jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alcides Gustavo López Paredes | Patricia Esther Ochoa Pesantes | Karla Alexandra Ávila Hidalgo
Source: Revista Ingenio global. (article, 2026-03-13)
Field: Chemistry — Analytical Chemistry

Reconocimiento asistido por redes neuronales de grupos funcionales en espectros FTIR de compuestos obtenidos por pirólisis Objective: The description of pyrolysis products using FTIR spectroscopy has problems due to the complexity and overlap of spectral bands. In this context, the study aimed to evaluate how deep learning models can automatically recognize functional groups in FTIR spectra of pyrolytic compounds. Methodology: Three neural network architectures were tested: 1D CNN, Inception V3, and Transformer for automated multi-label recognition of 18 functional groups. A set of 12,500 spectra was created, sourced from reference spectral databases (NIST and SDBS) and experimental samples of bio-oils produced by the pyrolysis of biomass and plastics. Results: The Transformer model achieved the best performance, with a macro F1 score of 0.896, an AUC-ROC of 0.967, and a molecular perfection rate of 72.4%, significantly outperforming conventional methods (SVM+PCA: F1 = 0.783). Additionally, the interpretability analysis with SHAP confirmed that the models identified spectral areas that chemically matched the spectroscopic assignments. Conclusions: The results demonstrated the viability of deep neural networks as an assisted tool for the prompt and reliable identification of functional groups in pyrolytic products, highlighting their potential to support chemical characterization processes and spectral analysis in research contexts and analytical applications. Interpretability ; Fourier transform infrared spectroscopy ; Pattern recognition (psychology) ; Pyrolytic carbon ; Artificial neural network ; Spectral analysis ; Pyrolysis ; Partial least squares regression Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Edible Oils Quality and Analysis
Authors: Yuanbin Wang | Yupeng Bai | Peng Wang | Wenhu Wang | Mingzhu Zhu | Ming Luo
Source: Journal of Materials Informatics (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Physically synthesized data for deep learning-based visual scratch inspection of aerospace alloys with complex geometries Aerospace alloys often operate under extreme conditions. Accurate defect segmentation in images of aerospace components is the key to quantifying the defects and evaluating their impact for part lifespan. The components usually have complex free-form surfaces, leading to uneven light distribution in images. The variable image presentations pose a great challenge for accurate segmentation, especially with limited data. Generative adversarial networks and other training-based methods are commonly used for image generation, but they still rely on sufficient high-quality training data. In this paper, a physical-based image generation method is proposed to create any possible scratches according to physical laws to improve the scratch segmentation capability with limited data. First, an efficient scratched blade surface image generation pipeline is developed. Then, a systematic strategy to maximize the effect of physical synthetic scratch images is presented. The experiments show that the segmentation intersection-over-union could be improved from 0.66 to 0.83 with only 20 real images for training, and reveal the influences of network structure, image and label quality, data fusion strategy on segmentation performance. Scratch ; Segmentation ; Aerospace ; Pipeline (software) ; Image segmentation ; Image (mathematics) ; Key (lock) ; Scale-space segmentation Machine Learning in Materials Science ; Image Processing Techniques and Applications ; Industrial Vision Systems and Defect Detection
Authors: Zhikang Cao | Xiaoyuan Wang | Guorui He | Yichong Li | Yang Yang | Yufei Wang | Haiyan Gao
Source: Materials Today Communications (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Facilitating creep-resistant Al alloy design through a physics-aware integrated machine learning pipeline Pipeline (software) ; Alloy ; Process (computing) Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Model Reduction and Neural Networks
Authors: Ziyi Liu | Haihong Wu | Weihan Liang | Xing Wang | Yuxuan Xu | Deshan Chen | Pingping Yao
Source: Journal of Alloys and Compounds (article, 2026-02-07)
Field: Materials Science — Metals and Alloys

Experimental investigation and machine learning prediction of stress relaxation resistance in C17200 alloy under aging treatment Alloy ; Stress relaxation ; Relaxation (psychology) ; Stress (linguistics) Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes
Authors: Yu Harabuchi | Ruben Staub | Min Gao | Nobuya Tsuji | Benjamin List | A. A. Varnek | Satoshi Maeda
Source: ChemRxiv (article, 2026-01-12)
Field: Materials Science — Materials Chemistry

Predicting Enantioselectivity via Kinetic Simulations on Gigantic Reaction Path Networks Asymmetric catalysts exert control over the reactivity and enantioselectivity of chemical reactions, often through their large size and highly flexible geometries. Current quantum chemical calculations provide a powerful means to predict enantioselectivity through exhaustive conformational sampling at the selectivity-determining transition state (TS). However, this approach still relies on a predetermined selectivity-determining step and on the assumption that selectivity is governed by the Boltzmann distribution of TS conformers. To address this issue, constructing a reaction path network that captures the kinetically accessible regions of the potential energy surface is essential. In this study, we applied a delta-learning neural network potential (ΔNNP) to an asymmetric organocatalyst belonging to the imidodiphosphorimidates (IDPi) family, comprising over 200 atoms. The ΔNNP demonstrated accuracy comparable to that of density functional theory (DFT) using GFN2-xTB as the baseline method and learning the DFT–GFN2-xTB energy differences through the neural network model. Using the ΔNNP-based single component - artificial force induced reaction (denoted by NNP/AFIR) method, we constructed a reaction path network containing 48463 paths. Kinetic simulations based on this network were then performed to predict the reaction mechanism and enantioselectivity. Within the network, neumerous paths, including asynchronous concerted and stepwise mechanisms, were found to be energetically competitive and thus to contribute to the overall kinetics. Traffic volume analysis further revealed that intermediates with negligible yields can still play important kinetic roles. Overall, the NNP/AFIR approach would provide a powerful framework not only for deepening mechanistic understanding but also for accelerating the rational design of asymmetric catalytic systems with high reactivity and enantioselectivity. Potential energy surface ; Reactivity (psychology) ; Kinetic energy ; Path (computing) ; Transition state ; Catalysis ; Potential energy ; Density functional theory ; Quantum ; Artificial neural network Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Kenji Okada | Kazushi Okada | Kei-ichi Okazaki | Toshifumi Mori | Kang Kim | Nobuyuki Matubayasi
Source: The Journal of Chemical Physics (article, 2026-03-02)
Field: Materials Science — Materials Chemistry

Deep learning of committor for ion dissociation and interpretable analysis of solvent effects using atom-centered symmetry functions The association and dissociation of ion pairs in water are fundamental to physical chemistry, yet their reaction coordinates are complex, involving not only interionic distance but also solvent-mediated hydration structures. These processes are often represented by free-energy landscapes constructed from collective variables (CVs), such as interionic distance and water-bridging structures; however, it remains uncertain whether such representations reliably capture the transition pathways between the associated and dissociated states. In this study, we employ deep learning to identify reaction coordinates for NaCl ion pair association and dissociation in water, using the committor as a quantitative measure of progress along the transition pathway through the transition state. The solvent environment surrounding the ions is encoded through descriptors based on atom-centered symmetry functions (ACSFs), which serve as input variables for the neural network. In addition, SHapley Additive exPlanations analysis, as an explainable artificial intelligence technique, is applied to identify ACSFs that contribute to the reaction coordinate. A comparative analysis of their correlation with CVs representing water-bridging structures, such as interionic water density and the number of water molecules coordinating both ions, further provides a molecular-level interpretation of the ion association-dissociation mechanism in water. Dissociation (chemistry) ; Ion ; Molecule ; Artificial neural network ; Symmetry (geometry) ; Transition state ; Ion-association ; Solvent effects Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Hyukwon Kwon | Chonghyo Joo | Man Sig Lee | Jaeho Baek | Meng Qi | Jae‐Won Lee | Junghwan Kim
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Knowledge-guided machine learning with an operating time index for selectivity prediction and optimization in industrial acetylene hydrogenation Artificial neural network ; Naphtha ; Ethylene ; Particle swarm optimization ; Acetylene ; Bayesian optimization ; Fluid catalytic cracking ; Decision tree ; Sensitivity (control systems) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis for Biomass Conversion
Authors: Leonid Borisovich Krivdin
Source: Mendeleev Communications (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Statistical methods in the NMR spectral analysis The present review provides a brief summary of the most popular statistical protocols applied in the NMR spectral analysis: machine-learning, neural network, computer assisted structure elucidation, and DP4-based probability methods, which produced a real breakthrough in the interpretation of challenging NMR spectra. Those protocols are a broad family of related machine-learning and artificial neural network statistical treatments, the latter being on the cutting edge of modern statistical science providing an alternative to the state-of-the-art multidimensional NMR experiments, which are often combined with quantum chemical calculations at different levels of theory. Statistical analysis ; Interpretation (philosophy) ; Artificial neural network ; Spectral analysis ; Statistical model ; NMR spectra database ; Quantum chemical ; Statistical parameter Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; NMR spectroscopy and applications
Authors: Ilyes Batatia | William J. Baldwin | Domantas Kuryla | Joseph Hart | Elliott Kasoar | Alin M. Elena | Harry Moore | Mikołaj Jan Gawkowski | Benjamin X. Shi | Venkat Kapil | Panagiotis Kourtis | Ioan-Bogdan Magdău | Gábor Csányi
Source: arXiv (Cornell University) (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry Accurate modelling of electrostatic interactions and charge transfer is fundamental to computational chemistry, yet most machine learning interatomic potentials (MLIPs) rely on local atomic descriptors that cannot capture long-range electrostatic effects. We present a new electrostatic foundation model for molecular chemistry that extends the MACE architecture with explicit treatment of long-range interactions and electrostatic induction. Our approach combines local many-body geometric features with a non-self-consistent field formalism that updates learnable charge and spin densities through polarisable iterations to model induction, followed by global charge equilibration via learnable Fukui functions to control total charge and total spin. This design enables an accurate and physical description of systems with varying charge and spin states while maintaining computational efficiency. Trained on the OMol25 dataset of 100 million hybrid DFT calculations, our models achieve chemical accuracy across diverse benchmarks, with accuracy competitive with hybrid DFT on thermochemistry, reaction barriers, conformational energies, and transition metal complexes. Notably, we demonstrate that the inclusion of long-range electrostatics leads to a large improvement in the description of non-covalent interactions and supramolecular complexes over non-electrostatic models, including sub-kcal/mol prediction of molecular crystal formation energy in the X23-DMC dataset and a fourfold improvement over short-ranged models on protein-ligand interactions. The model's ability to handle variable charge and spin states, respond to external fields, provide interpretable spin-resolved charge densities, and maintain accuracy from small molecules to protein-ligand complexes positions it as a versatile tool for computational molecular chemistry and drug discovery. Electrostatics ; Charge (physics) ; Formalism (music) ; Supramolecular chemistry ; Molecular dynamics ; Force field (fiction) ; Molecule ; Molecular model ; Field (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: K. Keerthi | E. A. Lohith | Sowjanya Vallem | K. Praveena | Dimpul Konwar | Kasibhatta Sivakumar | Rajenahally V. Jagadeesh | N.V.V. Jyothi | Aristides Bakandritsos | Sada Venkateswarlu | Minyoung Yoon | Radek Zbořil
Source: Coordination Chemistry Reviews (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Single-atom coordinated MXenes for organic pollutant detoxification: Mechanistic insights, challenges, and future directions MXenes ; Heteroatom ; Catalysis ; Pollutant ; Characterization (materials science) ; Metal-organic framework MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Catalytic Processes in Materials Science
Authors: Josef Tausendschön | Michael Poelzl | Nikola Petrović | Jason Williams | Elisabeth Fink
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-based time-series forecasting prevents electrode corrosion in organic electrochemistry Real-time sensing and machine learning unite to monitor electrode corrosion and predict reaction dynamics in electrochemical synthesis. Electrode ; Corrosion ; Electrochemical noise ; Electrochemistry ; Corrosion monitoring Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrochemical Analysis and Applications
Authors: Yuuki Funakoshi
Source:  (article, 2026-03-27)
Field: Chemistry — Organic Chemistry

Theoretical modeling of the time-resolved transient absorption spectrum of a molecular nanomotor using machine learning Transient (computer programming) ; Spectrum (functional analysis) ; Absorption (acoustics) ; Pattern recognition (psychology) ; Feature (linguistics) Supramolecular Chemistry and Complexes ; Mechanical and Optical Resonators ; Micro and Nano Robotics
Authors: Yan Xu | Shangshang Wu | Baihan Lv | Yanyan Zhu | Huiran Zhang | Shengzhou Li | Dongbo Dai
Source: JOM (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

A Study on Phase Prediction Methods Based on Wavelet Transform and Deep Learning by XRD Data Deep learning ; Pattern recognition (psychology) ; Wavelet transform ; Phase (matter) ; Wavelet Machine Learning in Materials Science ; Advanced X-ray Imaging Techniques ; Quantum, superfluid, helium dynamics
Authors: Haimei Xu | Xinwei Guan | Lin Liang | Yuanshuai Liu | Zhihao Lei | Xiaoning Li | Shuaiyu Jiang | Zijun Yong | Yibo Ma | Wenping Sun | Hongge Pan | Baohua Jia | Valentin Valtchev | Tianyi Ma
Source: Chemical Society Reviews (article, 2026-01-01)
Field: Chemistry — Inorganic Chemistry

Molecularly engineered zeolites with extra-large-pore architectures and functional opportunities Zeolites are crystalline microporous materials extensively applied in ion exchange, adsorption, separation, and catalysis. However, small-, medium-, and large-pore zeolites with 8-12-membered-ring (MR) frameworks suffer from intrinsic diffusion and reactivity limitations in the conversion of bulky molecules. Recent advances in molecular engineering have enabled the synthesis of extra-large-pore (ELP) frameworks with window sizes exceeding 12-MRs, which bridge the gap between microporous and mesoporous materials. These architecturally unique ELP zeolites facilitate the diffusion of bulky molecules, unlocking opportunities in catalysis, separation, and environmental remediation. This review consolidates the rapidly expanding field of ELP zeolites, providing a comprehensive overview ranging from early germanosilicate and phosphate-based systems to recent high-silica and aluminosilicate ELP frameworks with three-dimensional interconnected pore networks. First, we delineate the structural characteristics of ELP zeolites (ring aperture, framework density, and pore dimensionality) and discuss advanced characterisation techniques that have enabled their precise structural elucidation. We then systematically summarise the synthetic methodologies, encompassing the prevailing 'bottom-up' and 'top-down' strategies, as well as emerging approaches such as high-throughput screening and machine learning-guided framework design. Representative ELP zeolites across phosphate-, germanosilicate-, pure-silica, aluminosilicate-, and heteroatom-containing frameworks are examined with respect to their functional potential in adsorption, separation, and catalysis. Finally, key challenges, including the high cost of multi-step templating, reliance on germanium, structural framework defects, and environmentally unsustainable synthesis methods, are highlighted, along with perspectives on accelerating the development of next-generation ELP zeolites through data-driven design integrated with <i>in situ</i> characterisation. Microporous material ; Mesoporous material ; Aluminosilicate ; Characterization (materials science) ; Metal-organic framework Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ouail Zakary | Weiguo Yin | Niraj Aryal
Source: ChemRxiv (article, 2026-03-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Local Symmetry Breaking and Two-Stage Phase Transition in RuP Uncovered by a Fine-Tuned Atomistic Foundation Model Resolving short-range structural distortions that persist within high-symmetry crystal structures remains a central challenge in materials chemistry. These distortions are dynamic, with correlations extending over nanometer length scales and picosecond timescales, making their atomistic modeling computationally demanding. Here, we address this challenge by modeling the parent compound of ruthenium-pnictide superconductors, RuP, in which such distortions and correlations are prominent. We fine-tune an atomistic foundation model (AFM) on ab initio molecular dynamics datasets and use the resulting interatomic potential to perform machine-learning molecular dynamics simulations of RuP. Analysis of the temperature dependence of lattice parameters and structure factors reveals a two-step phase transition, including an isosymmetric monoclinic-to-monoclinic transition near 180 K, followed by a monoclinic-to-orthorhombic transition near 330 K. Local order metrics and correlation functions further show that short-range monoclinic distortions persist above 330 K within the average high-symmetry phase, providing direct evidence of local symmetry breaking. Electronic-structure calculations reproduce the experimentally observed evolution from an insulating low-temperature state to an intermediate pseudogap-like regime and finally to a metallic high-temperature state, connecting the two-stage electronic transition to the underlying structural transformation. Phonon dispersion analysis reveals the emergence of imaginary phonon modes above the second transition, consistent with the dynamical instability of the average orthorhombic structure and the persistence of local lattice distortions. These findings provide an atomistic picture of local symmetry breaking in RuP and demonstrate that fine-tuned AFMs provide a practical approach for quantifying short-range distortions and correlated fluctuations in crystals with complex phase behavior. Phase transition ; Symmetry breaking ; Lattice (music) ; Monoclinic crystal system ; Molecular dynamics ; Phonon ; Orthorhombic crystal system ; Local symmetry ; Crystal structure Iron-based superconductors research ; Rare-earth and actinide compounds ; Machine Learning in Materials Science
Authors: Tianhao Su | Shunbo Hu | Yue Wu | Runhai Oyang | X. -D. Wang | Musen Li | Jeffrey R. Reimers | Tong‐Yi Zhang
Source: Research Square (preprint, 2026-03-20)
Field: Materials Science — Materials Chemistry

DeePAW: A universal machine learning model for orbital-free ab initio calculations Ab initio ; Density functional theory ; Electronic structure ; Enhanced Data Rates for GSM Evolution ; Frontier ; Ab initio quantum chemistry methods ; Deep learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Kevin Maik Jablonka
Source: ArXiv.org (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Clever Materials: When Models Identify Good Materials for the Wrong Reasons Machine learning can accelerate materials discovery. Models perform impressively on many benchmarks. However, strong benchmark performance does not imply that a model learned chemistry. I test a concrete alternative hypothesis: that property prediction can be driven by bibliographic confounding. Across five tasks spanning MOFs (thermal and solvent stability), perovskite solar cells (efficiency), batteries (capacity), and TADF emitters (emission wavelength), models trained on standard chemical descriptors predict author, journal, and publication year well above chance. When these predicted metadata ("bibliographic fingerprints") are used as the sole input to a second model, performance is sometimes competitive with conventional descriptor-based predictors. These results show that many datasets do not rule out non-chemical explanations of success. Progress requires routine falsification tests (e.g., group/time splits and metadata ablations), datasets designed to resist spurious correlations, and explicit separation of two goals: predictive utility versus evidence of chemical understanding. Spurious relationship ; Metadata ; Benchmark (surveying) ; Property (philosophy) ; Predictive modelling ; Test (biology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Big Data and Digital Economy