jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shian Zhong | Boying Huang | Pengwei Xu | Fanjie Xu | Yuhao Zhao | Jun Cheng | Fujie Tang | Weinan E | Zhong-Qun Tian
Source: arXiv (Cornell University) (article, 2026-03-29)
Field: Materials Science — Materials Chemistry

Solving the inverse problem of X-ray absorption spectroscopy via physics-informed deep learning Resolving transient atomic configurations in non-crystalline or dynamic environments remains a fundamental bottleneck in the physical sciences. While X-ray absorption spectroscopy (XAS) is a premier probe of local structure, inverting spectra into structural descriptors is a notoriously ill-posed problem due to inherent many-to-one mapping. Here, we present the Spectral Pattern Translator (SPT), a physics-informed deep learning framework that establishes a robust bridge between large-scale theoretical datasets and experimental reality. Our strategy exploits the Fourier duality between spectral energy oscillations and spatial scattering paths to overcome the "simulation-to-experiment" gap. By decomposing spectra into frequency domains, SPT effectively isolates robust structural coordination signals from the destabilizing noise inherent in experimental data. Trained on a massive library of diverse atomic environments, this approach achieves state-of-the-art accuracy in resolving continuous phase transitions in battery cathodes and deciphering local order in amorphous materials. With millisecond-scale latency, SPT removes the primary computational barrier to autonomous materials discovery, establishing a robust, noise-resilient engine for closed-loop robotic chemistry. Spectroscopy ; Inverse problem ; Bottleneck ; Noise (video) ; Spectral line ; Energy (signal processing) ; Deep learning ; Phase (matter) ; Absorption (acoustics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced X-ray Imaging Techniques
Authors: Xiankang Tang | Ruiwen Xie | Jan P. Hofmann | Hongbin Zhang
Source: arXiv (Cornell University) (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization Low-energy electron diffraction (LEED) is a cornerstone technique for determining surface atomic structures[heldStructureDeterminationLowenergy2025], yet the quantitative analysis of electron diffraction intensity as a function of incident electron energy -- that is, LEED-\textit{I(V)} analysis -- remains a complex inverse problem. In this work, we tackle quantitative LEED-\textit{I(V)} analysis based on physics-informed Bayesian optimization (BO). By embedding multiple scattering LEED forward models directly into a trust-region BO loop, our approach simultaneously optimizes both structural and experimental parameters, adaptively adjusting trust regions for efficient exploration of complex non-convex parameter spaces without manual intervention. The robustness and scalability of the approach are demonstrated using the Ag(100)-(1$\time$1) and Fe\textsubscript{2}O\textsubscript{3}(1$\overline{1}$02)-(1$\time$1) surfaces as examples. Our work establishes a general framework for solving inverse problems in various characterization techniques, unlocking a physics-informed efficient, reproducible, and autonomous paradigm. Diffraction ; Robustness (evolution) ; Inverse ; Embedding ; Inverse problem ; Surface (topology) ; Electron diffraction ; Low-energy electron diffraction ; Bayesian probability ; Characterization (materials science) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Quantum Computing Algorithms and Architecture
Authors: Konda Reddy Gondi | Sreenivasulu Surisetty | Damodhar Reddy | L.V.K.S. Bhaskar | Sneha P
Source: Journal of Applied Science and Technology Trends (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Artificial Intelligence and Knowledge-Based Engineering for the Design of Environmental Nanomaterials: A Review AI and KBE have been commonly applied into designing environmental nanomaterials to enhance performance, sustainability, and innovation. This review focused on how applying AI’s tools such as machine learning, expert systems, predictive modeling techniques and KBE frameworks accelerates material discovery time in addition to enhancing structural design optimization and decision making for environmental applications also. Case studies illustrate applications of the computing tools to integrate experimental work in water purification, pollution control, and renewable energy. The paper makes recommendations for the revision of existing methods and new areas of investigation for intelligent adaptive and sustainable frameworks for nanomaterial design. Expert system ; Applications of artificial intelligence ; Intelligent decision support system ; Work (physics) ; Environmental pollution ; Decision support system Machine Learning in Materials Science ; Nanoparticles: synthesis and applications ; Solar-Powered Water Purification Methods
Authors: Clodagh M Boland | Nhat Quynh Nguyen | Nathan R. B. Boase
Source: Macromolecular Rapid Communications (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

Active Learning for the Discovery of Antiviral Polymers ABSTRACT The development of nanomedicines has long relied on scientific intuition within finite chemical space, but machine learning offers a data‐driven approach to explore far broader chemical landscapes. Advances in high‐throughput polymer synthesis and screening now enable the generation of datasets large enough to train predictive models to accurately link polymer structure with biological function. The next challenge is using machine learning to guide iterative development of nanomedicines through active learning. Here we demonstrate an active learning workflow for the design of antiviral polymers. Using molecular descriptors derived from polymer composition and monomer structure, a machine learning model was trained on an experimental dataset of antiviral polymer activity. The trained model was coupled with unsupervised clustering of monomers to explore chemical diversity and predict antiviral activity across a virtual library of up to 500,000 new polymers. By calculating the expected improvement function, active learning identifies optimal candidates for synthesis to efficiently explore chemical space and optimize antiviral activity. This communication highlights how machine learning and active learning can serve as practical, accessible tools for chemists and biologists to accelerate design of functional polymers and future nanomedicines. Workflow ; Chemical space ; Active learning (machine learning) ; Intuition ; Cluster analysis ; Unsupervised learning ; Polymer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Nanopore and Nanochannel Transport Studies
Authors: Zuofang Feng | Yihui Zhu | Chaoyi Han | Huijie Ni | Lulu Zhang | Chuanxin Wei | Xing Chen | Zhengong Meng | Yanli Zhao | Yilong Lei
Source: Nature Communications (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Non-macrocyclic molecular design enables cavity-adaptive cocrystals with high elasticity and low-threshold lasing The construction of cavity-adaptive cocrystals (CACs) based on linear non-macrocyclic π-conjugated hosts remains challenging due to their inherently weak and less predictable binding capabilities compared to conventional macrocyclic hosts. Here we overcome this limitation through the rational design and synthesis of CACs employing a structurally engineered non-macrocyclic host. This host combines a linear π-conjugated backbone with sterically hindered groups, allowing dynamic cavity modulation while maintaining its luminescent characteristics upon solvent and guest encapsulation. Heteroatom-modified guest molecules of similar size form molecular cavities with comparable dimensions yet exhibit different luminescence characteristics. Further tailoring the host's π-backbone yields color-tailorable CACs. These systems exhibit relatively strong intralayer C - H···π interactions coupled with weak interlayer forces, leading to high elasticity and low-threshold lasing action in single crystals and microcrystals. This work establishes a non-macrocyclic host/guest platform with adaptive cavities, demonstrating its potential as flexible laser emitters. Lasing threshold ; Luminescence ; Rational design ; Elasticity (physics) ; Molecule ; Steric effects ; Laser ; Work (physics) Luminescence and Fluorescent Materials ; Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions
Authors: Imon Mia | Armi Tiihonen | Anna Ernst | Anusha Srivastava | Tonio Buonassisi | William G. Vandenberghe | Julia W. P. Hsu
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Multi-variable batch Bayesian optimization in materials research: Synthetic data analysis of noise sensitivity and problem landscape effects Abstract Bayesian optimization (BO) holds promise for accelerating materials science research; however, it faces challenges with high-dimensional inputs and experimental noise in real-world problems. This study addresses these issues by benchmarking batch BO on two synthetic six-variable optimization tasks at varying noise levels: a needle-in-a-haystack task (Ackley function) representing rare materials properties, and a smooth landscape (Hartmann function) simulating process optimization. We evaluate key BO strategies, including acquisition functions, batch-picking methods, and exploration hyperparameter tuning, while presenting a framework for tracking high-dimensional optimization progress. Results show optimization outcomes are highly sensitive to noise levels and landscape shapes. This information enables the design of robust materials optimization campaigns with pre-planned experimental budgets that account for real-world uncertainties. Our methodology facilitates greater BO utilization in experimental materials research, particularly for multi-variable optimization problems, by providing practical guidance for configuring BO campaigns in challenging scientific applications. Graphical abstract Bayesian optimization ; Noise (video) ; Sensitivity (control systems) ; Benchmarking ; Process (computing) ; Key (lock) ; Synthetic data ; Hyperparameter Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Advanced Bandit Algorithms Research
Authors: Jiawei Gong | Danqing Ma | Ralph Bulanadi | Moore, Robert Kennon;Mayo, Charles | Rama K. Vasudevan | Lei Zhao | Yongtao Liu
Source: ArXiv.org (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Accelerating Structure-Property Relationship Discovery with Multimodal Machine Learning and Self-Driving Microscopy Microscopy combined with local spectroscopy is widely used to correlate nanoscale structure with functional properties in materials, but conventional measurements rely heavily on human-selected sampling locations and predefined targets, limiting dataset diversity and the potential for discovery. Here, we present a framework that integrates autonomous microscopy with a dual-novelty deep kernel learning (DN-DKL) for adaptive data acquisition and a dual variational autoencoder (VAE) for representation learning. DN-DKL actively guides the microscopy toward structurally and spectroscopically novel regions, enabling efficient collection of large spectral datasets. Dual-VAE embeds local structure and spectroscopic responses into a shared latent manifold that serves as a structure-property relationship map. We applied this framework for the investigation of halide perovskite films using conductive atomic force microscopy. The results reveal distinct hysteresis behaviors that are linked to specific nanoscale structural motifs, including grain boundary junction points that show hysteresis under different bias conditions and asymmetric grain boundaries that suppress charge transport. This framework establishes a general strategy that leverages the complementary strengths of self-driving microscopy, machine learning, and human expertise to accelerate scientific discovery in functional materials. Representation (politics) ; Grain boundary ; Microscopy ; Nanoscopic scale ; Perovskite (structure) ; Kernel (algebra) ; Hysteresis ; Autoencoder Machine Learning in Materials Science ; Perovskite Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Florian Rothenhäusler | Holger Ruckdaeschel
Source: Adhesives (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning for Reactive Structural Adhesive Design: A Framework for Chemistry, Formulation, and Optimization Reactive structural adhesives—epoxies, polyurethanes, and acrylics—are essential in high-performance applications, yet their development remains complex due to multiscale adhesion mechanisms, combinatorial formulation spaces, and stringent performance requirements. Traditional trial-and-error approaches are time- and resource-intensive. Machine learning (ML) provides a powerful framework to accelerate adhesive design by capturing nonlinear relationships between formulation, processing, and performance, while enabling predictive modeling, optimization, and experiment prioritization. This review presents a process-oriented guide for ML-assisted adhesive development, covering component selection, feature engineering, initial dataset design, model choice, and iterative workflows integrating classical design-of-experiments, active learning, and Bayesian optimization. Emphasis is placed on interpreting ML outputs through the lens of polymer chemistry, reaction kinetics, and fracture mechanics to extract mechanistic insights and guide rational formulation design. Key challenges—including small, noisy datasets, multi-component interactions, and multi-objective trade-offs—are discussed, along with emerging directions such as collaborative databases, automated knowledge extraction, and hybrid ML–chemistry approaches to further enhance structural adhesive development. The review underscores the potential of integrating ML into adhesive R&amp;D to reduce experimental burden, improve formulation efficiency, and enable data-driven exploration of complex chemistries. Workflow ; Component (thermodynamics) ; Feature (linguistics) ; Adhesive ; Key (lock) ; Bayesian probability ; Nonlinear system Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Advanced Polymer Synthesis and Characterization
Authors: Jiahao Xu | Hanzi Zheng | Yang Gao | Yuanqiu Lai | Mengya Peng | Yike Hu | Tianmeng Yuan | Xiang Liu | Shihan Zhou | Wei Duan | Jia‐Wei Shen | Yongke Zheng
Source: MedComm (article, 2026-03-28)
Field: Chemistry — Inorganic Chemistry

The Application of Metal–Organic Frameworks as Drug Delivery Systems: From the Perspective of Molecular Dynamics Simulations Metal-organic frameworks (MOFs) have emerged as a promising class of nanomaterials for drug delivery due to their exceptionally high surface area, tunable pore structures, and chemical versatility. However, conventional experimental techniques cannot fully capture atomic-scale drug-carrier interactions or transient diffusion processes within MOF pores. Molecular dynamics (MD) simulation, a computational technique that tracks atom-level movements over time, has thus become indispensable for probing these microscopic mechanisms. This review introduces the fundamentals of MD simulation and comprehensively examines how MD simulation reveals drug adsorption mechanisms, functionalization effects, and release kinetics in MOF-based delivery systems. Then, it systematically compares major MOF families including isoreticular metal-organic frameworks, zeolitic imidazolate frameworks, Materials of Institute Lavoisier Frameworks, University of Oslo Frameworks, and porous coordinated networks and highlight their distinct host-guest interactions and stimuli-responsive behaviors. The integration of multiscale modeling and machine learning further enhances predictive capabilities for carrier design. By establishing MD simulation as a fundamental tool for understanding nanoscale drug-carrier interactions, this review provides a theoretical foundation for developing efficient, stable, and responsive MOF-based nanocarriers, advancing the field of precision nanomedicine. Drug delivery ; Molecular dynamics ; Zeolitic imidazolate framework ; Field (mathematics) ; Perspective (graphical) ; Multiscale modeling ; Targeted drug delivery Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Multicomponent Synthesis of Heterocycles
Source: ChemRxiv (article, 2026-03-09)
Field: Chemistry — Inorganic Chemistry

Low frequency (THz) Raman Spectroscopy - A green and versatile method to probe polymorphism, isomerism and solvent-induced dynamics in Fe-based metal-organic frameworks THz (low Raman spectroscopy ; Low frequency ; Dynamics (music) ; Spectroscopy ; Coherent anti-Stokes Raman spectroscopy Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Spectroscopy and Quantum Chemical Studies
Authors: Diego Garay-Ruiz | Sergio Pablo-García | Han Hao | Marisol Martín-González
Source: Digital Discovery (article, 2026-01-01)
Field: Chemistry — Electrochemistry

Database utility for cyclovoltammetry knowledge (DUCK): unified platform for electrochemical data Cyclic voltammetry (CV) is a valuable tool for electrochemistry, providing qualitative and quantitative information about redox processes occurring in solution. Despite its ubiquity, the lack of standardized reporting and sharing protocols, commonly adopted, significantly hinders data reusability and reproducibility. Here, we present DUCK (Database Utility for Cyclovoltammetry Knowledge), a comprehensive platform for organizing, analyzing and exploring CV data through ontology-based knowledge graphs. The system comprises three components: (1) CVOnto, a formal ontology for electrochemical CV measurement data, (2) DUCK-KG, an automated tool for the generation of knowledge graphs from experimental metadata, and (3) DUCK-Viz, an interactive web interface for visualization and analysis of CV data. We demonstrate applicability across two distinct domains: 130 CV experiments from electrodeposition studies in traditional laboratories, and 79 automated measurements from metal-ligand complexes in self-driving laboratories, performing Bayesian optimization workflows. The application to these two paradigms demonstrates DUCK's potential as a unified platform implementing FAIR data principles for the electrochemistry community. Sample (material) ; Information system ; Data access ; Relational database ; Data integration Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Conducting polymers and applications
Authors: Ailsa Geddis | Hannah Williams | Saba Bashir | Jason Malenfant | Caroline Dubois | Louis Hamlet | Jean-François Masson
Source: Chemical Society Reviews (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial intelligence and machine learning for plasmonic and surface-enhanced sensing Plasmonic sensing is a vibrant field where the optical properties of surface plasmons are exploited to create analytical sensors for biomedical, environmental and food safety applications, among others. Upon irradiation of light on a plasmon-active nanomaterial, the enhancement of the electric field leads to augmented scattering, absorption and luminescence of molecules in, respectively, surface-enhanced Raman scattering (SERS), surface-enhanced infrared absorption (SEIRA) and metal-enhanced fluorescence (MEF) and to highly sensitive refractometric sensors with surface plasmon resonance (SPR) and localised surface plasmon resonance (LSPR). The advent of a new generation of artificial intelligence (AI) and machine learning (ML) tools provides an opportunity to further advance the design, synthesis and characterisation of plasmonic materials, improve signal processing and image analysis in plasmonic sensing experiments and to design sensors with better sensitivity, selectivity and robustness. The review will first build basic knowledge in plasmonic sensing and AI/ML, before discussing opportunities for AI/ML-augmented sensor design and data analysis, and then discuss applications where AI/ML provided added benefits in plasmonic sensing. The review will conclude with a perspective on where the field is trending. Plasmon ; Raman scattering ; Surface plasmon resonance ; Absorption (acoustics) ; Surface plasmon ; Field (mathematics) ; SIGNAL (programming language) ; Signal processing Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Advanced biosensing and bioanalysis techniques
Authors: Thibaud Mabit
Source: ChemRxiv (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

Mechanism-based features: an inductive bias for knowledge transfer in low-data yield prediction This work investigates the transfer of chemical knowledge through mechanism-based descriptors and their impact on reaction yield prediction. By encoding mechanistic insight into domain-specific features, the proposed approach introduces a beneficial inductive bias. Using a Mizoroki-Heck C-glycosylation reaction as a case study, we first provide an overview of the reaction mechanism, then describe the dataset construction and featurization strategy, followed by classification model training and evaluation. While regression predicts continuous outcomes, such as reaction yields, a classification task can indicate whether a reaction is likely to proceed under standard conditions. Identifying feasible reactions is often more important at early stages than achieving high yields. By prioritizing feasibility, this approach provides a practical tool for rapidly screening reaction conditions. The results demonstrate that incorporating mechanism-guided features leads to a significant improvement in predictive performances, with gains of up to + 14% for balanced accuracy and +25% for F1-score in a low-data classification. Feature importance analysis further highlights the relevance of these descriptors, linking model predictions to key mechanistic factors. Overall, this study shows that mechanism-based feature design is a promising and data-efficient strategy to enhance model performance, particularly in low-data regimes, and can serve as a complementary approach to quantum mechanical and physicochemical descriptors. Relevance (law) ; Feature (linguistics) ; Task (project management) ; Encoding (memory) ; Key (lock) ; Yield (engineering) ; Regression ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Natural Products and Biosynthesis