jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shuquan Ye | Ben Fei | Hongbin Xu | Jiaying Lin | Wanli Ouyang
Source: Open MIND (preprint, 2026-02-20)
Field: Materials Science — Materials Chemistry

From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark The Solid Electrolyte Interphase (SEI) is critical to the performance of lithium-ion batteries, yet its analysis via Operando Infrared (IR) spectroscopy remains experimentally complex and expensive, which limits its accessibility for standard research facilities. To overcome this bottleneck, we formulate a novel task, Operando IR Prediction, which aims to forecast the time-resolved evolution of spectral ``fingerprints'' from a single static spectrum. To facilitate this, we introduce OpIRSpec-7K, the first large-scale operando dataset comprising 7,118 high-quality samples across 10 distinct battery systems, alongside OpIRBench, a comprehensive evaluation benchmark with carefully designed protocols. Addressing the limitations of standard spectrum, video, and sequence models in capturing voltage-driven chemical dynamics and complex composition, we propose Aligned Bi-stream Chemical Constraint (ABCC), an end-to-end physics-aware framework. It reformulates MeanFlow and introduces a novel Chemical Flow to explicitly model reaction trajectories, employs a two-stream disentanglement mechanism for solvent-SEI separation, and enforces physics and spectrum constraints such as mass conservation and peak shifts. ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines. ABCC even generalizes to unseen systems and enables interpretable downstream recovery of SEI formation pathways, supporting AI-driven electrochemical discovery. Benchmark (surveying) ; Infrared ; Constraint (computer-aided design) ; Infrared spectroscopy ; Sequence (biology) ; Generative model ; Flow (mathematics) ; Electrolyte Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Shuquan Ye | Ben Fei | Hongbin Xu | Jiaying Lin | Wanli Ouyang
Source: ArXiv.org (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

From Static Spectra to Operando Infrared Dynamics: Physics Informed Flow Modeling and a Benchmark The Solid Electrolyte Interphase (SEI) is critical to the performance of lithium-ion batteries, yet its analysis via Operando Infrared (IR) spectroscopy remains experimentally complex and expensive, which limits its accessibility for standard research facilities. To overcome this bottleneck, we formulate a novel task, Operando IR Prediction, which aims to forecast the time-resolved evolution of spectral ``fingerprints'' from a single static spectrum. To facilitate this, we introduce OpIRSpec-7K, the first large-scale operando dataset comprising 7,118 high-quality samples across 10 distinct battery systems, alongside OpIRBench, a comprehensive evaluation benchmark with carefully designed protocols. Addressing the limitations of standard spectrum, video, and sequence models in capturing voltage-driven chemical dynamics and complex composition, we propose Aligned Bi-stream Chemical Constraint (ABCC), an end-to-end physics-aware framework. It reformulates MeanFlow and introduces a novel Chemical Flow to explicitly model reaction trajectories, employs a two-stream disentanglement mechanism for solvent-SEI separation, and enforces physics and spectrum constraints such as mass conservation and peak shifts. ABCC significantly outperforms state-of-the-art static, sequential, and generative baselines. ABCC even generalizes to unseen systems and enables interpretable downstream recovery of SEI formation pathways, supporting AI-driven electrochemical discovery. Benchmark (surveying) ; Infrared ; Constraint (computer-aided design) ; Infrared spectroscopy ; Sequence (biology) ; Generative model ; Flow (mathematics) ; Electrolyte Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Tong Zhao | Yan Zeng
Source: Small Methods (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Active Learning‐Guided Polymorph Control in Co‐Precipitation Synthesis Phase control of polymorphic materials is important yet challenging due to the vast synthesis parameter space and the sensitivity of certain phases to specific conditions. Recent advances in integrating artificial intelligence with laboratory automation offer promising solutions to address experimentation challenges like this. In this work, we developed an active learning-guided robotic synthesis workflow to achieve phase control during co-precipitation synthesis. This workflow was demonstrated using FeC<sub>2</sub>O<sub>4</sub>·2H<sub>2</sub>O, a polymorphic compound with diverse applications. The optimal synthesis conditions for obtaining pure α-FeC<sub>2</sub>O<sub>4</sub>·2H<sub>2</sub>O were identified using Bayesian optimization. Building on this, an active learning-guided workflow that can predict phase outcomes based on given synthesis parameters was showcased, enabling more efficient exploration of selective synthesis. The influence of synthesis parameters on the morphology of FeC<sub>2</sub>O<sub>4</sub>·2H<sub>2</sub>O was also preliminarily examined. This study highlights how artificial intelligence with robotic synthesis can accelerate the uncovering of synthesis-phase relationships and advance controllable material synthesis. Workflow ; Automation ; Phase (matter) ; Sensitivity (control systems) ; Control (management) ; Bayesian probability Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Harsh Parmar
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

Brahmha: A Computational Framework for Intent-Driven Matter Synthesis Using Physical Tokens Brahma is a theoretical computational framework that proposes the transformation of human intent into physical reality through programmable matter. Inspired by transformer-based artificial intelligence systems, the framework introduces Physical Tokens (PTs), the Matter Encoding Language (MEL), and a Matter Compiler capable of reasoning across physics, chemistry, and material science simultaneously. Rather than creating matter, Brahma operates strictly within the laws of physics, focusing on rearranging and structuring existing matter through computational intent. This paper establishes the foundational architecture, simulation approach, and research roadmap for intent-driven matter synthesis, positioning Brahma as a unifying paradigm for computation across both digital and physical domains. This work is presented as a preprint for academic discussion, interdisciplinary collaboration, and future experimental validation. Compiler ; Encoding (memory) ; Computational model ; Transformation (genetics) ; Computation ; Preprint ; Structuring Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Micro and Nano Robotics
Authors: Mohammad Amin Akhound | Tara M. Boland | Mikkel Ohm Sauer | Matthias Batzill | Moses Abraham Bokinala | Stela Canulescu | Yury Gogotsi | Hofmann, Philip | András Kis | Jiong Lu | Thomas Michely | Søren Raza | Wencai Ren | Joshua Robinson | Zdenek Sofer | Jing Teng | Søren Ulstrup | Meng Zhao | Xiaoxu Zhao | Jens J. Mortensen | Thomas Olsen | Kristian S. Thygesen
Source: Open MIND (preprint, 2026-03-05)
Field: Materials Science — Materials Chemistry

Large-scale Integration of Experimental and Computational Data for 2D Materials The past decade has seen rapid growth in the number of experimentally realized two-dimensional (2D) materials with diverse chemical and physical properties. However, information on their crystal structure, synthesis routes, and measured or predicted properties, remains scattered across thousands of publications. Here we consolidate this fragmented knowledge by establishing X2DB - an open infrastructure that integrates experimental and computational data on 2D materials. Using extensive literature mining and direct community uploads, we identify 370 unique 2D materials that have been realized in monolayer or few-layer form, and link them to their digital counterparts in computational databases, enabling consistent ab initio characterization of their properties across monolayer, bilayer and bulk forms. We describe the structure and content of the database highlighting its support for community uploads, illustrate how it can be used to generate new scientific insight and introduce a hierarchical classification of the known set of 2D materials. Our work provides a foundation for the integration and cross-fertilization of experimental and theoretical knowledge, opening new avenues for data-driven, predictive synthesis of novel 2D materials. Set (abstract data type) ; Characterization (materials science) ; Experimental data ; Ab initio ; Work (physics) ; Foundation (evidence) 2D Materials and Applications ; Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: Zichen Cao | Yilin Wang | Yu L.L. Luo | Tian Lan | Yan Long Zhang | Zejian Xu | Xiaonan Sui
Source: Trends in Food Science & Technology (article, 2026-02-17)
Field: Materials Science — Biomaterials

Food-derived amyloid fibrils: Precision characterization strategies for source diversity and functional complexity Characterization (materials science) ; Amyloid fibril ; Functional diversity ; Bridging (networking) ; Fibril ; Amyloid (mycology) Supramolecular Self-Assembly in Materials ; Alzheimer's disease research and treatments ; Seaweed-derived Bioactive Compounds
Authors: Ning Linghu | X. Jiang | Jing Xu
Source: Carbon Energy (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Functionalizing Conductive Diamond: Recent Advance in Fabrication, Modifications, and Applications ABSTRACT Conductive diamond, especially boron‐doped diamond, has gained tremendous attention due to its high stability, broad potential window, low background current, good biocompatibility, and tunable surface properties. Over the past 5 to 10 years, significant progress has been made in the synthesis and modification of conductive diamond, positioning it as a promising functional material in various electrochemical applications. This review covers synthesis methods, such as high‐pressure high‐temperature and chemical vapor deposition, highlighting their role in controlling diamond growth, microstructure, and doping. Modification strategies, including boron, nitrogen, and phosphorus doping, as well as surface terminations, crystal orientation, stress engineering, and hybridization, are discussed in terms of enhancing their electrochemical properties and expanding applications. Conductive diamond shows promise in energy storage, electrocatalysis, electrosynthesis, environmental remediation, and biosensing, particularly in supercapacitors, water treatment, and electrical detectors, owing to its robustness and stability. The review also discusses future directions, focusing on AI‐driven process optimization, advanced modifications, and the development of multifunctional diamond composites. This review aims to highlight the potential of conductive diamond in next‐generation electrochemical and energy technologies. Diamond ; Electrical conductor ; Surface modification ; Electrically conductive ; Electrochemistry Diamond and Carbon-based Materials Research ; Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion
Authors: Jing Zhang | Jianbing Wang | Yi Fang | W. Li
Source: Carbon and Hydrogen (article, 2026-03-30)
Field: Chemistry — Spectroscopy

Fabrication, Modification, and Applications of Functional Aerogels: A Review ABSTRACT Aerogels, characterized by a highly porous three‐dimensional nanoscale structure, possess exceptional properties such as ultra‐low density, high specific surface area, and remarkably low thermal conductivity. This review comprehensively summarizes the literature in the field of aerogel research over the past 5 years, including the latest advancements and recent developments in the fabrication, modification, and application of functional aerogels. Key preparation methods—including sol‐gel processing, supercritical drying, ambient pressure drying, and freeze‐drying—are compared in terms of their advantages and limitations. We also discuss post‐synthesis modification strategies, such as surface chemical modification, nanoparticle doping, and polymer coating, which are crucial for tailoring the function of aerogels. The modified aerogels exhibit outstanding thermal, acoustic, optical, and electrical properties, enabling their use in diverse fields such as thermal insulation, environmental remediation (e.g., oil–water separation, heavy metal adsorption), chemical separation, and emerging biomedical applications such as drug delivery systems. Finally, the review addresses current challenges like production costs and mechanical strength, while highlighting future prospects involving novel biomass‐based and graphene‐based aerogels, sustainable recycling approaches, and the promising integration of artificial intelligence to accelerate material design and optimization. Supercritical fluid ; Aerogel ; Function (biology) ; Surface modification ; High pressure Aerogels and thermal insulation ; Subcritical and Supercritical Water Processes ; Solar-Powered Water Purification Methods
Authors: Yu Fang | Qi Shi | Jinping Qu | Xiang Lu
Source: Polymer (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Single molecule coupling of light and thermal for programmable energy storage Isomerization ; Photoisomerization ; Molecule ; Density functional theory ; Photothermal therapy ; Molecular engineering ; Molecular electronics ; Chromophore ; Thermal energy Photochromic and Fluorescence Chemistry ; Phase Change Materials Research ; Synthesis and Properties of Aromatic Compounds
Authors: DEARDAO DeSci Collaborative Team | Yao Ding
Source: arXiv (Cornell University) (preprint, 2026-01-02)
Field: Materials Science — Materials Chemistry

AI-Guided Computational Design of a Room-Temperature, Ambient- Pressure Superconductor Candidate: Grokene We introduce Grokene, a novel two-dimensional superlattice derived from graphene, which was identified through an AI-guided materials discovery workflow utilizing a large language model. Grokene is predicted to exhibit ambient-pressure, room-temperature superconductivity, with computational simulations revealing a high electron-phonon coupling constant and a substantial logarithmic-averaged phonon frequency (~1650 K), leading to a mean-field critical temperature of approximately 325 K. Full isotropic Eliashberg solutions further support a critical temperature around 310 K, underscoring its strong potential for room-temperature superconductivity. However, the strict two-dimensional nature of Grokene introduces phase fluctuations, limiting the observable superconducting transition to a Berezinskii-Kosterlitz-Thouless (BKT) temperature of about 120 K in monolayers. To elevate TBKT toward room temperature, strategies such as few-layer stacking, substrate or gate engineering, and optimization of superlattice structure and doping levels are proposed. Our integrated workflow, combining AI-driven materials discovery with advanced many-body theories (DFPT/EPW, Eliashberg, and RPA), provides a systematic and reproducible framework for exploring novel superconductors. We suggest that experimental synthesis and comprehensive characterization of Grokene will be essential to assess these computational predictions and to explore routes toward practical superconductivity under ambient pressure. Superconductivity ; Superlattice ; Workflow ; Coupling (piping) ; Observable ; Isotropy ; Limiting ; Ambient pressure ; Phonon Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: DEARDAO DeSci Collaborative Team | Yao Ding
Source: ArXiv.org (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

AI-Guided Computational Design of a Room-Temperature, Ambient- Pressure Superconductor Candidate: Grokene We introduce Grokene, a novel two-dimensional superlattice derived from graphene, which was identified through an AI-guided materials discovery workflow utilizing a large language model. Grokene is predicted to exhibit ambient-pressure, room-temperature superconductivity, with computational simulations revealing a high electron-phonon coupling constant and a substantial logarithmic-averaged phonon frequency (~1650 K), leading to a mean-field critical temperature of approximately 325 K. Full isotropic Eliashberg solutions further support a critical temperature around 310 K, underscoring its strong potential for room-temperature superconductivity. However, the strict two-dimensional nature of Grokene introduces phase fluctuations, limiting the observable superconducting transition to a Berezinskii-Kosterlitz-Thouless (BKT) temperature of about 120 K in monolayers. To elevate TBKT toward room temperature, strategies such as few-layer stacking, substrate or gate engineering, and optimization of superlattice structure and doping levels are proposed. Our integrated workflow, combining AI-driven materials discovery with advanced many-body theories (DFPT/EPW, Eliashberg, and RPA), provides a systematic and reproducible framework for exploring novel superconductors. We suggest that experimental synthesis and comprehensive characterization of Grokene will be essential to assess these computational predictions and to explore routes toward practical superconductivity under ambient pressure. Superconductivity ; Superlattice ; Workflow ; Coupling (piping) ; Observable ; Isotropy ; Limiting ; Ambient pressure ; Phonon Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Eisuke F. Sato | Akine Tani | Tatsuo Nakahama | Koichi Mitsudo | Seiji Suga
Source: European Journal of Organic Chemistry (article, 2026-04-03)
Field: Chemistry — Organic Chemistry

Informatics‐Driven and Automated Optimization in Flow Electrochemical Synthesis Electrochemical synthesis has emerged as a powerful platform for environmentally sustainable chemical transformations. When integrated with flow chemistry, electrosynthetic processes exhibit enhanced scalability, making them suitable for industrial applications. Recently, the integration of electrochemical flow systems with informatics techniques has accelerated the optimization of reaction conditions. Data‐driven strategies facilitate rapid exploration of multidimensional parameter spaces, enabling identification of optimal reaction conditions with high efficiency. These advances have enabled the development of automated optimization systems. This review highlights recent progress in combining electrosynthesis, flow chemistry, and computational tools, focusing on representative examples that illustrate efficient optimization protocols and autonomous reaction development. By showcasing these developments, we discuss how the integration of these technologies is driving innovation in electrochemical synthesis. Identification (biology) ; Process integration ; Electrochemistry ; Flow (mathematics) ; Flow chemistry ; Continuous flow ; Reaction conditions ; System integration Radical Photochemical Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation ; Electrocatalysts for Energy Conversion
Authors: Vladislav Antonovich Mints | Jack K Pedersen | Gustav K. H. Wiberg | Jens Edelvang-Pejrup | Kirsten M. Ø Jensen | Divyansh Gautam | Jan Rossmeisl | Matthias Arenz
Source: Open Access CRIS of the University of Bern (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Learning in higher dimensions: a strategy for alloy electrocatalyst discovery. In this work, we demonstrate the inversion of the classical bottom-up approach to drive the discovery of improved energy conversion electrocatalysts top-down. Starting with complex alloy catalysts of many constituents, we down-select to optimal materials by removing low-performing elements from the alloy. The efficiency of this data-driven approach arises from the fact that when studying many elements together in one material, information is also obtained on the less complex alloys that contain fewer constituents. Therefore, the number of experiments required to study the complex alloy is fewer than those needed for studying all constituent alloys individually. In addition, this top-down approach allows for a new way of comparing activity models constructed from experimental data with theoretical simulations. We introduce the approach by studying the Au-Ir-Os-Pd-Pt-Re-Rh-Ru high entropy alloy (HEA) composition space for the acidic oxygen reduction reaction (ORR). By studying 200 compositions, we created a machine-learned activity model and provide evidence that the model can predict the activity of underlying, less complex compositions that are contained in the Au-Ir-Os-Pd-Pt-Re-Rh-Ru HEA composition space. Alloy ; Electrocatalyst ; High entropy alloys ; Oxygen reduction ; Complex system ; Entropy (arrow of time) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Materials Characterization Techniques
Authors: Anouar Bouanga | Soumaya El Assri | Rachid Bouharroud | Abdeslam Asehraou | Viktória Lilla Balázs | Aleksandar Széchenyi | Virginia Hock | R. Sabbahi
Source: International Journal of Biological Macromolecules (article, 2026-03-08)
Field: Materials Science — Biomaterials

Chitosan at the crossroads: Engineering sustainable biofunctional materials for health, food, and environmental resilience Resilience (materials science) ; Chitosan ; Sustainability ; Sustainable development ; Psychological resilience Nanocomposite Films for Food Packaging ; Advanced Cellulose Research Studies ; Polymer-Based Agricultural Enhancements
Authors: Yu-Chien Huang | Dennis Chung-Yang Huang | Yun-Cheng Tsai
Source: arXiv (Cornell University) (preprint, 2026-01-20)
Field: Materials Science — Materials Chemistry

Automated Analysis of DFT Output Files for Molecular Descriptor Extraction and Reactivity Modeling Understanding the relationship between molecular structure and chemical reactivity or properties is fundamental to rational molecular design. Linear free energy relationships (LFERs), particularly Hammett analysis, have long served as powerful tools in organic chemistry. Recently, these approaches have been enhanced by incorporating computationally derived parameters, enabling broader applicability across diverse molecules and reactions. To facilitate and scale this process, we present DFTDescriptorPipeline, a fully automated workflow for extracting quantum chemical descriptors from Gaussian log files and constructing structure-property and structure-reactivity relationships using multivariate linear regression (MLR) models. We validate the workflow across four case studies, including photoswitchable molecules and catalytic reactions. In each case, the models provide interpretable results, demonstrating the versatility of this approach and its relevance to a wide range of chemical contexts. We anticipate that this platform will serve as a generalizable framework for integrating quantum chemical calculations into data-driven molecular design. Workflow ; Quantum chemical ; Relevance (law) ; Molecular descriptor ; Range (aeronautics) ; Gaussian ; Scale (ratio) ; Linear model ; Reactivity (psychology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Radical Photochemical Reactions