jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xupeng Wang | Wei Gao | Chaokun Huang | Yanlong Luo | Xiujuan Wang | Lin Yong
Source: Chemical Engineering Journal (article, 2026-01-26)
Field: Chemistry — Organic Chemistry

Inspired by natural antioxidants: Design of high-performance phenolic antioxidants based on machine learning and its application in NR Antioxidant ; Gallic acid ; Butylated hydroxyanisole ; Bond-dissociation energy ; Syringic acid ; Phenols ; Vulcanization ; Thermal stability Free Radicals and Antioxidants ; Thermal and Kinetic Analysis ; Polymer composites and self-healing
Authors: Danila Shiryaev | Balaranjan Selvaratnam | Yujing Sun | Emil Jaffal | Anton O. Oliynyk
Source: Journal of Chemical Education (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Exploring Feature Engineering for Crystal Structure Classification: Interactive Applications of PCA and PLS-DA Clustering Active learning through interactive exploration significantly enhances student engagement and understanding of chemistry. This educational activity demonstrates Principal Component Analysis (PCA) and Partial-Least-Square-Discriminant Analysis (PLS-DA), two foundational machine learning techniques widely applied in contemporary research. Interactive Python-based programs offer accessible educational platforms for students exploring chemical data, requiring no prior programming experience. This application allows learners to actively engage in feature exploration and dimensionality reduction processes, applied to clustering and classifying binary AB equiatomic solid state compounds. Students can actively select and modify chemical and physical features, observing in real time how these choices impact the effectiveness of the PCA and PLS-DA clustering models. Initially, PCA enables unsupervised visualization of natural clustering and correlations among compounds without prior labeling. Subsequently, by employing PLS-DA, students develop supervised models capable of predicting crystal structures, explicitly illustrating supervised versus unsupervised learning paradigms. The activity highlights the importance of explainability in machine learning models rather than operating the models as a ″black box″. Beyond learning fundamental concepts, the activity encourages students to participate in genuine exploratory processes, mirroring the investigative approaches historically utilized by researchers and practiced today. By experimenting freely with data sets and computational methods, students experience firsthand the iterative nature of scientific discovery, fostering deeper insight into both chemical informatics and the broader research methodology. Mirroring ; Cluster analysis ; Dimensionality reduction ; Visualization ; Unsupervised learning ; Feature (linguistics) ; Principal component analysis ; Cheminformatics ; Data visualization Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Jigyasa Nigam | Tess Smidt | Geneviève Dusson
Source: ArXiv.org (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Machine learning electronic structure and atomistic properties from the external potential Electronic structure calculations remain a major bottleneck in atomistic simulations and, not surprisingly, have attracted significant attention in machine learning (ML). Most existing approaches learn a direct map from molecular geometries, typically represented as graphs or encoded local environments, to molecular properties or use ML as a surrogate for electronic structure theory by targeting quantities such as Fock or density matrices expressed in an atomic orbital (AO) basis. Inspired by the Hohenberg-Kohn theorem, in this work, we propose an operator-centered framework in which the external (nuclear) potential, expressed in an AO basis, serves as the model input. From this operator, we construct hierarchical, body-ordered representations of atomic configurations that closely mirror the principles underlying several popular atom-centered descriptors. At the same time, the matrix-valued nature of the external potential provides a natural connection to equivariant message-passing neural networks. In particular, we show that successive products of the external potential provide a scalable route to equivariant message passing and enable an efficient description of long-range effects. We demonstrate that this approach can be used to model molecular properties, such as energies and dipole moments, from the external potential, or learn effective operator-to-operator maps, including mappings to the Fock matrix and the reduced density matrix from which multiple molecular observables can be simultaneously derived. Bottleneck ; Electronic structure ; Scalability ; Equivariant map ; Construct (python library) ; Dipole ; Artificial neural network ; Observable Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Amelia Chen | Bhargavi Krishna | Christina Hoffmann | Gary Taufer | Jonathan Edelen | Boaz Nash | Morgan Henderson | Stuart Calder
Source: Open MIND (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Automation of sample identification for neutron beamlines Neutron scattering experiments are a critical tool for the investigation of molecular structure in compounds. The HB-2A neutron powder diffractometer at the High Flux Isotope Reactor at ORNL conducts magnetic studies of samples by illuminating them with different energy neutron beams and recording the scattered neutrons. Proper identification and alignment of samples during an experiment is key to ensuring high quality data is collected. At present, this process is performed manually by beamline scientists. RadiaSoft, in collaboration with the beamline scientists and engineers at ORNL, has developed a machine learning-based software automating sample identification. We utilize a fully connected convolutional neural network configured in a U-Net architecture to identify the sample and its center of mass. We then move the sample using a custom Python-based EPICS IOC interfaced with the motors. In this poster, we provide an overview of our machine learning tools and show our results identifying samples at ORNL. Beamline ; Automation ; Sample (material) ; Neutron ; Software ; Identification (biology) ; Neutron scattering ; Research reactor ; Process (computing) Machine Learning in Materials Science ; Nuclear Physics and Applications ; Boron Compounds in Chemistry
Authors: Sri Vishnu Jami | Sakti Prasanna Muduli | Paresh Kale
Source: Journal of the Korean Physical Society (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Predicting magnetron sputtering deposition rate through process parameters using supervised machine learning Sputter deposition ; Deposition (geology) ; Sputtering ; Microelectronics ; Physical vapor deposition ; Supervised learning ; Cavity magnetron ; Process (computing) Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Surface Roughness and Optical Measurements
Authors: Y. G. Li | Zhichen Pu | Lijiang Yang | Zehao Zhou | Yi Qin Gao
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Multi-objective fluorescent molecule design with a data-physics dual-driven generative framework Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectives and constraints. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning prediction, and the prohibitive cost of quantum chemical calculation. Here we present LUMOS, a data-and-physics driven framework for inverse design of fluorescent molecules. LUMOS couples generator and predictor within a shared latent representation, enabling direct specification-to-molecule design and efficient exploration. Moreover, LUMOS combines neural networks with a fast time-dependent density functional theory (TD-DFT) calculation workflow to build a suite of complementary predictors spanning different trade-offs in speed, accuracy, and generalizability, enabling reliable property prediction across diverse scenarios. Finally, LUMOS employs a property-guided diffusion model integrated with multi-objective evolutionary algorithms, enabling de novo design and molecular optimization under multiple objectives and constraints. Across comprehensive benchmarks, LUMOS consistently outperforms baseline models in terms of accuracy, generalizability and physical plausibility for fluorescence property prediction, and demonstrates superior performance in multi-objective scaffold- and fragment-level molecular optimization. Further validation using TD-DFT and molecular dynamics (MD) simulations demonstrates that LUMOS can generate valid fluorophores that meet various target specifications. Overall, these results establish LUMOS as a data-physics dual-driven framework for general fluorophore inverse design. Generalizability theory ; Property (philosophy) ; Workflow ; Inverse ; Generator (circuit theory) ; Fluorescence ; Chemical space ; Fluorophore Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Trevor David Rhone | Dylan Sheils | Yoshiharu Krockenberger
Source: ACS Omega (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of BCS Superconductors without BCS Theory We show that machine learning (ML) approaches can expedite the future discoveries of BCS superconductors, providing a faster computationally inexpensive alternative to density functional theory for estimating whether a material is a BCS superconductor. The key to the technological applications of superconductors is the superconducting critical temperature. Although the electron pairing mechanism for unconventional superconductors is not known, the pairing mechanism for BCS superconductors is well understood by the Eliashberg function and the McMillan equation. Nevertheless, the first-principles calculations needed are exceedingly expensive. For this reason, a rapid screening of candidate BCS superconductors and their <i>T</i> <sub>c</sub> using first-principles calculations is prohibitive. We leverage machine learning to eliminate the need for first-principles calculations when predicting whether or not a material is a BCS superconductor. A database of experimentally relevant crystal structure data is carefully curated and materials descriptors suitable for describing the experimental data are chosen. Machine learning models are trained to classify if a material is a BCS superconductor. This framework provides a roadmap for accelerating the discovery of novel BCS superconductors without BCS theory. Superconductivity ; BCS theory ; Computational learning theory ; High-temperature superconductivity Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Physics of Superconductivity and Magnetism
Authors: Tarek Khater | Aamna AlShehhi | Thu Bao Nguyen Le | Vincent Chan | Vi Khanh Truong
Source: ACS Materials Letters (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Bayesian Optimization-Guided Discovery of Gallium-Containing Semiconductors with Targeted Band Gaps Gallium-based semiconductors provide tunable electronic and optical properties across different oxidation states and compositional environments. However, rational discovery of gallium-containing materials with targeted band gaps is difficult, given the vast chemical space and expensive first-principles screening. In this study, we present a machine-learning-guided Bayesian optimization (BO) framework that enables inverse design of Ga-based compositions with predefined electronic properties while maintaining chemical plausibility. Multiple regression models were evaluated, and KNN emerged as the optimal surrogate (R2 = 0.812). Using the KNN predictor, BO proposes novel Ga-containing compositions targeting band gaps from 0.5–3.5 eV. Chemical plausibility is enforced via SMACT screening (charge balance, elemental feasibility, physical plausibility). BO adaptively steers the search toward regions of highly expected improvement. Postoptimization analyses show 100% uniqueness and novelty relative to training data, with increased SMACT validity near 1.5–2.5 eV. The framework accelerates inverse design under realistic chemical constraints, offering an alternative to purely DFT-centric screening. Bayesian optimization ; Bayesian probability ; Inverse ; Novelty ; Chemical space ; Semiconductor ; Uniqueness ; Band gap Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Heusler alloys: electronic and magnetic properties
Authors: Y. G. Li | Zhichen Pu | Lijiang Yang | Zehao Zhou | Yi Qin Gao
Source: arXiv (Cornell University) (preprint, 2026-01-20)
Field: Materials Science — Materials Chemistry

Multi-objective fluorescent molecule design with a data-physics dual-driven generative framework Designing fluorescent small molecules with tailored optical and physicochemical properties requires navigating vast, underexplored chemical space while satisfying multiple objectives and constraints. Conventional generate-score-screen approaches become impractical under such realistic design specifications, owing to their low search efficiency, unreliable generalizability of machine-learning prediction, and the prohibitive cost of quantum chemical calculation. Here we present LUMOS, a data-and-physics driven framework for inverse design of fluorescent molecules. LUMOS couples generator and predictor within a shared latent representation, enabling direct specification-to-molecule design and efficient exploration. Moreover, LUMOS combines neural networks with a fast time-dependent density functional theory (TD-DFT) calculation workflow to build a suite of complementary predictors spanning different trade-offs in speed, accuracy, and generalizability, enabling reliable property prediction across diverse scenarios. Finally, LUMOS employs a property-guided diffusion model integrated with multi-objective evolutionary algorithms, enabling de novo design and molecular optimization under multiple objectives and constraints. Across comprehensive benchmarks, LUMOS consistently outperforms baseline models in terms of accuracy, generalizability and physical plausibility for fluorescence property prediction, and demonstrates superior performance in multi-objective scaffold- and fragment-level molecular optimization. Further validation using TD-DFT and molecular dynamics (MD) simulations demonstrates that LUMOS can generate valid fluorophores that meet various target specifications. Overall, these results establish LUMOS as a data-physics dual-driven framework for general fluorophore inverse design. Generalizability theory ; Property (philosophy) ; Workflow ; Inverse ; Generator (circuit theory) ; Fluorescence ; Chemical space ; Fluorophore Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Nikita Yu Nikitin | R. Khmyrov | Pavel Podrabinnik | Nestor Solis Pinargote | Anton Smirnov | Idarmach Idarmachev | Tatiana Tarasova | S.N. Grigoriev
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Creating synthetic datasets using the Monte Carlo method for training, testing, and validating models based on machine learning methods Monte Carlo method ; Copula (linguistics) ; Experimental data ; Synthetic data ; Latin hypercube sampling ; Artificial neural network ; Graph ; Centrality Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Gaussian Processes and Bayesian Inference
Authors: Karim Ben Hicham | Moreno Ascani | Jan G. Rittig | Alexander Mitsos
Source: arXiv (Cornell University) (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Differentiable Thermodynamic Phase-Equilibria for Machine Learning Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure into neural networks have recently shown strong performance for activity-coefficient modeling. However, extending such approaches to equilibrium data arising from an extremum principle, such as liquid-liquid equilibria, remains difficult. Here we present DISCOMAX, a differentiable algorithm for phase-equilibrium calculation that guarantees thermodynamic consistency at both training and inference, only subject to a user-specified discretization. The method is rooted in statistical thermodynamics, and works via a discrete enumeration with subsequent masked softmax aggregation of feasible states, and together with a straight-through gradient estimator to enable physics-consistent end-to-end learning of neural $g^{E}$-models. We evaluate the approach on binary liquid-liquid equilibrium data and demonstrate that it outperforms existing surrogate-based methods, while offering a general framework for learning from different kinds of equilibrium data. Artificial neural network ; Differentiable function ; Consistency (knowledge bases) ; Estimator ; Softmax function ; Binary number Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems
Authors: Jiang Zou | Hongbo Gao | Duo Wang | Yunquan Chen | Shiyou Deng | Nuo Shi | Yang Song | Chunlin Huang | Dingchun Zi | Yu Du | Yuxiang Bai | N. WANG | Ge Wang | Zhengling Liu | Junhua Zhang | Peng Zhou
Source: Frontiers in Plant Science (article, 2026-02-24)
Field: Chemistry — Analytical Chemistry

Enabling rapid and accurate grand discrimination of flue-cured tobacco: a near-infrared hyperspectral and machine learning approach To address the inefficiency and subjectivity of manual grading, this study established a machine learning model based on near-infrared hyperspectral data (950-1650 nm) for the accurate classification of first-roasted tobacco grades. Multivariate statistical analysis uncovered the intrinsic correlations among grade, spectral data, and chemical composition, thereby laying a theoretical foundation for hyperspectral-based grading technology. Three preprocessing methods (namely, multiplicative scatter correction (MSC), standard normal variate transformation, and Savitzky-Golay convolutional smoothing) and four classification models (namely, random forest, backpropagation neural network, extreme learning machine, and partial least squares-discriminant analysis (PLS-DA)) were employed. Moreover, characteristic bands were selected through the successive projections algorithm (SPA) and competitive adaptive reweighted sampling to investigate how the number of characteristic bands affects the grade classification accuracy. The results showed that rank exhibited highly significant correlations with nicotine, reducing sugars, total sugars, and sugar-nicotine ratio, and that spectra exhibited highly significant correlations with nicotine. The classification accuracy of full-band MSC preprocessing combined with the PLS-DA model reached 98.5%, while the classification accuracy reached 94.0% when using 70% of the full bands selected using the SPA. In conclusion, near-infrared hyperspectroscopy combined with machine learning not only offers high efficiency, accuracy, and non-destructiveness in the grading of first-roasted tobacco leaves but also provides a theoretical basis for industrial hyperspectral grading by elucidating the correlations among spectrum, chemical composition, and grade. This method avoids the subjectivity of manual grading and offers key technical support to advance the intelligence and automation of first-roasted tobacco leaf grading in the tobacco industry. Hyperspectral imaging ; Preprocessor ; Pattern recognition (psychology) ; Convolutional neural network ; Grading (engineering) ; Data pre-processing ; Extreme learning machine ; Pairwise comparison ; Artificial neural network Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Ryan Tyler | Masengo Ilunga | Arjun Maity | Pramod K. Sinha
Source:  (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Predicting Conductivity of Typical Nanocomposite Materials Using Extreme Gradient Boosting Machine Learning: A Comparative Analysis between Optuna and Grid Search Cross-Validation Hyperparameter Tuning Hyperparameter ; Hyperparameter optimization ; Grid ; Boosting (machine learning) ; Conductivity ; Gradient boosting Machine Learning in Materials Science ; Machine Learning and ELM ; Smart Materials for Construction
Authors: Elisa Roldan | Tasneem Sabir
Source: ArXiv.org (article, 2026-02-09)
Field: Materials Science — Biomaterials

SpinCastML an Open Decision-Making Application for Inverse Design of Electrospinning Manufacturing: A Machine Learning, Optimal Sampling and Inverse Monte Carlo Approach Electrospinning is a powerful technique for producing micro to nanoscale fibers with application specific architectures. Small variations in solution or operating conditions can shift the jet regime, generating non Gaussian fiber diameter distributions. Despite substantial progress, no existing framework enables inverse design toward desired fiber outcomes while integrating polymer solvent chemical constraints or predicting full distributions. SpinCastML is an open source, distribution aware, chemically informed machine learning and Inverse Monte Carlo (IMC) software for inverse electrospinning design. Built on a rigorously curated dataset of 68,480 fiber diameters from 1,778 datasets across 16 polymers, SpinCastML integrates three structured sampling methods, a suite of 11 high-performance learners, and chemistry aware constraints to predict not only mean diameter but the entire distribution. Cubist model with a polymer balanced Sobol D optimal sampling provides the highest global performance (R2 > 0.92). IMC accurately captures the fiber distributions, achieving R2 > 0.90 and <1% error between predicted and experimental success rates. The IMC engine supports both retrospective analysis and forward-looking inverse design, generating physically and chemically feasible polymer solvent parameter combinations with quantified success probabilities for user-defined targets. SpinCastML reframes electrospinning from trial and error to a reproducible, data driven design process. As an open source executable, it enables laboratories to analyze their own datasets and co create an expanding community software. SpinCastML reduces experimental waste, accelerates discovery, and democratizes access to advanced modeling, establishing distribution aware inverse design as a new standard for sustainable nanofiber manufacturing across biomedical, filtration, and energy applications. Monte Carlo method ; Inverse ; Electrospinning ; Sampling (signal processing) ; Nanofiber ; Sobol sequence ; Fiber ; Design of experiments Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Machine Learning in Materials Science