jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Di Zhang | Hao Li
Source: Molecular Chemistry & Engineering (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

The hidden engine of AI in electrocatalysis: Databases and knowledge graphs at work The development of efficient electrocatalysts is crucial for advancing renewable energy technologies, such as hydrogen production, carbon dioxide reduction, and fuel cells. However, the complexity of electrocatalytic systems and the vast chemical space of potential materials present significant challenges for traditional discovery methods. Databases and knowledge graphs have emerged as indispensable tools in this domain, enabling the fine-tuning of large language models (LLMs) and the development of robust machine learning (ML) models. By organizing and integrating vast amounts of experimental and computational data, these tools facilitate the extraction of relevant knowledge, improve model performance, and accelerate the discovery of high-performance electrocatalysts. This perspective explores the synergistic role of databases and knowledge graphs in electrocatalyst discovery, focusing on their contributions to LLM fine-tuning, ML model development, and the creation of predictive tools. We also discuss the challenges associated with data quality, standardization, and integration, and propose strategies to maximize the potential of these tools in AI-driven electrocatalysis research. • A systematic workflow is proposed to construct physically meaningful knowledge graphs from electrocatalysis databases. • Overview and comparison of databases: DigCat, ElectroCat, Catalysis-Hub, and others. • DFT-based descriptors are integrated into knowledge graphs for mechanistic insights. • A unified framework links experiments, theory, and AI via knowledge graphs. • Key challenges in data quality, AI errors, extraction, and sustainability are discussed. Computer science ; Electrocatalyst ; Work (physics) ; Database ; Information retrieval ; Data science ; Engineering ; Chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Nida Katı | Ferhat Uçar
Source: Journal of Energy Storage (article, 2025-08-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Investigation of prediction approaches for the design and performance analysis of supercapacitors with biomass-based activated carbon electrodes This article presents an innovative approach integrating biomass valorization and artificial intelligence for developing high-performance supercapacitor electrodes. Apricot kernel shells were converted into activated carbon through controlled hydrothermal carbonization at varying durations (24, 36, and 48 h) followed by KOH activation. 48-hour processed sample exhibited exceptional characteristics with a BET surface-area of 976.650 m 2 /g and superior electrochemical performance. Comprehensive characterization through SEM, EDS, XRD, and FTIR analyses revealed the evolution of porous structures and surface chemistry modifications during activation process. Electrochemical testing demonstrated that optimized electrode achieved a specific capacitance of 210 F/g at 1 A/g current density, with excellent charge–discharge symmetry and reversibility. A novel aspect of this configuration is the development of an XGBoost-based machine learning framework for predicting supercapacitor performance, achieving an R 2 score of 0.991 and RMSE of 0.042. Proposed predictive model, validated through extensive residual analysis and normal probability assessment, enables rapid screening of potential electrode materials. Successful integration of sustainable biomass processing with advanced machine learning techniques establishes an ecosystem for efficient development of next-generation energy storage devices. Findings demonstrate the viability of agricultural waste valorization for high-performance supercapacitor applications while introducing a computational approach that significantly accelerates the optimization of electrode materials. • High-performance supercapacitor electrodes from apricot kernel shells. • Controlled hydrothermal carbonization and KOH activation. • Optimized electrode achieved 210 F/g at 1 A/g. • Machine learning framework for performance prediction. • Sustainable energy storage solution. Supercapacitor ; Activated carbon ; Biomass (ecology) ; Electrode ; Carbon fibers ; Nanotechnology ; Process engineering ; Materials science ; Engineering ; Chemistry ; Electrochemistry ; Biology ; Ecology ; Composite material ; Organic chemistry Supercapacitor Materials and Fabrication ; Advanced Battery Technologies Research ; Electric and Hybrid Vehicle Technologies
Authors: Danny Pérez | Aparna P. A. Subramanyam | Ivan Maliyov | Thomas D. Swinburne
Source: npj Computational Materials (article, 2025-08-16)
Field: Materials Science — Materials Chemistry

Uncertainty quantification for misspecified machine learned interatomic potentials Abstract The use of high-dimensional regression techniques from machine learning has significantly improved the quantitative accuracy of interatomic potentials. Atomic simulations can now plausibly target quantitative predictions in a variety of settings, which has brought renewed interest in robust means to quantify uncertainties. In many practical settings where model complexity is constrained (e.g., due to performance considerations), misspecification — the inability of any one choice of model parameters to exactly match all training data — is a key contributor to errors that is often disregarded. Here, we employ a recent misspecification-aware regression technique to quantify parameter uncertainties, which is then propagated to a broad range of phase and defect properties in tungsten. The propagation is performed through both brute-force resampling and implicit Taylor expansion. The propagated misspecification uncertainties robustly quantify and bound errors on a broad range of material properties. We demonstrate application to recent foundational machine learning interatomic potentials, accurately predicting and bounding errors in MACE-MPA-0 energy predictions across the diverse materials project database. Computer science Machine Learning in Materials Science ; Fault Detection and Control Systems ; Computational Drug Discovery Methods
Authors: Hsiao-Kang Ma | Zhaoyang Wang | Meichen Pan | Fengxia Hu | Guangdong Sun | Xiuling He
Source: APL Materials (article, 2025-05-01)
Field: Materials Science — Biomaterials

Beyond biomedical frontiers: Multidimensional applications of silk and its derived proteins in energy, environment, and agri-food systems As a natural protein-based biopolymer with millennia of application history, silk has evolved into a transformative material platform bridging sustainable technology and biological design. This review systematically examines the molecular origins and processing innovations of silk-derived proteins, focusing on five core material attributes: biocompatibility, controlled biodegradation, mechanical adaptability, optical transparency, and processability. Recent breakthroughs in genetic engineering (CRISPR-edited silkworms) and nanocomposite fabrication (carbon nanotube hybrids) are critically analyzed for their roles in overcoming historical limitations in batch consistency and functional integration. The application survey spans (1) flexible electronics utilizing silk’s dielectric tunability for epidermal sensors, (2) biophotonic devices exploiting humidity-responsive optical modulation, (3) environmental technologies for air/water purification, and (4) agricultural innovations through silkworm by-product valorization. Industrial translation challenges are methodically addressed, including sterilization-induced denaturation, long-term storage stability, and scalability constraints in electrospinning processes. Emerging opportunities in intelligent food packaging and implantable drug delivery systems are highlighted, with particular emphasis on closed-loop production models that transform sericulture waste into high-value biomaterials. The review concludes by outlining synergistic research directions combining synthetic biology, machine learning, and green chemistry to advance silk’s role in sustainable manufacturing paradigms. SILK ; Materials science ; Polymer science ; Nanotechnology Silk-based biomaterials and applications ; Insect Utilization and Effects ; Biochemical and Structural Characterization
Authors: Mahima Samanth | Pavan Hiremath | G. Divya Deepak | Nithesh Naik | Ach. Fauzan HS | Srinivas Shenoy Heckadka | R. C. Shivamurthy
Source: SSRN Electronic Journal (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Sustainable Composites from Sugarcane Bagasse Fibers and Bio-Based Epoxy with Insights into Wear Performance, Thermal Stability, and Machine Learning Predictive Modeling Bagasse ; Epoxy ; Thermal stability ; Composite material ; Materials science ; Thermal ; Stability (learning theory) ; Pulp and paper industry ; Computer science ; Machine learning ; Engineering ; Chemical engineering Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Tribology and Wear Analysis
Authors: Tao Pang | Xingyu Tao | Peng‐Yu Zhuang | Mengqi Wu | Jinlong Li | Hong Huang | Jianping Sun | Jingru Liu
Source: Journal of Fluorescence (article, 2025-03-29)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Carbon Quantum Dot-Enhanced Fluorescent Probe for the Detection of Zn2+ in Sweat Chemistry ; Fluorescence ; Nanotechnology ; Quantum dot ; Detection limit ; Biological system ; Chromatography ; Materials science Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Ching-Chien Chen | Robert J. Appleton | Saswat Mishra | Kat Nykiel | Alejandro Strachan
Source: npj Computational Materials (article, 2025-06-20)
Field: Materials Science — Materials Chemistry

Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning Pressure-induced phase transformations in materials are of interest in a range of fields, including geophysics, planetary sciences, and shock physics. In addition, the high-pressure phases can exhibit desirable properties, eliciting interest in materials science. Despite its importance, the process of finding new high-pressure phases, either experimentally or computationally, is time-consuming and often driven by intuition. In this study, we use graph neural networks trained on density functional theory (DFT) equation of state data of 2258 materials and 7255 phases to identify potential phase transitions. The model is used to explore possible phase transitions in 7677 pairs of phases and promising cases are confirmed or denied via DFT calculations. Importantly, the new data is added to the training set, the model is refined, and a new cycle of discovery is started. Within 13 iterations, we discovered 28 new high-pressure stable phases (never synthesized through high-pressure routes nor reported in high-pressure computational works) and rediscovered 18 pressure-induced phase transitions. The results provide new insight and classification of pressure-induced phase transitions in terms of the ambient properties of the phases involved. Computer science ; Throughput ; Artificial neural network ; High pressure ; Artificial intelligence ; Graph ; Deep learning ; Machine learning ; Theoretical computer science ; Engineering ; Engineering physics ; Telecommunications Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; High-pressure geophysics and materials
Authors: Yongbo Shi | Yu Bao | Shuo Cao | Ye Su | Ping Qian
Source: Chemistry of Materials (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Design of Transition Metal Dichalcogenides Using General-Purpose Machine-Learned Interatomic Potentials Exploring the general-purpose interatomic potential for describing multiple compounds is challenging. In this study, we confirm the applicability of machine learning methods. By fitting the density-functional theory (DFT) data set using a feedforward neural network, the general-purpose machine-learned interatomic potentials (GMIPs) for transition metal dichalcogenides (TMDCs) are obtained, which can effectively describe the interatomic interactions within MX2 (M = Mo and W; X = S, Se, and Te) compounds and MoX2/WX2 heterojunctions. GMIPs demonstrate a high degree of accuracy across a wide range of temperatures and strains. Subsequently, we confirm the effectiveness of the potential through GMIP-based molecular dynamics (MD) simulations. The mechanical behavior of these compounds and heterojunctions is analyzed in large supercells containing 30,000 and 48,000 atoms. Some key parameters, including Poisson’s ratio, elastic modulus, critical strain, and tensile strength, are revealed. The lattice thermal conductivity is predicted by both nonequilibrium molecular dynamics (NEMD) and homogeneous nonequilibrium molecular dynamics (HNEMD). Among all compounds, WS2 exhibits the highest thermal conductivity (168–207 W/mK), while MoTe2 shows the lowest thermal conductivity (21–35 W/mK). Moreover, the presence of VS, VMo, and VW vacancies in MoS2 and WS2 results in the degradation of mechanical properties and a decrease in thermal conductivity. Interatomic potential ; Molecular dynamics ; Thermal conductivity ; Non-equilibrium thermodynamics ; Transition metal ; Thermal ; Lattice (music) 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Alexander J. Hoffman | Mingrou Xie | Rafael Gómez‐Bombarelli
Source: Microporous and Mesoporous Materials (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Learning descriptors to predict organic structure-directing agent applicability in zeolite synthesis Zeolite ; Chemistry ; Chemical engineering ; Materials science ; Organic chemistry ; Catalysis ; Engineering Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Surface Chemistry and Catalysis
Authors: M. Maruf | Sungmin Kim | Zeeshan Ahmad
Source: The Journal of Physical Chemistry Letters (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Learning Long-Range Interactions in Equivariant Machine Learning Interatomic Potentials via Electronic Degrees of Freedom Machine learning interatomic potentials (MLIPs) provide a computationally efficient alternative to quantum mechanical simulations for predicting material properties. Message-passing graph neural networks, commonly used in these MLIPs, rely on local descriptor-based symmetry functions to model atomic interactions. However, such local descriptor-based approaches struggle with systems exhibiting long-range interactions, charge transfer, and compositional heterogeneity. In this work, we develop a new equivariant MLIP incorporating long-range Coulomb interactions through the explicit treatment of electronic degrees of freedom, specifically global charge distribution within the system. This is achieved using a charge equilibration scheme based on the predicted atomic electronegativities. We systematically evaluate our model across a range of benchmark periodic and nonperiodic data sets, demonstrating that it outperforms both short-range equivariant and long-range invariant MLIPs in energy and force predictions. Due to the explicit treatment of long-range interactions using partial charges, our model achieves higher accuracy using a 4 Å cutoff radius than a short-range model with a 6 Å cutoff. Our approach enables more accurate and efficient simulations of systems with long-range interactions and charge heterogeneity, expanding the applicability of MLIPs in computational materials science. Statistical physics ; Range (aeronautics) ; Coulomb ; Computer science ; Charge (physics) ; Invariant (physics) ; Degrees of freedom (physics and chemistry) ; Cutoff ; Equivariant map ; Physics ; Quantum mechanics ; Mathematics ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Luis H.M. Torres | Joel P. Arrais | Bernardete Ribeiro
Source: Pattern Recognition (article, 2025-04-15)
Field: Materials Science — Materials Chemistry

Rethinking transformers with convolution and graph embeddings for few-shot molecular property discovery The prediction of molecular properties is a critical step in drug discovery campaigns. Computational methods such as graph neural networks (GNNs) and Transformers have effectively leveraged the small-range and long-range dependencies in molecules to preserve the local and global patterns for multiple molecular property prediction tasks. However, the dependence of these models on large amounts of experimental data poses a challenge, particularly on smaller biological datasets prevalent across the drug discovery pipeline. This paper introduces FS-GCvTR, a few-shot graph-based convolutional Transformer architecture designed to predict chemical properties with a small amount of labeled compounds. The convolutional Transformer is presented as a crucial component, effectively integrating both local and global dependencies of molecular graph embeddings by propagating a set of convolutional tokens across Transformer attention layers for molecular property prediction. Furthermore, a few-shot meta-learning approach is introduced to iteratively adapt model parameters across multiple few-shot tasks while generalizing to new chemical properties with limited available data. Experiments including few-shot evaluations on multi-property datasets show that the FS-GCvTR model outperformed other few-shot graph-based baselines in specific molecular property prediction tasks. • A few-shot GNN-Transformer, FS-GCvTR is proposed for molecular property discovery. • A Convolutional Transformer learns local and global information in graph embeddings. • A meta-learning approach adapts FS-GCvTR across tasks to predict molecular properties. • Experiments show that FS-GCvTR outperforms standard graph-based methods. Convolution (computer science) ; Graph ; Property (philosophy) ; Computer science ; Shot (pellet) ; Transformer ; One shot ; Algorithm ; Mathematics ; Artificial intelligence ; Theoretical computer science ; Materials science ; Voltage ; Engineering ; Electrical engineering ; Mechanical engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Houliang Zhou | Benjamin Zalatan | Joan Delia Stanescu | Martin P. Harmer | J. M. Rickman | Lifang He | Christopher J. Marvel | Brian Y. Chen
Source: npj Computational Materials (article, 2025-03-27)
Field: Materials Science — Materials Chemistry

Learning to predict rare events: the case of abnormal grain growth Abnormal grain growth (AGG) in polycrystalline microstructures, characterized by the rapid and disproportionate enlargement of a few “abnormal” grains relative to their surroundings, can lead to dramatic, often deleterious changes in the mechanical properties of materials, such as strength and toughness. Thus, the prediction and control of AGG is key to realizing robust mesoscale materials design. Unfortunately, it is challenging to predict these rare events far in advance of their onset because, at early stages, there is little to distinguish incipient abnormal grains from “normal” grains. To overcome this difficulty, we propose two machine learning approaches for predicting whether a grain will become abnormal in the future. These methods analyze grain properties derived from the spatio-temporal evolution of grain characteristics, grain-grain interactions, and a network-based analysis of these relationships. The first, PAL (Predicting Abnormality with LSTM), analyzes grain features using a long short-term memory (LSTM) network, and the second, PAGL (Predicting Abnormality with GCRN and LSTM), supplements the LSTM with a graph-based convolutional recurrent network (GCRN). We validated these methods on three distinct material scenarios with differing grain properties, observing that PAL and PAGL achieve high sensitivity and precision and, critically, that they are able to predict future abnormality long before it occurs. Finally, we consider the application of the deep learning models developed here to the prediction of rare events in different contexts. Rare events ; Statistics ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Metallurgical Processes and Thermodynamics
Authors: Pedro S.F. Mendes | Núria López | Sandip De | R Fushimi | Ramazan Yıldırım | Annette Trunschke
Source: ChemCatChem (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Data as a Key Resource in Catalysis: A Community Account Abstract The deployment of artificial intelligence (AI) is transforming the scientific fields central to interdisciplinary catalysis research. By enabling more effective use of data, AI (including simpler machine learning and data science tools) holds great promise for accelerating discoveries. However, progress has so far been modest, largely due to the lack of standardized, machine‐readable, and openly shared catalysis data. This perspective, accounting for community insights emerging at conferences, analyses the underlying reasons for these challenges and proposes solutions to a future whereFAIR data management becomes an integral part of research in catalysis. In the short‐term, we deem that mandatory FAIR data depositing prior to scientific publications along with consensualized top‐down guidelines on data sharing powered by ease‐to‐use tools can make the necessary step change happen to catalyse data as key resource in our community. Software deployment ; Key (lock) ; Data sharing ; Resource (disambiguation) ; Data management ; Shared resource Machine Learning in Materials Science ; Research Data Management Practices ; Scientific Computing and Data Management
Authors: Jhamil G. Gutierrez | Jocelyn F. Villaverde
Source:  (article, 2025-11-06)
Field: Materials Science — Polymers and Plastics

Classification and Identification of Natural Biodegradable Fabrics using Convolutional Neural Network Textile waste management faces persistent challenges particularly in the accurate classification of fabrics, which often relies on manual sorting methods based on fiber composition labels. These conventional practices are time consuming, error-prone, and frequently hindered by missing, faded, incorrect and damaged label tags. This paper presents an automated system for identifying natural fiber-based biodegradable fabrics using computer vision and machine learning. A custom device was developed, comprising a USB camera with a varifocal lens, a Raspberry Pi 5, and a 7-inch touchscreen display. The system employs a convolutional neural network (CNN) trained, validated and tested on 1,440 fabric images captured under varying focal lengths and zoom configurations. The final model achieved testing accuracy of 99.07% and reliably classified six biodegradable fabric types: Abaca, Cotton, Hessian, Linen, Silk, and Wool. This work highlights the feasibility of CNN-based fabric classification systems and offers a scalable, practical alternative to conventional sorting methods. The developed system holds significant potential for enhancing textile recycling, material recovery, and automated waste management processes within circular economy frameworks. Convolutional neural network ; Sorting ; Identification (biology) ; Zoom ; USB ; Machine vision ; Artificial neural network Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Microplastics and Plastic Pollution