jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dario Baum | Arno Förster | Lucas Visscher
Source: Scientific Data (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

qsGW quasiparticle and GW-BSE excitation energies of 133,885 molecules Machine learning applications in the chemical sciences, especially when based on neural networks, critically depend on the availability of large quantities of high-quality data. As they provide excellent accuracy for both charged and neutral excitations, a large dataset containing quasiparticle self-consistent GW (qsGW) and Bethe-Salpeter equation (BSE) data would be highly desirable to model excited state energies and properties. In this work, we introduce a dataset for qsGW-BSE excitation energies and qsGW quasiparticle energies of unprecedented size. Our dataset, denoted QM9GWBSE, supplies GW-BSE singlet-singlet and singlet-triplet excitation energies, corresponding transition dipole moments and oscillator strengths as well as qsGW quasiparticle energies for all molecules from the popular QM9 dataset. We anticipate that QM9GWBSE will provide a solid foundation to train highly accurate machine learning models for the prediction of molecular excited state properties. Quasiparticle ; Excited state ; Excitation ; Dipole ; Molecule ; Foundation (evidence) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Peijia Liu | Jiapei Zhang | Dengkui Wang | Xuan Fang | Dan Fang | Yong Wang | Fujun Liu | Songjiang Li | Hao Yan | Taiyan Ge | Jinhua Li | Xiaohua Wang
Source: Chinese Physics B (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Bandgap Prediction in Low-Dimensional Semiconductors via Transfer Learning: Augmenting Small Datasets with Pretrained CGCNN Models Abstract The bandgap regulation of low-dimensional semiconductor materials is the core challenge of optoelectronic device design, and the computational cost and experimental cycle of traditional methods seriously restrict the R&amp;#38;D efficiency. Aiming at the problems of overfitting and cross-dimensional generalization in existing deep learning models under small sample conditions, this study proposes a collaborative solution that integrates model optimization and transfer learning. By systematically introducing the regularization strategy into the crystal graph convolutional neural network (CGCNN), the bandgap prediction error of the test set is successfully reduced by 25.7%, while maintaining the high-precision feature extraction ability of the training set (R 2 =0.998), which significantly improves the stability of the model. Based on the optimized network architecture, the transfer learning framework of "bulk material pre-training - low-dimensional material fine-tuning" is further constructed, and the knowledge transfer is completed by using the 20377 individual phase data of the Materials Project, and finally the average absolute error of bandgap prediction is 0.15 eV on the two-dimensional semiconductor test set, which is 35% higher than the accuracy of the traditional training method, and the prediction error of the formed energy and total energy is reduced by 22%-30% at the same time. This study not only confirms the adaptability of deep learning in cross-dimensional material computing but also provides a solution with both computational efficiency and generalization capabilities for accurate prediction in small-sample scenarios. Overfitting ; Band gap ; Transfer of learning ; Test set ; Semiconductor ; Generalization ; Artificial neural network ; Stability (learning theory) ; Deep learning Machine Learning in Materials Science ; Ga2O3 and related materials ; Advanced Sensor and Energy Harvesting Materials
Authors: Huan Ma | Jie Liu | Zhenyu Li | Jinlong Yang
Source: Precision Chemistry (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Theoretical Perspectives of Precision Chemistry Precise prediction and control of chemical reactions and material properties─the central goal of precision chemistry─truly rely on accurate and efficient theoretical modeling of chemical systems. In recent years, significant progress has been made in theoretical and computational chemistry. Especially, interdisciplinary studies in the direction of the two emerging computational frontiers, artificial intelligence and quantum computation, are revolutionizing this field. Expressive neural networks can be used to represent many-electron wave functions with high accuracy, capturing strong correlations with a favorable computational cost compared to other methods. Leveraging quantum computation in quantum chemistry is also expected to deliver a quantum advantage in problems including electronic structure calculations. With artificial intelligence and quantum computation, we expect to establish a unified, high-precision, and scalable computational foundation for generating accurate quantum chemical data essential for precision chemistry. Quantum ; Computation ; Quantum computer ; Scalability ; Quantum chemical ; Computational model Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum Computing Algorithms and Architecture
Authors: Sohrab Rohani | Mohammad Amin Ghanavati | Seyed Mohamad Moosavi
Source: Research Square (preprint, 2026-03-30)
Field: Chemistry — Physical and Theoretical Chemistry

A Multimodal Semi-Supervised Learning Framework for Pharmaceutical Cocrystals Prediction Cocrystal ; Leverage (statistics) ; Generalization ; Key (lock) ; Graph ; Cognitive reframing Crystallography and molecular interactions ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Raj Maddipati | Dhruthi Boddapati | Arunan, Elangannan | Phani Motamarri | Konduri Aditya
Source: arXiv (Cornell University) (preprint, 2026-01-13)
Field: Materials Science — Materials Chemistry

ChemXDyn: Dynamics-informed species and reaction detection methodology from atomistic simulations Accurate identification of chemical species and reaction pathways from molecular dynamics (MD) trajectories is a prerequisite for deriving predictive chemical-kinetic models and for mechanistic discovery in reactive systems. However, state-of-the-art trajectory analysis methods infer bonding from instantaneous distance thresholds, which can misclassify transient, nonreactive encounters as bonds and thereby introduce spurious intermediates, distorted reaction networks, and biased rate estimates. Here, we introduce ChemXDyn, a dynamics-aware computational methodology that leverages time-resolved interatomic distance signatures as a core principle to robustly identify chemically consistent bonded interactions and, consequently, extract meaningful reaction pathways. In particular, ChemXDyn propagates molecular connectivity through time while enforcing atomic valence and coordination constraints to distinguish genuine bond-breaking and bond-forming events from transient, nonreactive encounters. We evaluate ChemXDyn on ReaxFF MD simulations of hydrogen and ammonia oxidation and on neural-network potential MD simulations of methane oxidation, and benchmark its performance against widely used trajectory analysis methods. Across these cases, ChemXDyn suppresses unphysical species prevalent in static analyses, recovers experimentally consistent reaction pathways, and improves the fidelity of rate constant estimation. In ammonia oxidation, ChemXDyn removes unphysical intermediates and resolves key NOx- and N2O-forming and -consuming routes. In methane oxidation, it reconstructs the canonical progression from CH4 to CO2. By linking atomistic dynamics to chemically consistent reaction identification, ChemXDyn provides a transferable foundation for MD-derived reaction networks and kinetics, with potential utility spanning combustion, catalysis, plasma chemistry, and electrochemical environments. Molecular dynamics ; Spurious relationship ; ReaxFF ; Chemical reaction ; Reaction dynamics ; Reaction rate ; Trajectory ; Methane ; Reaction rate constant Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Luohaoran Wang | Jacob Harris | Steven Mamolo | Sangharsha Gharat | Alan Taub | Ali Zolali | Mihaela Banu
Source: Preprints.org (preprint, 2026-02-02)
Field: Materials Science — Polymers and Plastics

A Theory-Guided Machine Learning and Molecular Dynamics Approach for Characterizing Fast-Curing Polyurethane Systems Fast-curing polyurethane (PU) systems are attractive for high throughput manufacturing, but quantifying cure kinetics, gelation, and cure-dependent glass transition temperature (Tg) is difficult, especially at low degree of cure (DoC). Here, a fast-reacting BASF PU formulation was studied using non-isothermal differential scanning calorimetry (DSC) at multiple heating rates, rheometry at 50 °C, and molecular dynamics (MD) simulations to extend Tg(α) in the low-DoC regime. DSC provided reaction enthalpy and conversion histories, and Kamal–Sourour (KS) parameters were identified by robust nonlinear fitting, reproducing conversion and curing-rate profiles (R² &amp;gt; 0.99 and &amp;gt; 0.95). Rheology indicated gelation at ~550–600 s (DoC ≈ 0.53), and DSC-based Tg at uncured, gelation, and fully cured states established the experimental Tg trend. MD (LAMMPS) with topological crosslinking and NPT thermal scans extracted Tg from density–temperature slopes at selected DoC points. Experimental and MD Tg data were fused with Gaussian process regression constrained by the DiBenedetto relationship (5-fold cross-validation), giving λ ≈ 0.28 and confidence intervals. This framework links kinetics, gelation, and Tg evolution for fast-curing PU and identifies the low-DoC region as the main source of uncertainty. Differential scanning calorimetry ; Rheometry ; Rheology ; Polyurethane ; Glass transition ; Molecular dynamics ; Enthalpy ; Process (computing) ; Thermal Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Photopolymerization techniques and applications
Authors: Zhiling Zheng
Source: Journal of Chemical Education (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

Developing an AI Course for Synthetic Chemistry Students Artificial intelligence (AI) and data science are transforming chemical research, yet few formal courses are tailored to synthetic and experimental chemists, who often face steep entry barriers due to limited coding experience and lack of chemistry-specific examples. We present the design and implementation of AI4CHEM, an introductory data-driven chemistry course created for students on the synthetic chemistry track with no prior programming background. The curriculum emphasizes chemical context over abstract algorithms, using an accessible web-based platform to ensure zero-install machine learning (ML) workflow development practice and in-class active learning. Assessment combines code-guided homework, literature-based mini-reviews, and collaborative projects in which students build AI-assisted workflows for real experimental problems. Learning gains include increased confidence with Python, molecular property prediction, reaction optimization, and data mining, and improved skills in evaluating AI tools in experimental practices. All course materials are openly available, offering a discipline-specific, beginner-accessible framework for integrating AI into synthetic chemistry training. Course (navigation) ; Chemistry education ; General chemistry ; Synthetic biology ; Curriculum Machine Learning in Materials Science ; Various Chemistry Research Topics ; Genetics, Bioinformatics, and Biomedical Research
Authors: Hao Wang | Ji Cheng | Zhangyu Wu | Xianfeng Chen | Siqi Liu | Deyu Niu | Jie Zhang | Kai Jin | Chaobin He
Source: Nature Communications (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Machine learning guided resolution of mechanical trade-off in polymer composites via stress adaptive interface Developing polymer composites that simultaneously achieve high strength, toughness, and impact resistance remains a fundamental challenge due to inherent trade-offs and brittle interfacial failure. Here, we propose a universal toughening strategy that integrates a bone-inspired trabecular interlock architecture with a thermodynamically driven, stress-adaptive interface to enable efficient energy dissipation under mechanical loading. To address multi-objective optimization in composites design, we further develop a data-driven framework combining Pareto Set Learning and Active Learning, which systematically explores the composition-performance landscape to identify balanced, high-performance formulations. The optimized composites exhibit synergistic mechanical properties: strength up to 250 MPa, fracture toughness exceeding 14 MPa·m<sup>1/2</sup>, and impact resistance of nearly 4.8 J, surpassing most bioinspired and engineered polymer counterparts. The strategy is scalable, chemically versatile, and broadly applicable, offering a programmable route to next-generation lightweight composites for aerospace, transportation, and protection. Brittleness ; Interlock ; Toughness ; Dissipation ; Interface (matter) ; Fracture toughness ; Polymer ; Toughening Machine Learning in Materials Science ; Polymer crystallization and properties ; Calcium Carbonate Crystallization and Inhibition
Authors: Dounia Shaaban Kabakibo | Félix Therrien | Yoshua Bengio | Michel Côté | Hongyu Guo | Homin Shin | Alex Hernandez-Garcia
Source: arXiv (Cornell University) (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

A Comparative Study of Molecular Dynamics Approaches for Simulating Ionic Conductivity in Solid Lithium Electrolytes Accurate prediction of ionic conductivity is critical for the design of high-performance solid-state electrolytes in next-generation batteries. We benchmark molecular dynamics (MD) approaches for computing ionic conductivity in 21 lithium solid electrolytes for which experimental ionic conductivity has been previously reported in the literature. In particular, we compare simulations driven by density functional theory (DFT) and by universal machine-learning interatomic potentials (uMLIPs), namely a MACE foundation model. We find comparable performance between DFT and MACE, despite MACE on one GPU more than 350 times faster than DFT on a 64-CPU node. The framework developed here is designed to enable systematic comparisons with additional uMLIPs and fine-tuned models in future work. Ionic conductivity ; Molecular dynamics ; Conductivity ; Electrolyte ; Density functional theory ; Ionic bonding ; Fast ion conductor Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Jianbin Hu | Ao Ma | Junhua Xiao | Yunkun Wang | Yunqian Wang
Source: ACS ES&T Water (article, 2026-01-27)
Field: Chemistry — Inorganic Chemistry

Multimodal Machine Learning for Intelligent MOF Design: Adsorption Mechanism Analysis and Visual Prediction Metal–organic frameworks (MOFs) show great potential for pollutant adsorption due to tunable pore structures and modifiable surface chemistry, but predicting their adsorption performance and screening optimal MOFs for specific scenarios remain challenging. Traditional experimental and empirical methods are inefficient, driving machine learning (ML) to accelerate MOF research; however, most existing ML models adopt single-modal approaches that fail to integrate heterogeneous data (e.g., MOF topological features, pollutant simplified molecular input line entry system SMILES data, and environmental parameters), limiting prediction accuracy. Herein, we propose a multimodal fusion framework, graph self-attention convolutional neural networks–fully connected network (GACNN-NN), which uses GACNN to decode MOF crystal spatial electronic distributions and a fully connected network to process pollutant physicochemical properties and environmental variables. We also developed a web-based prediction platform for real-time adsorption capacity estimation via inputting MOF synthesis and pollutant molecular parameters. Interpretability analysis further provides intuitive visualizations of key MOF adsorption sites, advancing MOF screening for environmental applications. Interpretability ; Limiting ; Adsorption ; Graph ; Convolutional neural network ; Artificial neural network ; Process (computing) Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Adsorption and biosorption for pollutant removal
Authors: Xiaowei Xie | John L. Weber | Mats Svensson | Ryne C. Johnston | Edward Harder | Leif D. Jacobson
Source: Journal of Chemical Theory and Computation (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Accurate Hydration Free Energy Calculations for Diverse Organic Molecules With a Machine Learning Force Field Free energy perturbation (FEP) calculations using classical force fields remain the dominant approach for large-scale, computational drug discovery efforts, but the accuracy is fundamentally limited by simplified forms that cannot quantitatively reproduce <i>ab initio</i> methods without significant fine-tuning. Machine Learning force fields (MLFFs) offer a promising avenue to retain quantum mechanical accuracy with significantly reduced computational cost compared with <i>ab initio</i> molecular dynamics (AIMD) simulations. Thus far, direct applications of ML force fields to FEP calculations lack systematic protocols and extensive benchmarking. In this work, we take a step in this direction by presenting a general and robust workflow for solvation (hydration) free energy (HFE) calculations which is independent of the details of the particular MLFF architecture used. Combining a broadly trained ML force field, Organic_MPNICE, with sufficient statistical and conformational sampling empowered by the solute-tempering technique, affords sub-kcal/mol average errors in HFE predictions relative to experimental estimates. This approach outperforms state-of-the-art classical force fields and DFT-based implicit solvation models on a diverse set of 59 organic molecules and provides a route to <i>ab initio</i>-quality HFE predictions, advancing the use of ML force fields in thermodynamic property prediction. Force field (fiction) ; Solvation ; Workflow ; Molecular dynamics ; Free energy perturbation ; Quantum ; Field (mathematics) ; Energy (signal processing) ; Organic molecules Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Rasak Adisa
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction and Classification of Multi-Component Alloys Using Interpretable Machine Learning Pattern recognition (psychology) ; Support vector machine ; Feature (linguistics) ; Training set ; Artificial neural network Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Metallurgy and Material Forming
Authors: Jun Li | Chenyi Zhang | Mingzhe Li | Shiwen Luo | Xuao Lu | Yakun Zhu | Zhengyang Gao | Weijie Yang
Source: Journal of Alloys and Compounds (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Attention to Training Set Partitioning in Machine Learning for Solid-State Hydrogen Storage Alloys Training (meteorology) ; Set (abstract data type) ; Training set ; Hydrogen storage ; Hydrogen Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Big Data and Digital Economy
Authors: Janna Jeschke | Hatice Mutlu
Source: Nature Communications (article, 2026-02-05)
Field: Materials Science — Polymers and Plastics

From waste rubber to value polybutadiene modification for circular materials Synthetic rubber waste poses a growing environmental challenge due to its cross-linked, non-recyclable nature. This Perspective examines emerging post-polymerization modification strategies for linear polybutadiene and vulcanized rubber, spanning catalytic, metal-free, and catalyst-free approaches, and evaluates their alignment with Green and Circular Chemistry principles. By coupling sustainability metrics with assessments of technological readiness, we highlight current limitations and opportunities for the real-world implementation of these approaches. Looking ahead, we outline how integrating mechanochemistry, machine learning, and life-cycle assessment can enable scalable, low-impact transformations that recast vulcanized polybutadiene from waste to resource, paving the way toward a circular elastomer framework. Vulcanization ; Polybutadiene ; Natural rubber ; Elastomer ; Circular economy ; Sustainability ; Tread Polymer Nanocomposites and Properties ; Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization
Authors: L. M. Wang | Jacob Harris | Steven Mamolo | Sangharsha Gharat | Ali M. Zolali | Alan Taub | MIHAELA BANU
Source: Polymers (article, 2026-03-11)
Field: Materials Science — Polymers and Plastics

A Theory-Guided Machine Learning and Molecular Dynamics Approach for Characterizing Fast-Curing Polyurethane Systems Fast-curing polyurethane (PU) systems are attractive for high-throughput manufacturing, but quantifying cure kinetics, gelation, and cure-dependent glass transition temperature (Tg) is difficult, especially at a low degree of cure (<i>DoC</i>). Here, a fast-reacting BASF PU formulation was studied using non-isothermal differential scanning calorimetry (DSC) at multiple heating rates, rheometry at 50 °C, and molecular dynamics (MD) simulations to extend Tgα in the low-<i>DoC</i> regime. DSC provided reaction enthalpy and conversion histories, and Kamal-Sourour (KS) parameters were identified by robust nonlinear fitting, reproducing conversion and curing rate profiles (<i>R</i><sup>2</sup> > 0.99 and >0.95). Rheology indicated gelation between 475 and 625 s (<i>DoC</i> ≈ 0.53), and DSC-based Tg at uncured, gelation, and fully cured states, established the experimental Tg trend. MD (LAMMPS) with topological crosslinking and NPT thermal scans extracted Tg from density-temperature slopes at selected <i>DoC</i> points. Experimental and MD Tg data were fused with Gaussian process regression constrained by the DiBenedetto relationship (5-fold cross-validation), giving <i>λ</i> ≈ 0.29 and confidence intervals. This framework links kinetics, gelation, and Tg evolution for fast-curing PU and identifies the low-<i>DoC</i> region as the main source of uncertainty. Differential scanning calorimetry ; Rheometry ; Glass transition ; Polyurethane ; Rheology ; Curing (chemistry) ; Molecular dynamics ; Enthalpy Polymer composites and self-healing ; Epoxy Resin Curing Processes ; Photopolymerization techniques and applications
Authors: Vaibhav Paigwar | Rajesh K. Khatirkar | Khushboo Khurana | Alok Bhadauria
Source: Canadian Metallurgical Quarterly (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Predicting and optimising aluminium alloy compositions for target mechanical properties using machine learning Aluminium alloy ; Aluminium ; Alloy Machine Learning in Materials Science ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties