jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dhanaji Chamakrao Kurumkar | Santosh Balu Yede
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-31)
Field: Chemistry — Analytical Chemistry

Economics - AI in Transport Abstract Artificial Intelligence (AI) has emerged as a transformative tool in scientific research, particularly in the analysis and interpretation of complex spectroscopic data. Traditional spectroscopic techniques such as Raman spectroscopy, infrared spectroscopy, nuclear magnetic resonance (NMR), and mass spectrometry generate large and complex datasets that require advanced analytical methods for accurate interpretation. AI-driven approaches, including machine learning and deep learning algorithms, offer powerful solutions for automated pattern recognition, feature extraction, and predictive analysis in spectroscopy. This review explores the integration of artificial intelligence with spectroscopic techniques, highlighting recent advancements, applications, and challenges in the field. The study discusses how AI enhances the accuracy, efficiency, and speed of spectral data analysis while reducing human intervention. Additionally, it examines various AI models used in spectroscopy and their potential in chemical analysis, biomedical diagnostics, and material science. The paper also addresses current limitations and future research directions for the effective implementation of AI-driven spectroscopic data interpretation. Transformative learning ; Interpretation (philosophy) ; Big data ; Deep learning ; Applications of artificial intelligence ; Artificial neural network Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: P. D. Varuna S. Pathirage | Konstantinos D. Vogiatzis
Source: Open MIND (preprint, 2026-01-20)
Field: Materials Science — Materials Chemistry

DDCCNet: Physics-enhanced Multitask Neural Networks for Data-driven Coupled-cluster We present the data-driven coupled-cluster deep network (DDCCNet), a family of multitask, physics-enhanced deep learning architectures designed to predict coupled-cluster singles and doubles (CCSD) amplitudes and correlation energies from lower-level electronic structure methods. The three DDCCNet variants (termed as v1, v2, and v3) progressively incorporate architectural refinements ranging from parallel subnetworks for t_1 and t_2 amplitudes to feature-partitioned blocks and physics-enhanced intermediate prediction layers that are structured in accordance with coupled-cluster equations to enhance physical consistency and multitask learning efficiency. These models jointly learn correlated amplitude patterns while embedding symmetry and orbital-level interactions directly into the network structure. Applied to methanol conformers, CO2 clusters, and small organic molecules, DDCCNet_v2 delivered the most accurate and transferable performance, achieving chemically precise correlation energies across diverse molecular systems. Collectively, DDCCNet establishes a scalable, physically grounded framework that unifies machine learning and ab initio theory for efficient, data-driven electronic structure prediction. Artificial neural network ; Consistency (knowledge bases) ; Embedding ; Deep learning ; Amplitude ; Ab initio ; Correlation ; Pairing Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Stiv Llenga | Ganna Gryn'ova
Source: ChemRxiv (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Navigating high-dimensional spaces in chemistry and beyond with a reference-based coordinate assignment High-dimensional representations underpin modern machine learning across chemistry, biology, and data science, yet their rapidly increasing dimensionality limits scalability, interpretability, and computational e!ciency. Here we introduce the reference-based coordinate assignment (RCA), a geometric framework that reconstitutes dimensionality reduction as a positioning problem. Instead of deriving coordinates through global variance maximisation or optimisation-based projections, RCA determines positions from distances between investigated and reference samples, forming a new coordinate system. Using multilateration, data points are embedded relative to these references, enabling deterministic and pointwise embedding. Across diverse domains-including molecular datasets, image recognition, and speech classification-RCA produces compact representations that preserve the predictive power of machine learning models while dramatically reducing their computational cost. These results show that such reference distances can serve as scalable coordinates for complex data, providing an e!cient and interpretable framework for navigating high-dimensional spaces. How much, or how little information is necessary to represent a molecule to a machine to enable e!cient learning, su!ciently accurate property prediction, and accelerated discovery? Modern molecular representations encode structural and electronic information through hundreds or thou ENCODE ; Pointwise ; Curse of dimensionality ; Property (philosophy) ; Coordinate system ; Dimensionality reduction ; Interpretability ; Computational complexity theory ; Coordinate descent Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Xingyu Guo | Cheng Gui | Zhenbin Wang
Source: ArXiv.org (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Are Universal Potentials Ready for Alkali-Ion Battery Kinetics? Accelerating alkali-ion battery discovery requires accurate modeling of atomic-scale kinetics, yet the reliability of universal machine learning interatomic potentials (uMLIPs) in capturing these high-energy landscapes remains uncertain. Here, we systematically benchmark state-of-the-art uMLIPs, including M3GNet, CHGNet, MACE, SevenNet, GRACE, and Orb, against DFT baselines for cathodes and solid electrolytes. We find that the Orb-v3 family excels in static migration barrier predictions (MAE $\approx$ 75--111 meV), driven primarily by architectural refinements. Conversely, for dynamic transport, the GRACE model trained on the OMat24 dataset demonstrates superior fidelity in reproducing ion diffusivities and structural correlations. Our results reveal that while architectural sophistication (e.g., equivariance) is beneficial, the inclusion of high-temperature, non-equilibrium training data is the dominant driver of kinetic accuracy. These findings establish that modern uMLIPs are sufficiently robust to serve as zero-shot surrogates for high-throughput kinetic screening of next-generation energy storage materials. Battery (electricity) ; Sophistication ; Benchmark (surveying) ; Reliability (semiconductor) ; High fidelity ; Kinetic energy Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Jesse Ahlbrecht | Marius D. R. Lutz | Vera Jost | Michael Färber | Stefan Bräse | Georg Wuitschik
Source: ACS Central Science (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments High-throughput experimentation (HTE) accelerates chemical discovery by shortening the lead times for molecule synthesis. The choice of initial reaction conditions directly influences the outcome and length of any reaction optimization. But human involvement in plate design and data analysis remains a significant cost factor and is accompanied by biases. Therefore, making the most out of past reaction outcomes is crucial. While advances in machine learning allow us to generate promising reaction conditions, this approach is often not suitable because not enough relevant reaction data are available or it is of insufficient quality. Herein we introduce a robust statistical method using <i>z</i>-scores to analyze 66,000 internal HTE reactions on complex molecules. Additionally, we publish the underlying data as well as a tool to analyze and draw actionable conclusions from this data set. We exemplify the power of this method for the widely employed Buchwald-Hartwig and Suzuki-Miyaura cross-coupling reactions. The results reveal optimal conditions that differ significantly from literature-based guidelines. These data-driven insights provide high-quality starting points for optimization campaigns, improving their overall efficiency. Work (physics) ; Outcome (game theory) ; Publication ; Lead (geology) ; Reaction conditions ; Data point Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Nadeem Hussain Solangi | Rana R. Neiber | Jai Kumar | Soumia Zagad | Jingmeng Jiao | Maokuan Guo | Xiaoli Lu | Victor Charles | Jun Lu
Source: Coordination Chemistry Reviews (article, 2026-03-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

In-situ coordination defects in metal oxides: chemical perspectives and operando characterization for high-performance multivalent-ion energy storage Energy storage ; Supercapacitor ; Battery (electricity) ; Characterization (materials science) ; Electrochemical energy storage ; Electrochemistry ; Oxygen storage ; Vacancy defect Supercapacitor Materials and Fabrication ; Advancements in Battery Materials ; Electrocatalysts for Energy Conversion
Authors: Virender Bhadoria
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

The Cylindrical–Septadimensional Chemical Manifold: A Geometric Hypothesis and Applications for Periodic Similarity Representation This work introduces a cylindrical–septadimensional manifold representation of the periodic system. In this framework, chemical elements are embedded within a curved similarity space defined by electronic configuration, relativistic effects, bonding behavior, and physicochemical descriptors. Periodicity emerges as a cyclic boundary condition along the valence-shell dimension, yielding a cylindrical topology that removes artificial discontinuities present in flat tabular layouts. The septadimensional structure is proposed as the minimal latent dimensionality required to preserve intrinsic variance in elemental properties while maintaining continuous periodic relationships. The model is presented as a geometric and topological representation rather than a new physical theory. Conceptual projections and manifold visualizations are provided to illustrate cylindrical periodicity and higher-dimensional continuity. Potential applications include chemical similarity navigation, materials substitution mapping, descriptor compression for machine learning, periodicity-aware generative chemistry, and exploratory visualization of elemental relationships. This representation offers a unified geometric framework for organizing chemical knowledge in continuous descriptor space. Representation (politics) ; Manifold (fluid mechanics) ; Similarity (geometry) ; Visualization ; Curse of dimensionality ; Topology (electrical circuits) ; Boundary (topology) ; Space (punctuation) ; Classification of discontinuities Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Computational Drug Discovery Methods
Authors: Sandeep Kumar Singh | Abhishek Sharma | Arti Kashyap
Source: Scientific Reports (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

A crystal graph to vector approach for predicting magnetic properties We introduce CG-Vec, a crystal graph-to-vector framework that replaces iterative message passing with compact, interpretable descriptors coupled to conventional machine learning. Across diverse datasets, CG-Vec matches the accuracy of deep graph networks on large datasets but substantially outperforms them in data-scarce regimes. The advantage is most pronounced for magnetic properties, where existing approaches have struggled: CG-Vec delivers reliable predictions of magnetization in both ferromagnetic and ferrimagnetic systems and of Curie temperature. Beyond magnetism, CG-Vec performs competitively for formation energy and band gap, demonstrating broad applicability. These results establish vectorized representations as a practical and scalable alternative to deep architectures, enabling efficient and interpretable modeling of complex material properties. Ferrimagnetism ; Scalability ; Graph ; Curie temperature ; Ferromagnetism ; Magnetization Machine Learning in Materials Science ; Magnetic Properties and Applications ; Multiferroics and related materials
Authors: Jordan M. Marshall | Marlena Alexander | Vincent Joseph García | Joshua Safin | Sheryl L Sanchez | Elham Foadian | Claudia J. Rawn | Katharine Page | Sergei V Kalinin | Mahshid Ahmadi
Source: ChemRxiv (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware Prediction of Thermal Expansion in Complex Oxides via LLM-Curated Literature Data Machine learning (ML) is increasingly used in materials discovery, but its practical application is often limited by the time required to construct structured experimental datasets and by the lack of reliable uncertainty estimates. We introduce an integrated workflow that combines documentgrounded large language model (LLM) literature mining, human verification, and probabilistic modeling to enable uncertainty-aware prediction of the coefficient of thermal expansion (CTE) in complex oxides. Experimentally reported CTE values, compositions, and measurement temperature ranges are extracted from full-text articles using a document-grounded large language model and subsequently normalized and verified. Composition-derived descriptors and the reported temperature bounds are used as inputs to a probabilistic regression model that predicts both the expected CTE and a composition-dependent uncertainty, enabling prediction intervals for new compositions. On held-out tests, the model achieves competitive mean accuracy relative to deterministic baselines while producing uncertainty estimates that increase systematically for sparsely represented or chemically distinct compositions, enabling risk-aware screening and prioritization. The workflow supports comparative screening of compositions with targeted CTE behavior and helps guide experimental selection for detailed thermophysical characterization. This study illustrates how LLM-assisted literature curation can be combined with uncertainty-aware machine learning to construct property prediction workflows for materials systems where experimental data are sparse and primarily available in the literature. Workflow ; Construct (python library) ; Probabilistic logic ; Property (philosophy) ; Thermal expansion ; Model selection ; Uncertainty quantification ; Support vector machine ; Statistical model Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Thermal Expansion and Ionic Conductivity
Authors: Virender Bhadoria
Source: Open MIND (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

The Cylindrical–Septadimensional Chemical Manifold: A Geometric Hypothesis and Applications for Periodic Similarity Representation This work introduces a cylindrical–septadimensional manifold representation of the periodic system. In this framework, chemical elements are embedded within a curved similarity space defined by electronic configuration, relativistic effects, bonding behavior, and physicochemical descriptors. Periodicity emerges as a cyclic boundary condition along the valence-shell dimension, yielding a cylindrical topology that removes artificial discontinuities present in flat tabular layouts. The septadimensional structure is proposed as the minimal latent dimensionality required to preserve intrinsic variance in elemental properties while maintaining continuous periodic relationships. The model is presented as a geometric and topological representation rather than a new physical theory. Conceptual projections and manifold visualizations are provided to illustrate cylindrical periodicity and higher-dimensional continuity. Potential applications include chemical similarity navigation, materials substitution mapping, descriptor compression for machine learning, periodicity-aware generative chemistry, and exploratory visualization of elemental relationships. This representation offers a unified geometric framework for organizing chemical knowledge in continuous descriptor space. Representation (politics) ; Manifold (fluid mechanics) ; Similarity (geometry) ; Visualization ; Curse of dimensionality ; Topology (electrical circuits) ; Boundary (topology) ; Space (punctuation) ; Classification of discontinuities Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Computational Drug Discovery Methods
Authors: Gao‐Peng Ren | Cong Zhou | An Su | Ke‐Jun Wu
Source: AIChE Journal (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Learning temperature‐dependent molecular representations for physicochemical property prediction Abstract Physicochemical properties often display diverse and nonlinear dependencies on temperature. While machine learning has excelled at modeling molecular structural representations, accurately capturing the interplay between temperature and these properties remains a significant challenge. To address this, we introduce the Boltzmann layer, a neural network module inspired by the principles of statistical thermodynamics. Designed as a plug‐and‐play component, the Boltzmann layer enables seamless incorporation of temperature effects into molecular representations. By integrating the Boltzmann layer with the graph attention networks, our method achieves good predictive performance, yielding an average absolute relative deviation from 1.73% to 11.5% and an R 2 from 0.863 to 0.999 across 18 physicochemical properties. Notably, the model also excels in mixture systems, delivering exceptional performance on mixture viscosity and solubility. Our findings highlight that embedding physical domain knowledge within machine learning models leads to both greater predictive accuracy and deeper scientific insight. Boltzmann machine ; Embedding ; Boltzmann constant ; Artificial neural network ; Viscosity ; Property (philosophy) ; Domain (mathematical analysis) ; Restricted Boltzmann machine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Silvio Paganucci | Lukas Juchli | Filippo Parisi | Tina Moor | Lukas Braun | Tobias Wolfer | Patric Eberle
Source: Infrared Physics & Technology (article, 2026-03-17)
Field: Chemistry — Analytical Chemistry

Accurate discrimination and quantification of cellulosic fibers using NIR hyperspectral imaging and optimized machine learning Near-infrared hyperspectral imaging (NIR-HSI) enables non-destructive acquisition of spectral signatures that reflect physicochemical properties of textile fibers. However, for cellulose-based fibers, a major category of textile materials, the high similarity in their spectral response poses significant challenges for reliable identification and quantitative analysis. Here, we present specifically designed compact machine learning (ML) models, including the emerging Kolmogorov-Arnold network (KAN), to detect subtle variations in NIR spectra for two central tasks: (i) determining the blend ratio of cellulosic blend and (ii) classifying eight pure cellulosic fibers, including ecological materials. The spectral range 1410–1610 nm was identified as region of interest (ROI). A comprehensive analysis of classical and neural network-based ML models was conducted, with model size evaluated as an important factor for hardware constrained, in-situ NIR-HSI applications. Interestingly, tailored compact models ranked among the top performers. The KAN, one of the most compact architectures studied, achieved highest performance in both tasks: a mean absolute error (MAE) of 0.90 % for blend ratios and an accuracy of 93.8% for fiber classification, outperforming a 1D convolutional neural network. Our results highlight the potential of tailored ML architectures integrated with NIR-HSI for accurate identification and quantification of cellulosic fibers, advancing the precision and practical applicability of chemometric textile analysis. • NIR hyperspectral imaging was applied for non-destructive discrimination and quantification of cellulosic fibers and blends. • Specifically designed and size-optimized classical and neural network machine learning models, including the emerging KAN, were developed and compared. • Determination of the blending ratio of cotton/viscose blends and discrimination of cellulosic fibers, including cotton, viscose and six ecological plant fibers. • Interestingly, compact models ranked among the top performing models. • KAN achieved best performance: MAE < 0.90 % in cotton/viscose blend ratio determination and 93.8 % accuracy in classifying cellulosic fibers. Hyperspectral imaging ; Cellulosic ethanol ; Convolutional neural network ; Artificial neural network ; Pattern recognition (psychology) ; Identification (biology) ; Similarity (geometry) ; Spectral signature Spectroscopy and Chemometric Analyses ; Advanced Cellulose Research Studies ; Textile materials and evaluations
Authors: Ziliang Lu | Ishwar Kapoor | Takeru Araki | Lu Chen | Tao Song | Yanfang Li | Yang Su | Xiaoqin Zeng | LeYun Wang
Source: Materials Genome Engineering Advances (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Semantic Intelligence in Metallurgy: A Dual‐Stage Language Model Framework for Processing‐Aware Magnesium Alloy Design ABSTRACT Advances in artificial intelligence (AI) and large language models (LLMs) are transforming materials research by enabling automated data extraction, knowledge integration, and property prediction. This study presents a dual‐stage, LLM‐assisted framework for magnesium alloy design that combines semantic extraction, thermodynamic reasoning, and machine learning (ML). Using Qwen‐2.5, alloy chemistry, processing details, and thermal and mechanical property data are automatically extracted from full‐text literature and converted into structured records. The extracted information is expanded with thermodynamic phase descriptors predicted by DeepSeek‐R1 and numerical processing features generated from CLIP‐based embeddings. The feature set is optimized using sequential backward selection (SBS), and predictive models are developed using support vector machines (SVM), random forest (RF), and eXtreme Gradient Boosting (XGB). The proposed workflow effectively integrates chemistry, thermodynamics, and processing history, achieving robust predictions for thermal conductivity, yield strength, and ultimate tensile strength. The best performing models yielded R 2 values of ∼ 0.80 (RMSE ∼ 9.98 W·m −1 K −1 ), ∼ 0.69 (RMSE ∼ 37.2 MPa), and ∼ 0.73 (RMSE ∼ 31.5 MPa) for TC, YS, and UTS, respectively. Validation against CALPHAD calculations shows that DeepSeek‐R1 reproduces equilibrium phase fractions within 1 wt.% deviation. Overall, this work shows that semantic intelligence can link literature‐derived knowledge with predictive modeling, providing a pathway for processing‐informed alloy design. Workflow ; Feature selection ; Set (abstract data type) ; Support vector machine ; Semantic property ; Metamodeling ; Model selection ; Random forest ; Property (philosophy) Machine Learning in Materials Science ; Magnesium Alloys: Properties and Applications ; Hydrogen Storage and Materials