jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Henry Kelbrick Pentz | Thomas Warford | Ivan Timokhin | Hongpeng Zhou | Qian Yang | Anupam Bhattacharya | Artem Mishchenko
Source: Communications Physics (article, 2025-01-17)
Field: Materials Science — Materials Chemistry

Elf autoencoder for unsupervised exploration of flat-band materials using electronic band structure fingerprints Two-dimensional materials with flat electronic bands are promising for realising exotic quantum phenomena such as unconventional superconductivity and nontrivial topology. However, exploring their vast chemical space is a significant challenge. Here we introduce elf, an unsupervised convolutional autoencoder that encodes electronic band structure images into fingerprint vectors, enabling the autonomous clustering of materials by electronic properties beyond traditional chemical paradigms. Unsupervised visualisation of the fingerprint space then uncovers hidden chemical trends and identifies promising candidates based on similarities to well-studied exemplars. This approach complements high-throughput ab initio methods by rapidly screening candidates and guiding further investigations into the mechanisms underlying flat-band physics. The elf autoencoder is a powerful tool for autonomous discovery of unexplored flat-band materials, enabling unbiased identification of compounds with desirable electronic properties across the 2D chemical space. Autoencoder ; Pattern recognition (psychology) ; Artificial intelligence ; Materials science ; Computer science ; Deep learning Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Thermal properties of materials
Authors: Jesse Angelis | Eva Ayla Schröder | Zixuan Xiao | Wassim Gabriel | Mathias Wilhelm
Source: PROTEOMICS (review, 2025-04-10)
Field: Chemistry — Spectroscopy

Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics, including, but not limited to, models for predicting digestibility, retention time, charge state distribution, collisional cross section, fragmentation ion intensities, and detectability. The combination of these models enables not only the in silico generation of spectral libraries but also finds many additional use cases in the design of targeted assays or data-driven rescoring. This review serves as both an introduction for newcomers and an update for experienced researchers aiming to develop accessible and reproducible models for peptide property predictions. Key limitations of the current models, including difficulties in handling diverse post-translational modifications and instrument variability, highlight the need for large-scale, harmonized datasets, and standardized evaluation metrics for benchmarking. Benchmarking ; Computer science ; Fragmentation (computing) ; Proteomics ; Artificial intelligence ; Deep learning ; Predictive modelling ; Property (philosophy) ; Machine learning ; In silico ; Mass spectrometry ; Data science ; Data mining ; Chemistry Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Jingru Gan | Peichen Zhong | Yong Du | Yanqiao Zhu | Chenru Duan | Haorui Wang | Daniel Schwalbe-Koda | Carla P. Gomes | Kristin A. Persson | Wei Wang
Source: ArXiv.org (preprint, 2025-02-28)
Field: Materials Science — Materials Chemistry

MatLLMSearch: Crystal Structure Discovery with Evolution-Guided Large Language Models Crystal structure generation is fundamental to materials science, enabling the discovery of novel materials with desired properties. While existing approaches leverage Large Language Models (LLMs) through extensive fine-tuning on materials databases, we show that pre-trained LLMs can inherently generate novel and stable crystal structures without additional fine-tuning. Our framework employs LLMs as intelligent proposal agents within an evolutionary pipeline that guides them to perform implicit crossover and mutation operations while maintaining chemical validity. We demonstrate that MatLLMSearch achieves a 78.38% metastable rate validated by machine learning interatomic potentials and 31.7% DFT-verified stability, outperforming specialized models such as CrystalTextLLM. Beyond crystal structure generation, we further demonstrate that our framework adapts to diverse materials design tasks, including crystal structure prediction and multi-objective optimization of properties such as deformation energy and bulk modulus, all without fine-tuning. These results establish our framework as a versatile and effective framework for consistent high-quality materials discovery, offering training-free generation of novel stable structures with reduced overhead and broader accessibility. Crystal structure prediction ; Leverage (statistics) ; Metastability ; Crossover ; Crystal structure ; Pipeline (software) ; Data structure ; Overhead (engineering) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Xuesong Wang | Nan He | Zidong Zhou | Jing Chen | Abdul Mateen | Yinfei Shao | Xiang Chen | Chengbin Liu | Altaf Mujear | Jianwei Zhang | Feng Liu | Zhihao Bao
Source: Renewable and Sustainable Energy Reviews (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

AI-driven exploration and design of inorganic battery materials Interpretability ; Battery (electricity) ; Bridge (graph theory) ; Big data ; Renewable energy ; Scalability Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies
Authors: D. G. Lee | Hiroshi Mizuseki | Jaewoong Choi | Byungju Lee
Source: Communications Materials (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Building an end-to-end battery recipe knowledge base via transformer-based text mining Recent studies have increasingly applied natural language processing to automatically extract experimental information from battery materials literature. Despite the complexity of battery manufacturing—from material synthesis to cell assembly—no comprehensive study has systematically organized this information. Here we present a language modeling-based protocol for extracting complete battery recipes from scientific papers. Using machine learning-based filtering and topic modeling, we identified 2174 relevant papers and extracted over 5800 paragraphs describing synthesis and assembly procedures. Deep learning-based named entity recognition models were trained to extract 30 entities with F1-scores of 88.18% and 94.61%. We also evaluated large language models, including GPT-4, using few-shot learning and fine-tuning. These results enabled the structured construction of 165 end-to-end recipes and identification of trends such as precursor–method associations. The resulting knowledge base supports flexible recipe retrieval and provides a scalable framework for organizing protocols across large volumes of publications, thereby accelerating literature review and data-driven battery design. The complex process of battery manufacturing lacks a systematic organization of information from material synthesis to cell assembly. Here, a language modeling-based protocol, Text-to-Battery Recipe, accurately extracts end-to-end battery recipes, offering a foundational knowledge base to accelerate literature review and battery design. Recipe ; End-to-end principle ; Transformer ; Knowledge base ; Computer science ; End user ; End-user development ; Engineering ; Electrical engineering ; Artificial intelligence ; World Wide Web ; History ; Archaeology Machine Learning in Materials Science
Authors: Lei Zhang | Hangyuan Deng
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

NJmat 2.0: User Instructions of Data-Driven Machine Learning Interface for Materials Science NJmat is a user-friendly, data-driven machine learning interface designed for materials design and analysis. The platform integrates advanced computational techniques, including natural language processing (NLP), large langua... | Find, read and cite all the research you need on Tech Science Press Computer science ; Interface (matter) ; Human–computer interaction ; World Wide Web ; Operating system Machine Learning in Materials Science
Authors: Xiao-Qi Han | Peng‐Jie Guo | Ze-Feng Gao | Hao Sun | Zhong-Yi Lu
Source: npj Computational Materials (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

InvDesFlow-AL: active learning-based workflow for inverse design of functional materials Developing inverse design methods for functional materials with specific properties is critical to advancing fields like renewable energy, catalysis, energy storage, and carbon capture. Generative models based on diffusion principles can directly produce new materials that meet performance constraints, thereby significantly accelerating the material design process. However, existing methods for generating and predicting crystal structures often remain limited by low success rates. In this work, we propose a novel inverse material design generative framework called InvDesFlow-AL, which is based on active learning strategies. This framework can iteratively optimize the material generation process to gradually guide it towards desired performance characteristics. In terms of crystal structure prediction, the InvDesFlow-AL model achieves an RMSE of 0.0423 Å, representing an 32.96% improvement in performance compared to existing generative models. Additionally, InvDesFlow-AL has been successfully validated in the design of low-formation-energy and low-Ehull materials. It can systematically generate materials with progressively lower formation energies while continuously expanding the exploration across diverse chemical spaces. Notably, through DFT structural relaxation validation, we identified 1,598,551 materials with Ehull < 50 meV, indicating their thermodynamic stability and atomic forces below 1e-4 eV/Å. These results fully demonstrate the effectiveness of the proposed active learning-driven generative model in accelerating material discovery and inverse design. To further prove the effectiveness of this method, we took the search for BCS superconductors under ambient pressure as an example explored by InvDesFlow-AL. As a result, we successfully identified Li2AuH6 as a conventional BCS superconductor with an ultra-high transition temperature of 140 K, and also discovered several other superconducting materials that surpass the theoretical McMillan limit and have transition temperatures within the liquid nitrogen temperature range. This discovery provides strong empirical support for the application of inverse design in materials science. Inverse ; Material Design ; Workflow ; Process (computing) ; Generative grammar ; Stability (learning theory) ; Generative model ; Generative Design ; Crystal (programming language) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Crystallography and molecular interactions
Authors: Hanna Türk | Davide Tisi | Michele Ceriotti
Source: PRX Energy (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Reconstructions and Dynamics of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>β</mml:mi></mml:math>-Lithium Thiophosphate Surfaces Lithium thiophosphate (LPS) is a promising solid electrolyte for next-generation lithium-ion batteries due to its superior energy storage, high ionic conductivity, and low-flammability properties. Despite its potential, the high reactivity of LPS with common contaminants such as atmospheric water, preparation solvents, and electrode materials poses significant challenges for commercialization. The lack of understanding regarding the structure, morphology, and chemical behavior of the surface of LPS slows down the search for solutions to these issues. Here, we utilize a machine-learning interatomic potential to achieve a fundamental atomistic understanding of the mechanical and chemical properties of the <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mi>β</a:mi></a:math>-<c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"><c:mi>Li</c:mi></c:math><e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"><e:msub><e:mi/><e:mn>3</e:mn></e:msub></e:math><g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"><g:mi>PS</g:mi></g:math><i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline"><i:msub><i:mi/><i:mn>4</i:mn></i:msub></i:math> surfaces. Employing molecular-dynamics simulations, we identify relevant complexions formed by surface reconstructions, determine their surface energies, and compute the Wulff shape of <k:math xmlns:k="http://www.w3.org/1998/Math/MathML" display="inline"><k:mi>β</k:mi></k:math>-LPS. The most stable complexions exhibit properties distinctly different from the bulk, including amorphization, increased density, decreased conductivity, and large deformation of the structural building blocks. We demonstrate that these surfaces are not static but undergo significant dynamical activity at room temperature, which is clearly identified by an analysis featuring a time-averaged structural descriptor. Finally, we examine the changes of the electronic structure induced by the surface complexions as well as their effect on the reaction enthalpy with water, which provides us with details on changes in surface reactivity and active sites, underlining the importance of investigating surface complexions under realistic conditions. Thiophosphate ; Mathematics ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Ahmet Alperen Palabıyık
Source: Applied Biochemistry and Biotechnology (review, 2025-07-05)
Field: Materials Science — Materials Chemistry

Synzymes: The Future of Modern Enzyme Engineering Synzymes, synthetic mimics of natural enzymes, have emerged as a promising frontier in modern biocatalysis due to their enhanced stability, adaptability, and catalytic performance. Unlike natural enzymes, synzymes are engineered to function under extreme physicochemical conditions, making them suitable for a broad range of applications in biomedicine, industrial biotechnology, and environmental remediation. This review provides a comprehensive overview of the structural principles, functional mechanisms, and real-world applications of synzymes. Particular attention is given to their role in targeted drug delivery, biosensing, green manufacturing, and pollutant degradation. Furthermore, the integration of artificial intelligence and high-throughput screening technologies has accelerated synzyme design, enabling more efficient and cost-effective development pipelines. By evaluating both the innovations and current limitations in synzyme research, this article outlines the growing potential of synthetic enzymes as next-generation tools for sustainable and precision-driven solutions. Biochemical engineering ; Adaptability ; Biomedicine ; Synthetic biology ; Nanotechnology ; Computer science ; Biotechnology ; Risk analysis (engineering) ; Engineering ; Computational biology ; Biology ; Materials science ; Bioinformatics ; Business ; Ecology Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Enzyme Catalysis and Immobilization
Authors: Udita Das | Ankita Das | Ruhi Das | Asim K. Das
Source: Reviews in Inorganic Chemistry (article, 2025-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nanotechnology of colour: quantum dots (QDs), photonic crystals (PCs) and plasmonic nanoparticles Abstract This tutorial min review discusses the important features of nanotechnology of colour generation by the quantum dots (QDs), nanostructured photonic crystals (PCs) and plasmonic metal nanoparticles. Quantum dots (QDs) are characterized by the tunable and size dependent strong fluorescence emission properties. Photonic crystals (PCs) generate the material independent structural colour ( iridescent colour dependent on the angle of observation ) due to the constructive interference arising from Bragg diffraction by the nanostructured photonic crystals (PCs) producing the diffraction gratings. It causes the vibrant colour in the opals and pearls, wings of peacocks and butterflies, in the necks of pigeons, variable colours in chameleons, etc. Plasmonic nanoparticles like Au-, Ag- and Cu-nanoparticles show the both light absorption and scattering properties that can be tuned by varying the size, morphology and composition of the nanoparticles. Structural colour ( iridescent colour ) by PCs is used by nature while colouring by QDs and plasmonic nanoparticles are due to the man made activities. The nanotechnology based colour is superior to the organic pigment based colour in terms of brightness and photostability and this field is an emerging area of research with a great promise in terms of applications in various fields. Quantum dot ; Nanoparticle ; Chemistry ; Plasmon ; Nanotechnology ; Photonics ; Plasmonic nanoparticles ; Optoelectronics ; Materials science Gold and Silver Nanoparticles Synthesis and Applications ; Quantum Dots Synthesis And Properties ; Photonic Crystals and Applications
Authors: Yuhui Wu | Minmin Ding | Haonan He | Qijun Wu | Shaohua Jiang | Peng Zhang | Jian Ji
Source: npj Computational Materials (article, 2025-08-25)
Field: Materials Science — Materials Chemistry

A versatile multimodal learning framework bridging multiscale knowledge for material design Artificial intelligence has achieved remarkable success in materials science, accelerating novel material design. However, real-world material systems exhibit multiscale complexity—spanning composition, processing, structure, and properties—posing significant challenges for modeling. While some approaches fuse multiscale features to improve prediction, important modalities such as microstructure are often missing due to high acquisition costs. Existing methods struggle with incomplete data and lack a framework to bridge multiscale material knowledge. To address this, we propose MatMCL, a structure-guided multimodal learning framework that jointly analyzes multiscale material information and enables robust property prediction with incomplete modalities. Using a self-constructed multimodal dataset of electrospun nanofibers, we demonstrate that MatMCL improves mechanical property prediction without structural information, generates microstructures from processing parameters, and enables cross-modal retrieval. We further extend it via multi-stage learning and apply it to nanofiber-reinforced composite design. MatMCL uncovers processing-structure-property relationships, suggesting its promise as a generalizable approach for AI-driven material design. Bridging (networking) ; Computer science ; Multiscale modeling ; Human–computer interaction ; Chemistry Machine Learning in Materials Science ; Polymer composites and self-healing ; Additive Manufacturing and 3D Printing Technologies
Authors: Zhuo Su | Bingling Dai | Xue Wang | Yibin Jiang | Wenbin Lin | Cheng Wang
Source: Angewandte Chemie International Edition (article, 2025-05-07)
Field: Chemistry — Inorganic Chemistry

Machine Learning Reveals In‐Cavity Versus Surface Activity for Selective C─H Borylation by Metal‐Organic Framework Catalysts Metal-organic frameworks (MOFs) provide an expansive and tunable platform for heterogeneous catalysis, yet distinguishing between catalytic reactions occurring within their pores and those on their external surfaces remains a challenge. This study employs interpretable machine learning to elucidate structure-activity relationships in MOF-supported nickel (Ni) catalysts for selective sp<sup>3</sup> and sp<sup>2</sup> C─H borylation. By analyzing over 470 000 MOF structures, we developed a set of 45 concise and chemically meaningful descriptors that capture key structural variations across MOFs. These descriptors enabled us to identify the critical factors governing sp<sup>3</sup> versus sp<sup>2</sup> selectivity, revealing distinct activation mechanisms: sp<sup>3</sup> C─H borylation preferentially occurs within MOF cavities via a radical-mediated hydrogen atom transfer (HAT) mechanism, whereas sp<sup>2</sup> C─H borylation is associated with surface or defect sites, favoring a concerted metalation-deprotonation (CMD) pathway. Guided by these insights, we designed Ni catalysts that achieve up to 97.8% sp<sup>3</sup> selectivity and 88.7% sp<sup>2</sup> selectivity. This work provides a systematic framework for rational catalyst design and establishes generalizable principles for controlling activity preference in MOF-supported catalysis. Borylation ; Catalysis ; Selectivity ; Chemistry ; Metal-organic framework ; Metalation ; Combinatorial chemistry ; Stereochemistry ; Organic chemistry ; Aryl ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Advanced Photocatalysis Techniques
Authors: Syafira Masri | Nurulhuda Mohd | Noor Hayaty Abu Kasim | Masfueh Razali
Source: International Journal of Molecular Sciences (review, 2025-07-20)
Field: Materials Science — Polymers and Plastics

Mechanistic Insight into the Antioxidant and Antimicrobial Activities of Palm Oil-Derived Biomaterials: Implications for Dental and Therapeutic Applications Palm oil is a highly versatile natural resource that has gathered significant attention due to its bioactive properties, particularly its antimicrobial and antioxidant effects. Rich in tocotrienols, tocopherols, and carotenoids, palm oil exhibits potent antioxidant activity, while its fatty acid content and other bioactive molecules contribute to its antimicrobial efficacy against various pathogens. The underlying mechanisms of action driving these bioactivities involve intricate molecular interactions, biochemical pathways, and redox processes, which influence microbial cell function and oxidative stress reduction. This review provides a critical analysis of the current mechanistic understanding of palm oil's biofunctional properties, emphasizing its potential incorporation into engineered biomaterials. Particular focus is given to the chemical composition, reaction pathways, and synergistic potential of palm oil derivatives in material-based formulations. Furthermore, the potential applications of palm oil as a standalone or synergistic agent in novel therapeutic and industrial formulations are explored. By elucidating the mechanistic basis of its bioactivity within material contexts, this review highlights palm oil's promising role in the development of advanced functional materials for pharmaceutical and dental technologies. Antimicrobial ; Palm oil ; Antioxidant ; Biotechnology ; Traditional medicine ; Medicine ; Biochemical engineering ; Chemistry ; Biology ; Food science ; Microbiology ; Engineering ; Biochemistry Polymer Synthesis and Characterization ; Thermal and Kinetic Analysis ; Medicinal Plant Studies