jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Akshay Subramanian | Elton Pan | Juno Nam | Maurice Weiler | Shuhui Qu | Cheol Woo Park | Tommi Jaakkola | Elsa A. Olivetti | Rafael Gomez-Bombarelli
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because candidate generation is combinatorial and stability is only resolved after costly energy evaluations. Here we introduce PackFlow, a flow matching framework for molecular crystal structure prediction (CSP) that generates heavy-atom crystal proposals by jointly sampling Cartesian coordinates and unit-cell lattice parameters given a molecular graph. This lattice-aware generation interfaces directly with downstream relaxation and lattice-energy ranking, positioning PackFlow as a scalable proposal engine within standard CSP pipelines. To explicitly steer generation toward physically favourable regions, we propose physics alignment, a reinforcement learning post-training stage that uses machine-learned interatomic potential energies and forces as stability proxies. Physics alignment improves physical validity without altering inference-time sampling. We validate PackFlow's performance against heuristic baselines through two distinct evaluations. First, on a broad unseen set of molecular systems, we demonstrate superior candidate generation capability, with proposals exhibiting greater structural similarity to experimental polymorphs. Second, we assess the full end-to-end workflow on two unseen CSP blind-test case studies, including relaxation and lattice-energy analysis. In both settings, PackFlow outperforms heuristics-based methods by concentrating probability mass in low-energy basins, yielding candidates that relax into lower-energy minima and offering a practical route to amortize the relax-and-rank bottleneck. Scalability ; Maxima and minima ; Reinforcement learning ; Stability (learning theory) ; Crystal structure prediction ; Ranging ; Relaxation (psychology) ; Heuristic ; Workflow Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Nesimi Uludağ | Sümeyya İNÖNÜ ÜNİVERSİTESİ SERİN | Elvan Üstün | Goncagül Serdaroğlu
Source: Chemistry & Biodiversity (article, 2026-02-01)
Field: Chemistry — Organic Chemistry

A Synthesis of Novel Tetracyclic Azocino[4,3‐b]Indole Framework: Combined Spectroscopic, DFT, Molecular Docking, and ADMET Analysis The paper describes a new synthetic method for the 1,5-methanoazocino [4,3-b]indole. Starting from 2,3,4,9-tetrahydrospiro[1H-carbazole-1,2'-[1,3]dithiolan]-4(9H)-one I with (1,3-dithiolan-2-yl)methanamine, reduction of imine II with NaBH<sub>4</sub> yielded to the amine and benzoylation, N-((1,3-dithiolan-2-yl)methyl)-N-(2,3,4,9-tetrahydrospiro[carbazole-1,2'-[1,3]dithiolan]-4-yl)benzamide III. Compounds IV and V were completed through several steps from III. The resulting structure V underwent an intramolecular aldol reaction using NaH as a base for the cyclization of 2-benzoyle-4-hydroxy-1,2,3,4,5,7-hexehydro-1,5-methanoazocino[4,3-b]indole-6-one VI, which represents the tetracyclic skeleton of Strychnos alkaloids. Besides, quantum chemical computations were conducted on compounds I-VI. Regarding structural parameters and spectroscopic characterization, the experimental and computational data were found to be in good agreement. Also, ADMET, bioavailability, and drug-likeness properties of the data set were determined to provide comprehensive information on structure-activity relationships. The interactions of I-VI against DNA gyrase of E. coli, CYP51 from Candida glabrata, and leukemia inhibitory factor were evaluated with molecular docking for foresight into the possible bioactivities. This comprehensive work hopefully to provide the structural, physical, and chemical properties, which would be important to optimizing the related properties of the molecular systems for biomedical purposes. Intramolecular force ; Docking (animal) ; Imine ; Molecule ; DNA gyrase ; In silico ; Aldol reaction Synthesis and pharmacology of benzodiazepine derivatives ; Catalytic C–H Functionalization Methods ; Synthesis and bioactivity of alkaloids
Authors: Shamim Pourrahimi | Soroosh Hakimian
Source: Alloys (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Machine Learning for Alloy Design: A Property-Oriented Review Machine learning (ML) is becoming an established part of alloy research, offering new ways to link composition, processing routes, and microstructure with measured properties. In this work, recent studies using ML for predicting or optimizing alloy behavior are reviewed, covering mechanical, corrosion, phase-related, and physical properties. Unlike previous reviews organized by alloy system or modeling approach, this review is structured by target property (mechanical, corrosion, phase/structure, and physical), which helps identify the input features commonly used to model each property and highlights existing gaps in data and validation. For each study, the main property of interest, dataset features, model type, algorithm choice, use of hyperparameter tuning, and validation strategy were examined. Comparing these reports shows that ensemble models such as random forest and XGBoost, together with deep neural networks, usually perform better than linear approaches. At the same time, issues related to small datasets and inconsistent reporting remain major challenges. Attention is also drawn to new directions, particularly physics-based learning and multi-objective optimization, that are changing how ML is applied in materials design. Overall, this review summarizes current practices and outlines areas where closer integration of data-driven and experimental methods could accelerate the development of next-generation alloys. Property (philosophy) ; Hyperparameter ; Random forest ; Artificial neural network ; Ensemble learning ; Deep learning Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Pankaj Beldar | Atulkumar Patil | Gulshan Kumar | Vilas K. Matsagar
Source: Journal of Engineering and Applied Science (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Optimized deep learning using hybrid PSO-GA on raw XRD images for accurate classification of material degradation Abstract Accurate damage classification in materials using X-ray Diffraction (XRD) patterns is essential for ensuring structural integrity. This study introduces a novel hybrid deep learning and evolutionary optimization framework, PSO-GA, which integrates Convolutional Neural Network (CNN) with Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) for fine-grained classification of material conditions: fresh, slightly degraded and severely degraded. Unlike conventional methods relying on manual features or tabulated data, our model processes raw XRD image plots, capturing spatial and intensity variations. CNNs automatically extract hierarchical features, while GA and PSO jointly optimize model hyperparameters to improve classification accuracy. The proposed method achieved strong performance with F1-scores of 0.925, 0.865, and 0.775 for Intact, Partially Damaged, and Damaged classes, respectively. Feature visualization using t-SNE and PCA revealed distinct clustering among damage categories, confirming the model’s discriminative strength. Optimization results showed rapid convergence with PSO and stable fitness improvement with GA, reaching a fitness value close to 0.99. The PSO-GAframework is robust, interpretable, and novel in its end-to-end learning from raw XRD plots, making it suitable for real-time, non-destructive testing in industrial applications. Particle swarm optimization ; Pattern recognition (psychology) ; Discriminative model ; Convolutional neural network ; Cluster analysis ; Deep learning ; Hyperparameter ; Genetic algorithm Machine Learning in Materials Science ; Welding Techniques and Residual Stresses ; Advanced Neural Network Applications
Authors: N.D.A. Watuthanthrige | V. Lohmann | V. Lutz-Bueno | N.P. Truong | S.P. Armes | A. Anastasaki
Source: White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Depolymerization-induced morphological transformation Depolymerization offers a powerful route for the chemical recycling of vinyl polymers. However, current strategies focus almost exclusively on monomer recovery, which overlooks broader applications and opportunities. Herein, depolymerization-induced morphological transformation (DIMT) is introduced as a modular methodology to control the shape of sterically stabilized diblock copolymer nanoparticles and gain mechanistic insight into morphological transformations that occur during selective degradation of the methacrylic core-forming block. Notably, DIMT results in a sequential evolution in copolymer morphology from vesicles to worms to spheres. Transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS) studies enabled the construction of a predictive (pseudo)phase diagram. Furthermore, this new approach was also applied to the irreversible degelation of diblock copolymer worm gels, highlighting new opportunities to regulate material properties through depolymerization. Copolymer ; Depolymerization ; Monomer ; Transformation (genetics) ; Nanoparticle ; Transmission electron microscopy ; Morphology (biology) ; Steric effects Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Hydrogels: synthesis, properties, applications
Authors: Jacqueline Lease | PeiQin Tan | KaiXian Ng | Mohammed A. A. Farid | Yoshito Andou
Source: Polymer Journal (article, 2026-03-04)
Field: Materials Science — Biomaterials

Optimization of mechanochemical reactions through cellulose transacylation with vinyl laurate Abstract Cellulose esters with long-chain alkyl groups are promising sustainable materials because their hydrophobicity and thermal stability can be tuned through chemical modification. We report a mechanochemical transacylation method for synthesizing cellulose laurates using vinyl laurate in a dimethyl sulfoxide/sodium hydroxide medium. The process enables the efficient modification of 0.81 g of cellulose and yields products with degrees of substitution ranging from 0.50 to 2.94 under different designated conditions. Structural analysis by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) confirmed successful esterification and reduced crystallinity. The physical properties varied systematically with substitution level, with higher substitution improving thermal stability and reducing wettability, providing opportunities to tailor performance. Response surface methodology, which is based on a five-level, four-factor central composite rotatable design, was applied to study the interactive effects of synthesis parameters and optimize the degree of substitution. The experimental results closely matched the predicted values with 97.3% accuracy, demonstrating the robustness of the approach. This study establishes a green and reproducible route for producing functional cellulose esters with potential applications in bioplastics. Cellulose ; Thermal stability ; Fourier transform infrared spectroscopy ; Composite number ; Alkyl ; Infrared spectroscopy Advanced Cellulose Research Studies ; Antimicrobial agents and applications ; Natural Fiber Reinforced Composites
Authors: Haoyu Guo | Maria Tikhanovskaya | Paul Raccuglia | Alexey Vlaskin | Chris Co | Daniel J. Liebling | Scott Ellsworth | Martin Abraham | Elizabeth H. Dorfman | N. P. Armitage | Chunhan Feng | Antoine Georges | Olivier Gingras | Dominik Kiese | Steven A. Kivelson | Vadim Oganesyan | B. J. Ramshaw | Subir Sachdev | T. Senthil | J. M. Tranquada | Michael P. Brenner | Subhashini Venugopalan | Eun-Ah Kim
Source: Proceedings of the National Academy of Sciences (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Expert evaluation of LLM world models: A high-T <sub> <i>c</i> </sub> superconductivity case study Large Language Models (LLMs) show great promise as a powerful tool for scientific literature exploration. However, their effectiveness in providing scientifically accurate and comprehensive answers to complex questions within specialized domains remains an active area of research. Using the field of high-temperature cuprates as an exemplar, we evaluate the ability of LLM systems to understand the literature at the level of an expert. We construct an expert-curated database of 1,726 scientific papers that covers the history of the field, and a set of 67 expert-formulated questions that probe deep understanding of the literature. We then evaluate six different LLM-based systems for answering these questions, including both commercially available closed models and a custom retrieval-augmented generation (RAG) system capable of retrieving images alongside text. Experts then evaluate the answers of these systems against a rubric that assesses balanced perspectives, factual comprehensiveness, succinctness, and evidentiary support. Among the six systems, two using RAG on curated literature outperformed existing closed models across key metrics, particularly in providing comprehensive and well-supported answers. We discuss promising aspects of LLM performances as well as critical short-comings of all the models. The set of expert-formulated questions and the rubric will be valuable for assessing expert level performance of LLM based reasoning systems. Rubric ; Field (mathematics) ; Set (abstract data type) ; Construct (python library) ; Key (lock) Machine Learning in Materials Science ; Topic Modeling ; Computational and Text Analysis Methods
Authors: Qin Mao | Zi Rui Feng | Junming Hou | Che Liu | Yu Ming Ning | Rui Si Li | Qian Wen Wu | Yi Su Huang | Yi Zhang | Ze Gu | Jian Wei You | Tie Jun Cui
Source: PhotoniX (article, 2026-04-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial intelligence with metasurfaces: from intelligent design to intelligent computing Abstract The rapid development of artificial intelligence (AI) has revolutionized traditional design and optimization approaches for metamaterials. By leveraging AI techniques, the time required for the metamaterial design process is significantly reduced, thus improving the overall efficiency. On the other hand, the advancement of AI has placed extremely high demands on computational power. In exploring high-speed and energy- efficient next-generation computing hardware, metasurface-based intelligent computing technologies such as diffractive neural networks have attracted widespread attention. In this work, we focus on two main aspects: intelligent design of metasurfaces and intelligent computing based on metasurfaces. In the field of intelligent design, we analyze how deep learning-based inverse design methods overcome the efficiency bottleneck of traditional electromagnetic simulations and enable high-precision and automated generation of subwavelength structures. In intelligent computing, we comprehensively explore the implementation mechanisms of optical diffractive neural networks and microwave programmable neural networks based on metamaterials. By comparing the application performance of physical neural networks with different architectures in scenarios such as image recognition and wireless communication, we reveal the crucial role of deep integration of the metasurfaces and AI in driving new computing paradigms, providing a theoretical framework and technological roadmap for next-generation intelligent electromagnetic systems. Artificial neural network ; Bottleneck ; Intelligent decision support system ; Field (mathematics) ; Intelligent control ; Intelligent design ; Process (computing) ; Intelligent Network ; Convolutional neural network Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Neural Networks and Reservoir Computing
Authors: Yan Xiang | Zilu Zhang | Rebeca T. Stiepel | Chinmay S. Potnis | Lauren A. Onweller | Joseph Laforet | Harsha S. Gowda | Daniel Reker
Source: ChemRxiv (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Physics-informed design of drug-excipient nanoparticles via free energy calculation and yoked deep learning The discovery of self-assembling molecular systems is often constrained by the high computational cost of physics-based simulations and the limited generalizability of data-driven models trained on experimental datasets. This limits our ability to efficiently navigate massive design spaces. Here, we introduce a physics-informed discovery framework that integrates free-energy calculations with sequential-pool active learning and adaptive yoked deep learning to effectively navigate vast chemical coassembly spaces. Using pairwise free energy as a physically grounded descriptor of co-assembly propensity and excipient net charge as a proxy for interfacial stabilization, our approach predicts nanoparticle-forming drug–excipient combinations without relying on experimental data for model training. Our sequential-pool yoked deep learning strategy utilizes adaptive machine learning to reduce the number of expensive simulations required while maintaining predictive accuracy, enabling screening of 18 million molecular pairs. Prospective validations identified 24 drug nanoparticles using eight previously unknown stabilizing molecules. The new nanoparticles exhibit ultra-high drug loading, effectively improve the aqueous solubility of drugs, and maintain in vitro drug efficacy. Together, this work establishes a generalizable framework coupling molecular simulations and yoked deep learning to accelerate discovery in self-assembling molecular systems. Deep learning ; Generalizability theory ; Coupling (piping) ; Drug discovery ; Pairwise comparison ; Nanoparticle Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Computational Drug Discovery Methods
Authors: A Ayodeji | E E Alagbe | C Ogbuigbo | al et
Source: Portugaliae electrochimica acta (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Zinc Phosphate Coating in Corrosion Control - A Review Due to its wide areas of applications, ready availability and affordability, mild steel has become a very versatile metal in the industry, but it is usually exposed to different environments that can corrode it, thereby affecting its properties and performance.Hence, effective metal corrosion control is required before or during mild steel application.Zn(PO) coating is a preferred method for surface treatment and finishing of both ferrous and non-ferrous metals, being highly regarded for its efficiency, rapid operation and ability to provide outstanding corrosion and wear resistance, adhesion and lubricative properties.In this review, components of Zn(PO) bath, its detailed process, adsorption mechanism on metal surfaces, applications, thermodynamic and kinetic properties, are considered. Coating ; Corrosion ; Zinc ; Zinc phosphate ; Phosphate Corrosion Behavior and Inhibition ; Magnesium Oxide Properties and Applications ; Pigment Synthesis and Properties
Authors: Sanjanasri JP | Pratiti Bhadra | N. Sukumar | Soman KP
Source: ArXiv.org (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Molecular Representations for AI in Chemistry and Materials Science: An NLP Perspective Deep learning, a subfield of machine learning, has gained importance in various application areas in recent years. Its growing popularity has led it to enter the natural sciences as well. This has created the need for molecular representations that are both machine-readable and understandable to scientists from different fields. Over the years, many chemical molecular representations have been constructed, and new ones continue to be developed as computer technology advances and knowledge of molecular complexity increases. This paper presents some of the most popular digital molecular representations inspired by natural language processing (NLP) and used in chemical informatics. In addition, the paper discusses some notable AI-based applications that use these representations. This paper aims to provide a guide to structural representations that are important for the application of AI in chemistry and materials science from the perspective of an NLP researcher. This review is a reference tool for researchers with little experience working with chemical representations who wish to work on projects at the interface of these fields. Perspective (graphical) ; Popularity ; Interface (matter) ; Natural (archaeology) ; Natural language ; Computational linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; History and advancements in chemistry
Source:  (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Prediction of Proof Test Outcomes for Type 4 High Pressure Hydrogen Vessels with Composite Layers Using Machine Learning Models This study presents a machine learning-based approach for predicting the outcome of the Proof Test, a critical mechanical compliance test on Type IV high-pressure hydrogen vessels (HPVs). These vessels, composed of a polymer liner and carbon composite layers, undergo extreme pressure testing to verify their structural integrity and safety. The goal is to provide a fast and reliable predictive tool to anticipate test outcomes using process parameters collected during the filament winding stage. The dataset consists of 233 vessels, with over 180 passing the test, resulting in a natural class imbalance. 54 production parameters were averaged per vessel to create a compact feature set. Missing values were imputed using a conditional interpolation strategy based on similar test outcomes. A Random Forest classifier was trained using 70% of the data and evaluated on the remaining 30%, achieving 94% accuracy and a ROC-AUC of 0.8686. The confusion matrix reveals strong performances, particularly in identifying failed vessels, with very few false negatives. Feature importance analysis identified winding speed as the most critical variable, followed by winding time, liner inner pressure, doctor blade settings, and fiber tension. However, the relatively low individual importance scores highlight the potential significance of nonlinear interactions between parameters. A preliminary comparison with Burst Test results also supports the model’s potential as a non-destructive validation tool. Overall, this work demonstrates the feasibility of integrating machine learning into composite vessel manufacturing, offering a path toward reduced testing costs, enhanced safety, and greater automation in the hydrogen industry. Random forest ; Confusion matrix ; Test data ; Support vector machine ; Composite number ; Naive Bayes classifier ; Decision tree ; Automation Machine Learning in Materials Science ; Mechanical Behavior of Composites ; Epoxy Resin Curing Processes