior mechanical properties, electrical conductivity, and large surface area of CNT molecules contribute to their superior physicochemical properties and suitability for modern technological uses. To increase their effectiveness in applications and compatibility, it has been found that the chemical modification of these carbon nanotubes by incorporating various organic functional groups has been very effective. The following review specifically discusses the direct chemical functionalization of carbon nanotubes by bonding organic molecules and functional groups to the surface of MWCNTs and SWCNTs. Carbon nanotube ; Surface modification ; Functional group ; Molecule ; Covalent bond ; Organic molecules ; Carbon fibers ; Chemical modification Carbon Nanotubes in Composites ; Conducting polymers and applications ; Covalent Organic Framework Applications
Authors: Chaima ben Makhlouf | M. K. M. Ali | Wided Nouira | Fakher Hcini | Wael Chouk | Naoufel Ben Hamadi | Sobhi Hcini | Malek Gassoumi
Source: Journal of Sol-Gel Science and Technology (article, 2026-03-28)
Field: Materials Science — Materials Chemistry

Structural, vibrational, and dielectric characterization of sol–gel synthesized spinel ferrite for high-performance multifunctional applications Ni₀.₄Cd₀.₃Mg₀.₃Fe₂O₄ spinel ferrite was synthesized by the sol–gel method and calcined at 950 °C. XRD confirmed a well-crystallized cubic spinel structure with crystallite sizes between 99 and 113 nm. Dielectric and conductivity analyses revealed that conduction and relaxation are driven by the same charge carriers, with increased charge carrier mobility at higher frequencies. The Non-Small Polaron Tunneling (NSPT) model successfully described charge transport—selected over alternatives like CBH or OLPT due to the observed increase in the frequency exponent s with temperature—yielding a low activation energy (0.025 eV) and a polaron binding energy of 0.41 eV. Ghosh scaling demonstrated universal transport behavior, while impedance spectroscopy highlighted the role of grains and grain boundaries. The material’s high resistivity at room temperature makes it a promising candidate for microwave and electronic device applications. Spinel ; Polaron ; Dielectric ; Dielectric spectroscopy ; Activation energy ; Crystallite ; Conductivity ; Ferrite (magnet) ; Electrical resistivity and conductivity Magnetic Properties and Synthesis of Ferrites ; Electromagnetic wave absorption materials ; Pigment Synthesis and Properties
Authors: Tristan Maxson | Tibor Szilvási
Source: ChemRxiv (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Visualizing Catalyst Materials with Multi-Material 3D Printing Understanding the 3D structures of materials is central to catalysis education.Here, we present a practical workflow with examples for generating 3D printable models of atomic catalyst structures using multimaterial systems and open-source software to enable broad adoption.We demonstrate how printed models can represent features such as internal porosity, surface terminations, and alloying.The resulting models are valuable for both classroom instruction and research communication. Workflow ; 3D printing ; Software ; 3d printed ; Visualization ; Catalysis ; 3d model ; Software tool Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Various Chemistry Research Topics
Authors: Reshma Devi | Keith T. Butler | Gopalakrishnan Sai Gautam
Source: npj Computational Materials (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Leveraging transfer learning for accurate estimation of ionic migration barriers in solids Rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors, is exponentially dependent on the ionic migration barrier (<i>E</i> <sub><i>m</i></sub> ) within solids, a difficult-to-estimate quantity. Previous approaches to identify materials with low <i>E</i> <sub><i>m</i></sub> have often relied on imprecise descriptors or rules-of-thumb. Here, we present a graph-neural-network-based architecture that leverages principles of transfer learning to efficiently and accurately predict <i>E</i> <sub><i>m</i></sub> across a variety of materials. We use a model (labeled MPT) that has been simultaneously pre-trained on seven bulk properties, introduce architectural modifications to build inductive bias on different migration pathways in a structure, and subsequently fine-tune (FT) on a manually-curated, literature-derived, first-principles computational dataset of 619 <i>E</i> <sub><i>m</i></sub> values. Importantly, our best-performing FT model (labeled MODEL-3, based on test set scores) demonstrates substantially better accuracy compared to classical machine learning methods, graph models trained from scratch, and a universal machine learned interatomic potential, with a <i>R</i> <sup>2</sup> score and a mean absolute error of 0.703 ± 0.109 and 0.261 ± 0.034 eV, respectively, on the test set and is able to classify 'good' ionic conductors with an 80% accuracy. Thus, our work demonstrates the effective use of FT strategies and MPT architectural modifications to predict <i>E</i> <sub><i>m</i></sub> , and can be extended to make predictions on other data-scarce material properties. Test set ; Set (abstract data type) ; Graph ; Transfer of learning ; Work (physics) ; Training set ; Variety (cybernetics) ; Mean absolute error Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Electrocatalysts for Energy Conversion
Authors: Oliver Loveday | Kamila Kaźmierczak | Núria López
Source: ACS Catalysis (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Challenges and Opportunities of Pretrained Machine Learning Interatomic Potentials in Heterogeneous Catalysis The design of catalysts gets its fundamental rationale from accurate and efficient modeling of reactivity on surfaces and materials. To reach this detailed atomistic understanding, density functional theory (DFT) has been the key computational technique. However, the emergence of machine learning interatomic potentials (MLIPs) marks a significant paradigm shift, offering the potential to match DFT accuracy at a drastically reduced computational cost. This perspective provides an overview of state-of-the-art MLIPs for heterogeneous catalysis as "out-of-the-box" tools. We summarize the different families of MLIPs and their training processes and then apply these pretrained models to heterogeneous catalysis problems. Furthermore, we critically address the challenges of model transferability and integration in unified frameworks, underscoring the necessity for standardized protocols to benchmark performance across different architectures. Finally, we assess the capacity of pretrained models to democratize computational catalysis, highlighting the specific hurdles that remain in achieving reliable, predictive power for widespread use. Transferability ; Benchmark (surveying) ; Computational model ; Key (lock) ; Perspective (graphical) ; Density functional theory Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Kaixin Luo | Yuhan Yang | Sadaruddin Chachar | Chenggong Zhong | Meiqi Chen | Jun Xiong | Lianyi He | Dingying Liu | Shahla Karim Baloch | Ihab Elshoura | Zaid Chachar | Yuanzhe Cai | Feijuan Huang
Source: Frontiers in Pharmacology (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

AI-driven pilot platforms and computational pharmaceutics: accelerating innovation in small molecule drug development under industry 4.0 and 5.0 paradigms In the era of artificial intelligence (AI) and Industry 4.0, pilot-scale platforms for small molecule chemical drugs are undergoing a transformative digital evolution. These platforms serve as a critical link between early-stage laboratory research and full-scale pharmaceutical manufacturing, ensuring process feasibility, scalability, and regulatory compliance. This review offers a comprehensive and forward-looking analysis of the structure, function, and strategic importance of pilot-scale systems within the modern pharmaceutical landscape. Focusing on the integration of AI and intelligent automation, the study highlights innovations such as AI-driven process optimization, predictive maintenance, data integration, digital twin technologies, and continuous manufacturing. These technologies are reshaping conventional production paradigms by enhancing efficiency, improving quality control, and reducing environmental impact. The convergence of computational pharmaceutics and green chemistry is also examined as a key driver of sustainable and intelligent drug development. Moreover, the review addresses the industry's transition toward Industry 5.0, characterized by human-machine collaboration, data-centric innovation, and an emphasis on sustainability. Persistent challenges such as equipment standardization gaps, data-sharing limitations, and outdated infrastructure are critically discussed. Drawing from industrial case studies, academic research, and best practices, this paper explores both the opportunities and constraints associated with AI-enabled pilot platforms. Ultimately, the review aims to inform future strategies in digital pharmaceutical manufacturing by underscoring the importance of technological innovation, regulatory alignment, and collaborative ecosystems in advancing the development, efficiency, and sustainability of small molecule drug production. Transformative learning ; Standardization ; Pharmaceutical industry ; Process (computing) ; Sustainability ; Sustainable development ; Drug development ; Biopharmaceutical ; Industry 4.0 Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Jiayi Xu | Silong Zhai | HUANG Panyi | Wenbo Yu | Qingyi Mao | Kui Du | Weike Su | Bin Sun | Can Jin | An Su
Source: Communications Chemistry (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

An artificial intelligence-driven synthesis planning platform (PhotoCat) for photocatalysis While photocatalysis has emerged as a transformative tool in modern synthesis, AI-assisted reaction prediction faces significant challenges due to data limitations. We present PhotoCatDB - a curated, open-source database containing 26.7 K photocatalytic reactions with detailed mechanistic annotations, including 9.2 K multicomponent transformations. Leveraging this resource alongside 100 million molecular data points, we developed PhotoCat, a Transformer-based platform that achieves unprecedented accuracy in photocatalytic reaction prediction (82.6%), retrosynthesis (77.1%), and condition recommendation (88.5%). The platform's capabilities were experimentally validated through the discovery of four novel photocatalytic reactions with yields up to 75.3%. This integrated approach establishes a new paradigm for data-driven innovation in photocatalysis, bridging computational prediction with experimental validation to accelerate discovery in sustainable chemistry. Photocatalysis ; Transformative learning ; Bridging (networking) ; Reaction conditions ; Resource (disambiguation) ; Sustainable production Machine Learning in Materials Science ; Radical Photochemical Reactions ; Advanced Photocatalysis Techniques