jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Taehee Kim | Han-sol Kim | Jang-Seu Ki
Source: Journal of Freshwater Ecology (article, 2026-03-03)
Field: Materials Science — Biomaterials

Machine learning assessment of molecular trophic diatom index (mTDI) using planktonic eDNA for water quality in freshwater ecosystems Water quality indices using diatoms, such as the Trophic Diatom Index (TDI), are commonly used in freshwater environmental monitoring; however, their taxonomic identification is labor-intensive and ambiguous. In the present study, we applied planktonic environmental DNA (eDNA) metabarcoding to the molecular TDI (mTDI) and evaluated its feasibility as a rapid assessment tool compared with microscopic TDI. We collected surface water samples for eDNA metabarcoding and benthic diatom samples for morphological identification from the Han and Nakdong Rivers in Korea. The 18S rDNA metabarcoding determined the planktonic diatom community, and sequence read counts were used to calculate an mTDI. Canonical Correspondence Analysis (CCA) showed that eDNA community has a stronger statistical correlation with environmental factors. However, the predicted water quality exhibited a clear discrepancy between methods; planktonic eDNA–derived mTDI consistently produced lower estimates. Decomposition analysis of contribution revealed that this discrepancy was driven by the over-representation of pollution-tolerant diatoms, particularly Aulacoseira granulata and Cyclotella meneghiniana, in the eDNA dataset. SIMPER analysis identified these species as the principal contributors to the dissimilarity between the eDNA and microscopy diatom communities. Permutation importance was used to interpret (not predict) variable effects in the Random Forest model, with A. granulata and C. meneghiniana showing the highest %IncMSE (13.3% and 11.0%). These findings suggest that benthic microscopic and surface water eDNA samples may offer complementary insights into water quality. However, directly using eDNA read counts is potentially biased, given the structural inconsistency across sample types. Diatom ; Plankton ; Trophic level ; Benthic zone ; Water quality ; Environmental DNA ; Freshwater ecosystem ; Canonical correspondence analysis Diatoms and Algae Research ; Environmental DNA in Biodiversity Studies ; Protist diversity and phylogeny
Authors: Anoj Aryal | Weiyi Gong | Huta Banjade | Qimin Yan
Source: arXiv (Cornell University) (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Materials design based on a material-motif network and heterogeneous graphs Machine learning models for functional materials design require precise and informative representations of material systems. Common representations encode atomic composition and bonding but often do not include local coordination environments across chemically diverse crystals. Recurring structural motifs provide a motif level description of crystalline solids and can serve as interpretable descriptors for structure property learning. To analyze the motif connectivity in materials, we construct a bipartite material motif network from 131,548 Materials Project entries, with materials and motifs as the two node sets. Edges connect materials to their constituent motifs and are weighted by motif distortion, which quantifies the strength of each material motif association. Network connectivity is analyzed to identify motif-defined material clusters that capture recurring local geometries relevant to structure property trends. Most shared motifs act as hubs that connect otherwise disconnected regions of the network, enabling motif guided screening by expanding from known motifs to nearby materials in the same neighborhoods. A network embedding step converts this weighted connectivity into vector representations of materials. Using these motif informed embeddings, property prediction yields a formation energy mean absolute error (MAE) of 0.157 eV per atom and a bandgap MAE of 0.601 eV. These results indicate that motif connectivity provides a compact, interpretable representation that complements existing descriptors for scalable screening and structure property modeling. Motif (music) ; Network motif ; Bipartite graph ; Embedding ; ENCODE ; Structural motif ; Scalability Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Yunhao Fan | Sheng Zhang | Gia-Wei Chern
Source: ArXiv.org (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Machine learning nonequilibrium phase transitions in charge-density wave insulators Nonequilibrium electronic forces play a central role in voltage-driven phase transitions but are notoriously expensive to evaluate in dynamical simulations. Here we develop a machine learning framework for adiabatic lattice dynamics coupled to nonequilibrium electrons, and demonstrate it for a gating induced insulator to metal transition out of a charge density wave state in the Holstein model. Although exact electronic forces can be obtained from nonequilibrium Green's function (NEGF) calculations, their high computational cost renders long time dynamical simulations prohibitively expensive. By exploiting the locality of the electronic response, we train a neural network to directly predict instantaneous local electronic forces from the lattice configuration, thereby bypassing repeated NEGF calculations during time evolution. When combined with Brownian dynamics, the resulting machine learning force field quantitatively reproduces domain wall motion and nonequilibrium phase transition dynamics obtained from full NEGF simulations, while achieving orders of magnitude gains in computational efficiency. Our results establish direct force learning as an efficient and accurate approach for simulating nonequilibrium lattice dynamics in driven quantum materials. Non-equilibrium thermodynamics ; Adiabatic process ; Phase transition ; Wave function ; Lattice (music) ; Charge density wave ; Quantum Machine Learning in Materials Science ; Quantum many-body systems ; Organic and Molecular Conductors Research
Authors: Anton Beiersdorfer | Lisa Hetzel | Florian Deißenbeck | Carsten Staacke | Christopher Johannes Stein
Source: Machine Learning Science and Technology (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

GAP vs. MACE: efficiency evaluation in a liquid electrolyte system Abstract Machine learning interatomic potentials (MLIPs) have revolutionized molecular simulations, but as they evolve, so does the demand for advanced computing architectures, particularly graphics processing units (GPUs). However, the high cost of GPUs limits accessibility, making it crucial to compare GPU and central processing unit (CPU) based MLIPs under practical conditions. This study examines two popular MLIPs: the GPU-accelerated multi-atomic cluster expansion model and the CPU-based Gaussian approximation potentials model, applied to a battery electrolyte system known for its complex properties. By focusing on these models, our study evaluates differences in computational performance, resource efficiency, and accuracy in reproducing experimental properties. This rigorous benchmark provides insights into the trade-offs between GPU and CPU-based approaches in molecular simulations. Graphics processing unit ; Benchmark (surveying) ; Graphics ; Electrolyte ; Cluster (spacecraft) ; General-purpose computing on graphics processing units ; Battery (electricity) ; Gaussian ; Resource (disambiguation) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Protein Structure and Dynamics
Authors: Duc Viet Nguyen
Source: La Trobe University (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Prediction and Analysis of the Flower Maturity and Chemical Composition of Cannabis sativa L. Plants Using Machine Learning on Multispectral Imaging Data A thesis submitted in total fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Agriculture, Biomedicine and Environment &amp; ARC Research Hub for Medicinal Agriculture, La Trobe University, Victoria, Australia. Multispectral image ; Maturity (psychological) ; Cannabis sativa ; Chemical composition ; Degree (music) ; Biomedicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Cesare Malosso | Filippo Bigi | Paolo Pegolo | Joseph W. Abbott | Philip R. Loche | Mariana Rossi | Michele Ceriotti | Arslan Mazitov
Source: ArXiv.org (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

High-quality, high-information datasets for universal atomistic machine learning The quality, consistency, and information content of training data is often what determines the practical value of machine-learning models for atomistic simulations. Yet, many widely used electronic-structure databases are assembled having materials screening as primary goal rather than robust force-field learning, are limited in their scope to a specific class of chemical compounds, and/or employ inconsistent DFT functionals and settings. Here we introduce MAD-1.5, a highly curated dataset designed explicitly for training broadly applicable atomistic models across the periodic table at high levels of theory. MAD-1.5 extends the MAD dataset with targeted enrichment strategies that improve the coverage of chemical space to 102 elements while keeping the total number of configurations compact. All structures are computed with a single, standardized all-electron DFT workflow using the r$^2$SCAN meta-GGA functional and consistent convergence settings, ensuring uniformity across chemically heterogeneous systems. The dataset encompasses molecules, clusters, bulk crystals, surfaces, and low-dimensional structures, and its quality and consistency are further enhanced by outlier removal using uncertainty quantification. We demonstrate the high accuracy that can be achieved with the proposed dataset by training PET-MAD-1.5, a generally applicable r$^2$SCAN interatomic potential that covers 102 elements in the periodic table and achieves exceptional levels of benchmark accuracy and stability in challenging simulation protocols. Workflow ; Stability (learning theory) ; Benchmark (surveying) ; Consistency (knowledge bases) ; Outlier ; Convergence (economics) ; Table (database) ; Anomaly detection ; Class (philosophy) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications
Authors: Sadaf Noreen | Mamduh J. Aljaafreh | Ashour M. Ahmed
Source: Journal of Molecular Graphics and Modelling (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Exploring the structural basis of organic compounds by predicting experimental IR peaks: a machine learning analysis Basis (linear algebra) ; Estimator ; Hyperparameter ; Regression ; Experimental data ; Pattern recognition (psychology) ; Range (aeronautics) ; Support vector machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Xiaodong Li | Bin Zuo | Guoze Yan | Bingbing Shen | Pengde Li | Xingtao Xu | Haiyan Hu
Source: Coordination Chemistry Reviews (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Covalent organic frameworks for precision rare earth separation: from biomimetic ion channels to intelligent molecular machines Molecular machine ; Covalent bond ; Dynamic covalent chemistry ; Biomimetics ; Rare earth ; Molecular recognition ; Biomimetic materials Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes ; Extraction and Separation Processes
Authors: Yafei Luo | Jiaxin Zhang | Zihan Zhu | Luqiong Huang | Jiaqing Xu | Changwei Hu | Dian-yong Tang | Zhongzhu Chen | Zhishan Su
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Twisted Structure-Mediated Organic Afterglow Materials Enabling Ultra-Narrowband Pure Red Emission with Second-Scale Lifetime Phosphorescence ; Afterglow ; Feature (linguistics) ; Rational design ; Workflow ; Pyrene ; Reliability (semiconductor) Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Luminescence Properties of Advanced Materials
Authors: Jiali Lu | Shengfeng Yang
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

Diffusion-based Generative Machine Learning Model for Predicting Crack Propagation in Aluminum Nitride at the Atomic Scale Predicting atomic-scale crack propagation in aluminum nitride (AlN) is critical for semiconductor reliability but remains prohibitively expensive via molecular dynamics (MD). We develop a diffusion-based generative machine learning model to predict atomic-scale crack propagation in AlN, a critical semiconductor material, by conditioning solely on initial microstructure embeddings. Trained on MD simulations of single-crack systems, the model achieves a significant speedup while accurately forecasting dynamic fracture processes, including stress-driven crack initiation, crack branching, and atomic-scale bridging ligaments. Crucially, it demonstrates inherent physical fidelity by reproducing material-intrinsic mechanisms while disregarding periodic boundary artifacts, and generalizes to unseen multi-crack configurations. Validation against MD ground truth confirms the capability of the model to capture complex fracture physics without auxiliary stress or energy data, enabling rapid exploration of crack-mediated failure for semiconductor reliability optimization. Fracture mechanics ; Bridging (networking) ; Reliability (semiconductor) ; Multiscale modeling ; Generative grammar ; High fidelity ; Semiconductor ; Generative model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advancements in Semiconductor Devices and Circuit Design
Authors: Lulu Li | Julian Geiger | Pol Sanz Berman | Núria López
Source: Journal of the American Chemical Society (article, 2026-02-03)
Field: Materials Science — Materials Chemistry

Dynamic Polaronic Control of Metal Cluster Adaptability on Reducible Oxides Metal-oxide interactions are ubiquitous in many technological applications and involve a complex interplay between the oxide support and the metal nanoparticle. Particularly, it has been proposed that in strong metal-support interaction, the defect chemistry affects the metal cluster morphology. Here we develop a physics-guided machine learning framework to decode these interactions using Pt<sub>7</sub> and Pt<sub>13</sub> representative of planar and tridimensional clusters, analyzing the impact of across oxygen vacancy concentrations of CeO<sub>2-<i>x</i></sub> = 0-12.5% (528 configurations). Our models (<i>R</i><sup>2</sup> > 0.97) reveal that polaron swarms, rather than defect concentrations, predominantly control cluster shape and charge through size-dependent pathways. The framework yields quantitative design principles for defect-driven catalyst optimization and provides a general methodology for systematic mechanisms of metal-support interactions across diverse catalyst systems. Cluster (spacecraft) ; Polaron ; Adaptability ; Planar ; Metal ; Oxide ; Charge (physics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Duc Viet Nguyen
Source: Open MIND (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Prediction and Analysis of the Flower Maturity and Chemical Composition of Cannabis sativa L. Plants Using Machine Learning on Multispectral Imaging Data A thesis submitted in total fulfilment of the requirements for the degree of Doctor of Philosophy to the School of Agriculture, Biomedicine and Environment &amp; ARC Research Hub for Medicinal Agriculture, La Trobe University, Victoria, Australia. Multispectral image ; Maturity (psychological) ; Cannabis sativa ; Chemical composition ; Degree (music) ; Biomedicine Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Remote-Sensing Image Classification
Authors: Shanshan Hou | Sheng Zeng | Yugui Yang | Jiayin Song | Bing Sun
Source: Archives of Computational Methods in Engineering (article, 2026-03-02)
Field: Chemistry — Inorganic Chemistry

Multi-Scale Computational Methods for Reactive Transport of in situ Uranium Leaching: Pore Structure Evolution, Multi-Physics Coupling, and Numerical Simulations Porous medium ; Characterization (materials science) ; Coupling (piping) ; Nonlinear system ; Permeability (electromagnetism) ; Emulation ; Sorption ; Fractal ; Porosity Radioactive element chemistry and processing ; CO2 Sequestration and Geologic Interactions ; Groundwater flow and contamination studies
Authors: Weiqi Chen | Z. Y. Xu | Kang Wang | Lixin Gao | Aisheng Song | Tianbao Ma
Source: npj Computational Materials (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Author Correction: Transferable machine learning model for multi-target nanoscale simulations in hydrogen-carbon system from crystal to amorphous Nanoscopic scale ; Amorphous solid ; Crystal (programming language) ; Scale (ratio) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Hassan Al-Fayadh | Azhar Shuker | Maha Dayia | Ali Saadi | Emad Abdullah
Source: Pharaonic Journal of Science (article, 2026-03-12)
Field: Chemistry — Analytical Chemistry

Applications for Artificial Intelligence in Food and Dairy Science: Advancing Food Quality and Safety: A Review Artificial intelligence (AI) has become a key tool in food science that significantly raises the safety and quality of food. AI methods like computer vision, machine learning, and artificial neural networks enable effective analysis of complex food data. These technologies are widely used in sensory evaluation, spoilage prediction, and quality assessment of food products. Because AI can detect chemical and microbiological contaminants early on, it is also crucial for human health. AI-powered solutions also enhance food manufacturing procedures and reduce waste. All things considered, applying AI to food science improves consumer protection and encourages more reliable decision-making. Food spoilage ; Food safety ; Food quality ; Quality (philosophy) ; Key (lock) ; Novel food ; Food industry ; Food engineering Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Advanced Chemical Sensor Technologies
Authors: Pol Benítez | Cibrán Rico Lopez | Edgardo Saucedo | Claudio Cazorla
Source: Open MIND (preprint, 2026-02-24)
Field: Materials Science — Materials Chemistry

Machine Learning Modeling of Temperature-Dependent Optoelectronic Properties of Anharmonic Solid Solutions Leveraging strong optoelectronic responses to external stimuli, such as temperature and electric fields, is central to the development of advanced photonic technologies, including adaptive photodetectors and reconfigurable photovoltaic devices. However, only a limited number of semiconducting materials, typically characterized by strong electron-phonon coupling, are known to exhibit such pronounced responsiveness, and their equilibrium optoelectronic properties are often not optimally suited for targeted applications. Chemical engineering strategies, such as doping and solid-solution mixing, are therefore widely employed to fine-tune the electronic and optical properties of semiconductors. Predicting the impact of such modifications, however, remains highly challenging due to the intrinsic complexity of chemically disordered and anharmonic systems, as well as the computational limitations of conventional first-principles approaches. In this work, we introduce a new computational framework that combines ab initio electronic-structure methods with machine-learning techniques to achieve first-principles precision in the prediction of optoelectronic properties of anharmonic solid solutions at finite temperature. We apply this approach to silver chalco-halide solid solutions, an emergent class of optoelectronic materials that have been experimentally shown to exhibit large band-gap tunability and strong responses to thermal excitations. Our results provide quantitative insight into the interplay between chemical disorder, lattice dynamics, and electronic structure in these materials. More broadly, this study establishes a general strategy for the accurate modeling of optoelectronic functionality in chemically disordered semiconductors. Anharmonicity ; Photodetector ; Photonics ; Ab initio ; Thermal ; Photovoltaic system Machine Learning in Materials Science ; Perovskite Materials and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Xintian Wang | Junmin Chen | Zhuoying Zhu | Peichen Zhong
Source: arXiv (Cornell University) (preprint, 2026-04-09)
Field: Materials Science — Materials Chemistry

Differentiable hybrid force fields support scalable autonomous electrolyte discovery Autonomous electrolyte discovery demands a computational engine that satisfies a critical trilemma: it must be fast enough for high-throughput screening, accurate enough for quantitative property prediction, and calibratable enough for online refinement. Classical empirical force fields (FFs) are fast but rely heavily on error cancellation, while standard machine learning interatomic potentials (MLIPs) are computationally expensive, lack rigorous long-range physics, and resist gradient-based calibration. In this Perspective, we highlight that differentiable hybrid FFs resolve this trilemma by fusing physically motivated functional forms with neural-network short-range corrections. Grounded in Energy Decomposition Analysis (EDA), state-of-the-art models such as PhyNEO-Electrolyte and ByteFF-Pol achieve zero-shot generalization to bulk phases, delivering throughputs on the order of tens of ns/day (up to $\sim$50 ns/day, depending on model complexity) for 10,000-atom systems. Crucially, their physical skeletons provide a well-conditioned parameter space for differentiable molecular dynamics (dMD). This enables a dual-calibration paradigm: bottom-up \textit{ab initio} parameterization combined with top-down fine-tuning from macroscopic experimental observables. We propose that this architecture meets the requirements of a ``ChemRobot-ready'' digital twin by integrating physics-grounded simulation with experimentally calibratable refinement, thereby enabling closed-loop autonomous electrolyte discovery. Differentiable function ; Generalization ; Property (philosophy) ; Scalability ; Computational model ; Trilemma ; Decomposition ; Basis (linear algebra) ; Realization (probability) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems