jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jessica A. Martinez B. | Bhaskar Rana | Xuecheng Shao | Katarzyna Pernal | Michele Pavanello
Source: Open MIND (preprint, 2026-03-06)
Field: Materials Science — Materials Chemistry

Machine learning the two-electron reduced density matrix in molecules and condensed phases Machine learning is rapidly accelerating materials and chemical discovery, but most current models target energies, forces, or selected molecular properties rather than the underlying many-body electronic structure. Learning electronic-structure proxies, such as reduced density matrices, offers a path to surrogates that can predict a broad range of observables from a single ML model. Short of learning the full wavefunction, the two-electron reduced density matrix (2-RDM) is among the most information-rich, minimally lossy targets, providing direct access to expectation values of arbitrary one- and two-electron operators regardless of the strength of the underlying electron correlation. Here we show that learning the 2-RDM is a feasible goal, yielding exceptionally accurate models. We develop surrogates for correlated wavefunction methods (including configuration interaction and coupled cluster) that yield 2-RDMs with sufficient fidelity to provide direct, training-free access to energies and forces for driving energy-conserving molecular dynamics. To tackle realistic molecular condensed phases, we leverage a many-body expansion of the 2-RDM, using our ML models to supply the expansion terms and enabling ML-powered, coupled-cluster-quality electronic structure and energetics for large solvated systems. As a demonstration, we showcase a coupled-cluster-level electronic-structure calculation of glucose solvated by 500 water molecules achieved at Hartree-Fock cost. This work establishes a general framework for learning correlated electronic structure with high fidelity and deploying it to systems beyond the reach of conventional ab initio methods. Leverage (statistics) ; Density functional theory ; Ab initio ; Electronic structure ; Observable ; Wave function ; Molecule ; Range (aeronautics) ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Shengsheng Wei | Zipeng Yang | Chao Yang | Hongmei Zhao | Yang Li | Yuanyuan Guo | Andong Xia | Zhuoran Kuang
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Unveiling key descriptors <i>via</i> machine learning: toward rational molecular design of chromophores with excited-state intramolecular proton transfer Precise design of excited-state intramolecular proton transfer (ESIPT) molecules targeting advanced optoelectronic or biological sensing applications presents a fundamental challenge. Controlling the energy difference (Δ<i>E</i>*) between normal (N*) and tautomeric (T*) excited-state forms is crucial, yet the complex interplay of hydrogen bond (H-bond) strength, proton donor acidity, and proton acceptor basicity with Δ<i>E</i>* remains insufficiently explored. Conventional trial-and-error approaches for designing tailored ESIPT compounds suffer from inefficient synthesis. To address this, we constructed a high-quality ESIPT dataset by introducing ten substituents with progressively increasing electron-donating capacity into six representative ESIPT parent scaffolds. Integrating qualitative descriptors with data-driven machine learning (ML) enabled precise Δ<i>E</i>* prediction, significantly accelerating high-throughput screening. An interpretable Shapley additive explanations (SHAP)-based ML approach was applied to evaluate the relative importance of key H-bond descriptors while achieving accurate Δ<i>E</i>* prediction. Novel ESIPT candidates were generated using a variational autoencoder (VAE) model and filtered using predicted Δ<i>E</i>*, synthetic accessibility (SA) scores, and pharmacokinetic properties. Critically, we synthesized two AI-designed ESIPT molecules exhibiting distinct N*/T* dual emission, which provides a closed-loop experimental validation of this data-driven molecular design strategy. This work establishes a predictive framework for accurate Δ<i>E</i>* determination and accelerated exploitation of novel promising ESIPT compounds. Intramolecular force ; Tautomer ; Proton ; Molecule ; Chromophore ; Autoencoder ; Hydrogen bond Photochemistry and Electron Transfer Studies ; Molecular Sensors and Ion Detection ; Photochromic and Fluorescence Chemistry
Authors: Viet D. Nguyen | Sachchida Nand | Ramon Trevino | Arka Porey | William T. Thompson | Graham C. Haug | Seth O. Fremin | Shree Krishna Dhakal | William B Hughes | Dylan P Moran | Johant Lakey‐Beitia | Chandan Kumar Giri | Patrick Manning-Lorino | Samuel G. Greco | Hadi D. Arman | Oleg V. Larionov
Source: ChemRxiv (article, 2026-02-16)
Field: Chemistry — Organic Chemistry

Cyclic Sulfone Ring Remodeling Enables Molecular Shape Diversity-Oriented Synthesis of Privileged Biaryl and Oligoaryl Motifs Molecular shape is a key determinant of the structural properties and intermolecular interactions of privileged scaffolds, such as biaryl and oligoaryl compounds, which are widely used in the design of drugs, catalysts, and functional materials. Despite the well-recognized importance of molecular shape diversity and increased three-dimensionality, the chemical space of biaryl compounds remains dominated by linear and disk-like structures featuring para and meta substitution patterns. In contrast, more sphere-like bis-ortho-substituted biaryl scaffolds have been underexplored, because of the dearth of synthetic methods for generating focused libraries of ortho analogs in a synthetically efficient manner. We report herein the development of regioselective nucleophile/electrophile couple-mediated cyclic sulfone ring remodeling that can streamline access to a wide range of biaryl and oligoaryl scaffolds, featuring aliphatic and (hetero)aromatic amines, phenols, (thio)ethers, and phosphines. To facilitate prediction of the regiochemistry of structurally complex biaryl products, a machine learning model based on the previously unexplored Bernoulli Naïve Bayes classifier is developed. The integrated experimental, computational, and data-driven approach charts the course for mitigating structural biases and exploring the underpopulated biaryl chemical space in medicinal chemistry, catalysis, and materials science. Chemical space ; Regioselectivity ; Ring (chemistry) ; Structural motif ; Sulfone ; Intermolecular force ; Molecular conformation ; Chemical synthesis Axial and Atropisomeric Chirality Synthesis ; Catalytic Cross-Coupling Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: D. N. Chistikov | V. E. Bochenkov | D. A. Firsov | V. V. Korolev | A. V. Bochenkova
Source: Moscow University Chemistry Bulletin (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Application of Neural Networks for Investigating Potential Energy Surfaces of Electronically Excited States and Internal Conversion Mechanisms of Organic Molecules Based on non-empirical data obtained using a high-level multi-configurational quantum chemistry method, neural networks with the architectures of a multilayer perceptron and an E(3)-equivariant graph network were constructed and trained to predict the energies of the ground and the first two electronically excited states of the methaniminium cation, $${\text{C}}{{{\text{H}}}_{2}}{\text{NH}}_{2}^{ + }$$ . It is shown that the E(3)-equivariant graph neural network architecture demonstrates higher accuracy. Using the trained network, a segment of the cation’s potential energy surfaces near the region of the conical intersection between the first excited and the ground states was investigated; this region plays an important role in the mechanism of internal conversion and photoisomerization reactions. It is demonstrated that the neural network accurately reproduces the topography of the potential energy surfaces of the two electronic states in the region of their conical intersection. Excited state ; Conical intersection ; Artificial neural network ; Conical surface ; Potential energy ; Photoisomerization ; Quantum ; Internal conversion Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Spectroscopy and Quantum Chemical Studies
Authors: Rui Zhou | Yilin Fang | Zexin Jiang | Shuchang Li | Jintao Wang | G. Wang | Yuanyuan Wang | Ning Gao
Source: Materials Today Communications (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Coupling effects of temperature and vacancy clusters on the mechanical properties of UO2: Insights from machine learning molecular dynamics Irradiation in nuclear fuels generates vacancy clusters whose coupled impact with temperature on the mechanical integrity of uranium dioxide (UO 2 ) remains difficult to quantify across relevant length and time scales. Here we develop an on-the-fly machine-learning force field (MLFF) for UO 2 with vacancy-defect clusters, trained via on-the-fly machine learning on a high-throughput density functional theory (DFT) dataset and deployed for massively parallel molecular dynamics simulations in LAMMPS. The MLFF reproduces key elastic properties of UO 2 in close agreement with molecular dynamics simulation results and available experiments, enabling efficient simulations over 300–1500 K and 1–5% vacancy concentrations. We find that vacancy clusters and thermal perturbations act cooperatively to produce an approximately linear degradation of the bulk modulus and shear modulus, with the degradation rate increasing systematically with both vacancy concentration and cluster size. Beyond mechanical softening, rising temperature and vacancy concentration expose the limitations of the force field under high-entropy configurations, resulting in inadequate description of vacancy cluster evolution and growth by the MLFF. These results provide valuable insights for the further development and optimization of MLFF, establishing a predictive framework for the performance evaluation of uranium-based nuclear fuels. Vacancy defect ; Molecular dynamics ; Cluster (spacecraft) ; Coupling (piping) ; Field (mathematics) ; Density functional theory ; Thermal ; Fullerene Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear reactor physics and engineering
Authors: Vashishtha B. Patel | Sumit Kumar | Ojus Mohan
Source: Industrial & Engineering Chemistry Research (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Density Functional Theory Approach for Predicting Activation Barriers and Reaction Pathways in Methane-to-C <sub>2</sub> Conversion Computational advancements have accelerated density functional theory (DFT)-based catalyst design for CH4 conversion to value-added products. While the C1 reaction network study is computationally less demanding, exploring the C2 network comprising nearly 250 elementary reactions is challenging. To address this, we developed two machine learning (ML) models to predict activation energies correlating six and 37 descriptors, respectively. Among the models developed, gradient boost regression (GBR) with 37 descriptors exhibited the lowest mean absolute error (0.177 eV), predicting accurate C2 barriers. Leveraging this, we adopted a combined strategy: rate-determining step identification by GBR, followed by DFT validation. Applying this approach to oxidative coupling of methane on Ni(111) and NiB surfaces revealed that boron doping significantly lowers the CH2* dimerization barrier from 0.84 eV on Ni(111) to 0.49 eV, forming a C2 intermediate. The resulting ethylene subsequently hydrogenates to ethane while the integrated ML-DFT approach confirmed that competing desorption, hydroxylation, and oxidation exhibited higher barriers. Density functional theory ; Catalysis ; Coupling (piping) ; Artificial neural network ; Feature (linguistics) ; Ethylene ; Boron ; Methane ; Regression Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Hilal Ozyurt | Gokhan Onder Aksu | Hasan Can Gülbalkan | Seda Keskin
Source: Industrial & Engineering Chemistry Research (article, 2026-02-15)
Field: Materials Science — Materials Chemistry

ReDD-COFFEE under the Lens: Revealing Adsorption and Separation Performances of Hypothetical COFs Using Molecular Simulations and Machine Learning In this work, we performed a high-throughput computational screening approach combining Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML) to unlock the potential of the ReDD-COFFEE (Ready-to-use and Diverse Database of Covalent Organic Frameworks with Force field-based Energy Evaluation) database for gas adsorption and separation applications. Molecular simulations were first employed to assess CO<sub>2</sub>, CH<sub>4</sub>, H<sub>2</sub>, N<sub>2</sub> and O<sub>2</sub> uptakes of acylhydrazone-, azine-, and triazine-based hypothetical COFs (hypoCOFs). These data were then leveraged to train ML models capable of predicting adsorption properties for nearly 25000 different types of materials. Adsorption selectivities of ReDD-hypoCOFs were computed for six important gas separations: CO<sub>2</sub>/CH<sub>4</sub>, CO<sub>2</sub>/H<sub>2</sub>, CO<sub>2</sub>/N<sub>2</sub>, CH<sub>4</sub>/H<sub>2</sub>, CH<sub>4</sub>/N<sub>2</sub>, and O<sub>2</sub>/N<sub>2</sub>. Structure-performance analyses performed using molecular fingerprinting on top-selective materials demonstrated that nitrogen enriched aromatic rings and fluorinated linkers in addition to narrow pores (<10 Å) and low porosities (<0.7) collectively strengthen the CO<sub>2</sub> affinity of ReDD-hypoCOFs. Adsorption ; Monte Carlo method ; Covalent bond ; Grand canonical ensemble ; Separation (statistics) ; Molecular descriptor ; Molecule Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies
Authors: Tianci Cui | Yixian Peng
Source:  (article, 2026-01-23)
Field: Materials Science — Materials Chemistry

Explainable Machine Learning for Structural Materials: SHAP-Enhanced Ensemble Prediction of Concrete Compressive Strength Accurate prediction of concrete compressive strength is critical for structural design and quality assurance, yet traditional methods struggle with complex non-linear relationships between mixture proportions and mechanical properties. This study proposes a two-layer ensemble learning framework integrated with SHAP-based interpretability for transparent strength forecasting. The architecture employs seven diverse base learners (Random Forest, K-Nearest Neighbors, Deep Neural Network, Stacking, CatBoost, Support Vector Machine, Gaussian Process Regression) in the first layer, followed by a LightGBM meta-learner that adaptively synthesizes predictions to achieve superior accuracy (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>=0.948, RMSE=4.161 MPa). SHAP analysis quantifies global feature importance, identifying age (mean |SHAP|=1.5) and water content (mean |SHAP|=1.2) as dominant factors, while waterfall plots provide instance-level explanations for mixture optimization. Experimental validation on 1,030 concrete specimens demonstrates the framework's dual capability of delivering accurate predictions and actionable engineering insights, advancing interpretable AI applications in structural materials design. Interpretability ; Feature engineering ; Support vector machine ; Artificial neural network ; Compressive strength ; Feature (linguistics) ; Ensemble learning ; Gaussian process ; Process (computing) ; Quality (philosophy) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Explainable Artificial Intelligence (XAI)
Authors: Yonggang Shi | Ruomei Zhao | Qiang Xu | Yuanhui Wang | Baofeng Jin | Yanling Zhang | Xiaoyuan Tian | Jianjun Liu | Hanping Zhou | Shuoye Zhou | Weimin Guo | Jiajie Huang
Source: Industrial Crops and Products (article, 2026-03-26)
Field: Chemistry — Analytical Chemistry

In-situ and non-destructive monitoring of chemical components in tobacco leaf during curing based on multimodal data and machine learning Precise, in-situ, and non-destructive monitoring of chemical composition is critical for optimizing tobacco curing, yet remains challenging under complex and dynamic curing conditions. In this study, a multimodal online monitoring framework was developed by integrating visible-light imaging with temperature and relative humidity sensing to predict key chemical components during the curing process. Colorimetric features extracted from RGB, Lab, and YUV color models, together with environmental parameters, were used as inputs to machine learning models trained on datasets collected from two representative tobacco-growing regions, and external data was to test the models’ performance and robustness. The proposed framework achieved high predictive accuracy for carbohydrates and selected amino acids, with validation R 2 values exceeding 0.90 and satisfactory performance on external test data. SHapley Additive exPlanations (SHAP) analysis further revealed that relative humidity and V value (chrominance component in the YUV color model) were the dominant predictors for most components, while temperature played a key role in alanine variation. These results demonstrate the feasibility of using multimodal visual and environmental data for low-cost, in-situ estimation of chemical composition during tobacco curing, providing a practical tool for process monitoring and quality optimization. • In-situ multimodal acquisition system integrates image, temperature, and humidity monitoring during tobacco leaf curing. • Machine learning-based multimodal data analysis enables accurate prediction of chemical composition. • SHAP interpretation reveals the contribution of multimodal features to prediction models. • Visualization of compositional distribution captures dynamic changes throughout the curing process. Curing (chemistry) ; Tobacco leaf ; Pattern recognition (psychology) ; Multimodality ; Machine vision Spectroscopy and Chemometric Analyses ; Plant Surface Properties and Treatments ; Plant Physiology and Cultivation Studies
Authors: Muhammad Tasnood Anwar
Source:  (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Deep Learning-Based Prediction of Mechanical Properties of Steels and Alloys Using Chemical Composition Data Chemical composition ; Weldability ; Cementite ; Welding ; Deformation (meteorology) Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Artem  A. Bokarev | Artem A. Kabanov
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Evaluation of Elastic Properties of Solids Using Machine Learning, Molecular Dynamics and Simple Relationships: A Case Study of Carbon Allotropes Molecular dynamics ; Density functional theory ; Carbon fibers ; Simple (philosophy) ; Property (philosophy) ; Force field (fiction) Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Crystallography and molecular interactions
Authors: Wei Xue | Junli Wang | Wenbo Luo | Yanke Guo | Youhua Yan | Chen Wen | Shaoxiang Li | Andreas Züttel | Zhiyong Xue | Weihua Wang
Source: International Journal of Hydrogen Energy (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Accelerated discovery of low-cost Mg-based solid-state H2 storage alloys for enhanced hydrogen des-/absorption behavior via machine learning-guided binary-element minor doping Hydrogen storage ; Electronegativity ; Valence (chemistry) ; Desorption ; Dielectric ; Doping ; Hydrogen ; Softmax function Hydrogen Storage and Materials ; Metalloenzymes and iron-sulfur proteins ; Magnesium Alloys: Properties and Applications
Authors: Florian Rothenhäusler | Simon Knorr | Seema Agarwal | Holger Ruckdaeschel
Source: Applied Research (article, 2026-03-09)
Field: Materials Science — Polymers and Plastics

Starch Phosphate Carbamate Synthesis in the Age of Machine Learning: Maximization of the Phosphorus Content and the Educt Efficiency via Bayesian Optimization ABSTRACT Starch phosphate carbamates (SPC) have been a focal point in the research and development of sustainable flame retardants (FRs) for the past two decades. Within this framework, there is a notable emphasis on obtaining SPC with a high phosphorus content. Aligning with the contemporary trend of artificial intelligence and machine learning, the optimization of synthesis conditions for specific product properties can be efficiently achieved through their utilization. Hence, the objective of this study is to optimize the synthesis conditions, including the molar ratio of anhydroglucose units to urea and phosphoric acid, as well as the synthesis temperature and duration. The optimization is conducted via Bayesian optimization, aiming to maximize the phosphorus content and educt efficiency of SPC to obtain a potent, bio‐based FR. As a result, the phosphorus content and educt efficiency are increased to about 20.5% and 11.3%/mol, respectively. Phosphorus ; Molar ratio ; Phosphate ; Maximization ; Urea ; Ethyl carbamate Flame retardant materials and properties ; Industrial Gas Emission Control ; Phosphorus and nutrient management
Authors: Chengyu Zhou | Qing Zhao
Source: ChemRxiv (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Computational Progress of Designing Single Atom Alloy Catalysts for Methane Activation Nonoxidative coupling of methane to value-added hydrocarbons remains a major challenge in catalysis due to inert C-H bonds in methane and rapid catalyst deactivation caused by coke contamination. Single atom alloys (SAAs), atomically dispersing isolated metal atoms into a host metal surface, have emerged as promising catalysts for activating light alkanes. Here, we present a review of recent progresses on designing SAA catalysts for efficient and selective conversion of methane and other alkanes using computational modeling tools, including quantum mechanical simulations and machine learning models. First, we describe the various studies of applying firstprinciples simulations to discern reaction mechanisms, to understand the role of isolated dopant atoms in reducing C-H activation barriers, and to rationalize the synergy between dopant and host in improving selectivity towards desired products. Next, we present how the rapid development of machine learning models enables high-throughput screening of a large chemical space of SAA catalysts for methane transformation. We then extend the discussion from methane to the chemistry of activating other light alkanes, demonstrating the transferability of the synergy between dopant and host to activate multi-carbon hydrocarbons. Finally, we provide some perspectives on the future outlook for computational SAA catalyst design for methane activation. Methane ; Catalysis ; Dopant ; Atom (system on chip) ; Coke ; Oxidative coupling of methane ; Metal ; Heterogeneous catalysis Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion