jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yong Ran | Sofı́a Calero | Thijs J. H. Vlugt | David Dubbeldam
Source: Molecular Physics (article, 2025-10-07)
Field: Chemistry — Inorganic Chemistry

Matching pure and mixture isotherms using an invertible autoencoder Porous materials such as zeolites and Metal-Organic Frameworks are widely used for molecular separations based on adsorption and enthalpy/entropy characteristics. Ideal adsorption solution theory (IAST) predicts mixture adsorption behaviour on the basis of pure component isotherms of adsorbents in porous media. Mixture data at all mole fractions are required for breakthrough simulations. The use of IAST avoids the expensive computations of mixtures with Monte Carlo methods. Matching outcomes from computational physics studies to experimentally measurable properties is the foundation of the materials design pipeline. Here, we report the regression of an Invertible Autoencoder (IAE) for the forward and backward mapping of pure and mixture isotherms. The invertible autoencoder is defined as a soft-invertible neural network, which can be used as mapping function. Pure component isotherms are modelled using a 3-site Langmuir-Freundlich model, with a broad range of equilibrium pressure and heterogeneity factors. A synthetic dataset is generated from pure component isotherms and mixture isotherms calculated with RUPTURA. The IAE predicts pure and mixture isotherms with high precision over a large fugacity range, for up to 6 components and 3-site isotherms. This work contributes to inverting the full design pipeline from physical gas separation to adsorbate design, enabling property-guided materials discovery. Autoencoder ; Adsorption ; Invertible matrix ; Component (thermodynamics) ; Matching (statistics) ; Computation ; Fugacity ; Work (physics) ; Monte Carlo method Zeolite Catalysis and Synthesis ; Phase Equilibria and Thermodynamics ; Theoretical and Computational Physics
Authors: Patricia R. S. Wenceslau (21855841) | Antônio
S. N. Aguiar (20400920) | Vitor S. Duarte (4646770) | Leonardo R. de Almeida (11349982) | Chris H. J. Franco (6848042) | Gilberto L. B. de Aquino (3947279) | Jaqueline E. de Queiroz (21855844) | Adriano O. Maldaner (1990561) | Hamilton B. Napolitano (3161772)
Source: Figshare (article, 2025-07-31)
Field: Chemistry — Organic Chemistry

Comprehensive Analysisof Pyrazoline Analogs: ExploringTheir Antioxidant Potential as Biofuel Additives Global dependence on fossil fuels raises environmental and energy concerns, encouraging the search for renewable sources, such as biodiesel. However, their limited oxidative stability compromises their commercial viability. This study investigates two pyrazoline analogues (CNF and CNO) as potential antioxidant additives for biodiesel using a combined molecular modeling and machine learning approach. Supramolecular analysis revealed that C–H···O and C–H···π interactions influence molecular stability. Chemical reactivity descriptors indicated that the methoxy group (−OCH<sub>3</sub>) in CNO enhances the electron-withdrawing effect, making it more susceptible to oxidation. Kinetic evaluation predicted that both compounds have intermediate oxidation rates when exposed to hydroxyl radicals, with a performance comparable to widely used commercial antioxidants. These results suggest that pyrazoline analogues are promising candidates to mitigate the oxidative degradation of biodiesel, contributing to the greater efficiency and sustainability of biofuels. Pyrazoline ; Antioxidant ; Biofuel ; Renewable energy ; Reactivity (psychology) ; Biodiesel ; Oxidative phosphorylation ; Fossil fuel Free Radicals and Antioxidants ; Organic Chemistry Cycloaddition Reactions ; Biodiesel Production and Applications
Authors: Lukas M. Sigmund | Tina Seifert | Riya Halder | Giulia Bergonzini | Magnus J. Johansson | Per‐Ola Norrby | Kjell Jorner | Mikhail Kabeshov
Source: ChemRxiv (preprint, 2025-05-16)
Field: Chemistry — Organic Chemistry

Predicting Reaction Feasibility and Selectivity of Aromatic C–H Thianthrenation with a QM-ML Hybrid Approach The direct thianthrenation of aromatic C–H bonds is a valuable late-stage functionalization strategy that can assist, for example, the development of new drugs. We herein present a predictive computational model for this reaction, denoted PATTCH, which is based on semi-empirical quantum mechanics and machine learning. It classifies each aromatic C–H unit either as reactive or not with an accuracy of above 90%. It can address both the site-selectivity and reaction feasibility question associated with the thianthrenation protocol. First, this was achieved by selecting carefully engineered features, which take into account the electronic and steric influence on the site-selectivity. Second, parallel experimentation was used to supplement the available literature data with 54 new negative reactions (unsuccessful thianthrenation), which we show was instrumental for developing the PATTCH tool. Ultimately, we successfully applied the model to a challenging test set encompassing the differentiation between carbocycle vs. heterocycle functionalization, the identification of substrates that were reported to result in a mixture of isomeric products, and to molecules that could not be thianthrenated. The computational predictions were experimentally validated. The PATTCH tool can be obtained free of charge from https://github.com/MolecularAI/thianthrenation_prediction. Selectivity ; QM/MM ; Chemistry ; Computational chemistry ; Organic chemistry ; Catalysis ; Molecular dynamics Sulfur-Based Synthesis Techniques ; Chemical Synthesis and Reactions ; Synthesis of heterocyclic compounds
Authors: Tina N. Mihm | Kasidet Jing Trerayapiwat | Pierre Darancet | Sahar Sharifzadeh
Source: Physical Review Materials (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Ferroelectric phase transition in group-IV monochalcogenides from an equivariant machine learned force field Group-IV monochalcogenides are a class of layered ferroelectric semiconductors that have demonstrated spontaneous intrinsic polarization above room temperature. Here, we use the multiatomic cluster expansion machine learning architecture to train and test a force field capable of modeling the structural properties and second-order ferroelectric-to-paraelectric phase transition in a group-IV monochalcogenide, GeSe. The model captures the double-well potential energy surface associated with the onset of macroscopic polarization in bulk GeSe within $12.5\phantom{\rule{0.28em}{0ex}}\mathrm{meV}/\mathrm{atom}$, as well as near-equilibrium properties like the phonon dispersion. The development of this quantitatively accurate force field enables long-time molecular dynamics simulations, which predict the critical temperature of the ferroelectric-to-paraelectric phase transition in bulk GeSe to be ${T}_{c}=600\phantom{\rule{0.28em}{0ex}}\mathrm{K}$. This study demonstrates the capabilities of equivariant force fields to accurately describe phenomena associated with structural symmetry breaking. Ferroelectricity ; Phase transition ; Polarization (electrochemistry) ; Phonon ; Phase (matter) ; Force field (fiction) ; Point reflection ; Field (mathematics) Solid-state spectroscopy and crystallography ; Crystal Structures and Properties ; Phase-change materials and chalcogenides
Authors: Samir A. Salamah | Candeniz SECKIN | Süha Tuna
Source: International Journal of Refrigeration (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Performance optimization in thermoelectric coolers using physical data-driven artificial neural networks Artificial neural network ; Thermoelectric cooling ; TEC ; Robustness (evolution) ; Thermoelectric effect ; Mean squared error ; Seebeck coefficient ; Control theory (sociology) ; Coefficient of performance ; Thermoelectric generator Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Adsorption and Cooling Systems
Authors: Xinwei Dong | Tao Zhang | Fuxin Zheng | Qun Li | Zhao Xu | Yansheng Liu | Guofu Wang
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Multi-task learning on microscopic hyperspectral data enables accurate classification of graphene oxide films Hyperspectral imaging ; Graphene ; Characterization (materials science) ; Raman spectroscopy ; Artificial neural network ; Oxide ; Quality (philosophy) ; Deep learning Graphene research and applications ; Graphene and Nanomaterials Applications ; Stochastic Gradient Optimization Techniques
Authors: Xianglong Wang | Du Xiangpeng | Haifeng Gu | Haiyang Zhang | Rui Yuan | Leng Jiaxi | Zhiming Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

UO2-Mo-Nb composite nuclear fuel thermal conductivity and thermal stress coupling analysis through the machine learning and NSGA-II algorithm Thermal conductivity ; Ternary operation ; Composite number ; Stress (linguistics) ; Thermal ; Thermal conductivity measurement ; Finite element method ; Pellets Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Yifei Gao | Pengfu Gao | Ji Yao Guo | Yi Xie | Jinqiao Dong | Wei Gong | Yong Cui
Source: Science China Chemistry (article, 2025-10-17)
Field: Chemistry — Inorganic Chemistry

Data-driven machine learning assisted prediction of metal node types in metal-organic frameworks for guiding linker design and targeting inverse C3H8/C3H6 separation Metal-organic framework ; Boosting (machine learning) ; Node (physics) ; Propane ; Linker ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Covalent Organic Framework Applications
Authors: Rashedul
Alam Chowdhury (21260797) | Biswajit Ghose (21260800) | Deep Choudhuri (4796805)
Source: Figshare (article, 2025-05-07)
Field: Chemistry — Inorganic Chemistry

Uncoveringthe Role of Critical Bonds in the ThermomechanicalResponse of Zn<sub>2</sub>(1,4-benzenedicarboxylate)<sub>2</sub>(1,4-diazabicyclo[2,2,2]octane)Using <i>Ab Initio</i> Simulations and Physics-ConstrainedNeural Networks Metal–organic frameworks (MOFs) are susceptible to harsh conditions involving high temperatures, mechanical loading, host/guest chemical interactions, or a combination thereof due to the disruption of underlying bonds. Here, we probed the thermomechanical response of Zn<sub>2</sub>(BDC)<sub>2</sub>(DABCO) (BDC = 1,4-benzenedicarboxylate and DABCO = 1,4-diazabicyclo[2,2,2]octane) MOF or Zn-DMOF, whose metal nodes are connected to two BDC and DABCO linkers via Zn–O and N–Zn bonds, respectively. We have examined the relative contributions of such bonds toward Zn-MOF’s thermomechanical response at 200 and 300 K, by training a physics-constrained neural network with <i>ab initio</i> molecular dynamics (AIMD)-derived interatomic vibration data, and quantitatively estimated interatomic bond strengths within a local environment in the MOF body. We quantitatively show that N–Zn and Zn–O bonds near the Zn-based metal nodes are weaker than their surroundings. These findings were followed up by separately annealing Zn-DMOF at 920 K-AIMD (above its thermal decomposition temperature), mechanically straining it to failure using 0 K-DFT, and examining host/guest interactions in aqueous and acidic environments with 300 K-AIMD. Together, they indicated that structural instability in Zn-DMOF was initiated by the disruption of N–Zn and Zn–O bonds under harsh conditions and that Zn–O bonds are weaker than N–Zn bonds at <i>T</i> ≥ 300 K. Broadly, we show that bond strength estimates are a reasonable indicator of Zn<sub>2</sub>(BDC)<sub>2</sub>(DABCO)’s performance at high temperatures and mechanical loading, and demonstrate the viability of employing AIMD simulations and physics-constrained neural networks to quantify interatomic bond strengths of hybrid organic–inorganic materials. Molecular dynamics ; Bond strength ; Chemical bond ; DABCO ; Bond ; Bond order ; Annealing (glass) ; Artificial neural network Metal-Organic Frameworks: Synthesis and Applications ; Crystallography and molecular interactions ; Magnetism in coordination complexes
Authors: Chunxiang Zhao | Jiaqi Wang | Qiang Sun | Qilong Gao
Source: Nano Research (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Negative thermal expansion of two-dimensional materials: A review Negative thermal expansion (NTE) in two-dimensional (2D) materials defies the traditional views of thermal expansion and holds promise for the development of advanced nanoelectronics and temperature-controlled devices. Recent advances in 2D NTE materials, particularly through studies of out-of-plane bending vibrations, structural unit rotations, spin-lattice interactions, and strain-modulated lattice responses, have enhanced our mechanistic understanding of NTE in ultrathin systems. This review summarizes recent progress in uncovering the characteristics and origins of NTE in key 2D materials such as graphene, hexagonal BN, transition metal dichalcogenides, and novel 2D frameworks. Although porous structures often exhibit NTE dominated by low-energy optical phonons, this work highlights the growing recognition of acoustic phonon contributions and direction-dependent elastic effects in atomically thin systems, further discussing NTE tuning strategies such as substrate-induced strain control, interlayer coupling in van der Waals heterostructures, and magnetic configuration engineering. Current challenges, including narrow operational temperature windows, environmental sensitivity, and incompatibility with conventional materials, are critically evaluated, and innovative design approaches combining machine learning predictions, defect modulation, and topology optimization are outlined to advance the development of 2D NTE materials with customized thermomechanical performance. Negative thermal expansion ; van der Waals force ; Thermal expansion ; Phonon ; Nanoelectronics ; Thermal ; Bending ; Coupling (piping) Thermal Expansion and Ionic Conductivity ; Zeolite Catalysis and Synthesis ; Magnesium Oxide Properties and Applications
Authors: Florent Purnode | Louis Denis | François Henrotte | Gilles Louppe | Christophe Geuzaine
Source: ORBi (University of Liège) (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Neural-Network-based Homogenized Model for Ferromagnetic Laminated Cores Accounting for hysteresis and eddy currents in finite element simulations of electrical machines is usually computationally expensive. These phenomena have however a significant impact on the machine performance. To account for iron losses with a low computational cost, we have trained a Recurrent Neural Network to serve as material law in 2D finite element simulations. Finite element method ; Eddy current ; Ferromagnetism ; Hysteresis ; Artificial neural network ; Magnetic hysteresis ; Homogenization (climate) Magnetic Properties and Applications ; Magnetic properties of thin films ; Model Reduction and Neural Networks
Authors: Xingchen Shen | Jiongzhi Zheng | Michael Marek Koza | Petr Levinský | J. Hejtmánek | Philippe Boullay | B. Raveau | Jinghui Wang | Jun Li | Pierric Lemoine | Christophe Candolfi | Emmanuel Guilmeau
Source: ArXiv.org (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Crystalline Materials with Record Ultralow Lattice Thermal Conductivity via a Universal Descriptor Ultralow glass-like lattice thermal conductivity in crystalline materials is crucial for enhancing energy conversion efficiency in thermoelectrics and thermal insulators. We introduce a universal descriptor for thermal conductivity that relies only on the atomic number in the primitive cell and the sound velocity, enabling fast and scalable materials screening. Coupled with high-throughput workflows and universal machine learning potentials, we identify the candidate materials with ultralow thermal conductivity from over 25, 000 materials. We further validate this approach by experimentally confirming record-low thermal conductivity values of 0.15-0.16 W/m/K from 170 to 400 K in the halide metal CsAg2I3. Combining inelastic neutron scattering with first-principles calculations, we attribute the ultralow thermal conductivity to the intrinsically small sound velocity, strong anharmonicity, and structural complexity. Our work illustrates how a universal descriptor, combined with high-throughput screening, machine-learning potential and experiment, enables the efficient discovery of materials with ultralow thermal conductivity. Thermal conductivity ; Thermoelectric materials ; Phonon ; Inelastic neutron scattering ; Thermal ; Work (physics) ; Lattice (music) Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: David Gryc | Pritam Banerjee | Felipe Jacques Lima Duarte | Joerg R. Jinschek | Premkumar Murugesan | Babak Rezaei | Stephan Sylvest Keller | Roland A. Fischer | Mian Zahid Hussain
Source: Advanced Intelligent Discovery (article, 2025-09-19)
Field: Chemistry — Inorganic Chemistry

Bayesian Exploration of Metal‐Organic Framework‐Derived Nanocomposites for High‐Performance Supercapacitors Metal‐organic framework (MOF)‐derived materials are promising candidates for energy storage applications, particularly as supercapacitors, owing to their high porosity, tunable compositions, and pseudocapacitive behavior. However, performance optimization traditionally relies on resource‐intensive trial‐and‐error methods with limited insight into complex structural transitions. To address this challenge, a machine learning approach integrating Bayesian optimization (BO) to refine synthesis parameters systematically is presented. The importance of each parameter is assessed using correlation matrices, surrogate model structure, and Shapley values analysis in two models: random forest regressor, which achieves low prediction error, and extra trees regressor, which provides better generalization. This strategy efficiently explores the design space and significantly reduces experimental workload. Focusing on Mn‐MIL‐100‐derived MnO/C composites for supercapacitor applications, this approach identifies optimal conditions to increase energy storage performance while quantifying the influence of key process parameters. These findings demonstrate the potential of AI‐driven strategies to accelerate material discovery, enhance process efficiency, and advance the practical applications of MOF‐derived materials. Bayesian optimization ; Supercapacitor ; Process (computing) ; Bayesian probability ; Key (lock) ; Random forest ; Energy storage ; Bayesian inference Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Supercapacitor Materials and Fabrication
Authors: Linyang Wang | Ming Sun | Shuo Liu | Lipeng Shao | Qiuxiang Yao
Source: Chemical Engineering Science (article, 2025-12-29)
Field: Chemistry — Inorganic Chemistry

Catalytic conversion of C22 alkane to aromatics over carbon (coke and activated carbon)-coated mesoporous HZSM-5: Thermodynamic and machine learning algorithm study Ethylbenzene ; Dehydrogenation ; Carbonization ; Carbon fibers ; Naphthalene ; Mesoporous material ; Alkane ; Benzene ; Aromatization Zeolite Catalysis and Synthesis ; Mesoporous Materials and Catalysis ; Catalysis and Hydrodesulfurization Studies
Authors: Kai Yang | Yu Song | Yuhai Li | Morten M. Smedskjær | Mathieu Bauchy | Fabian Rosner
Source: Journal of Non-Crystalline Solids (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

Enabling extrapolation of Young’s modulus of CaO-Al2O3-SiO2 ternary glasses by topology-informed machine learning The application of machine learning (ML) in material discovery, particularly in the design of novel materials like glasses, has shown considerable promise. However, the efficacy of data-driven ML approaches is often hindered by the limited volume and representativeness of material datasets. While these approaches demonstrate notable success in interpolating data, they tend to perform inadequately in extrapolation tasks, which are crucial in the context of material discovery. In this study, we address this challenge by incorporating topological knowledge, derived from the atomic structures of glasses, to inform ML models with physics-based insights. To showcase this approach, we focus on predicting Young's modulus of CaO-Al 2 O 3 -SiO 2 glasses. By leveraging the topological information, i.e., the fractions of bond-stretching and bond-bending constraints, we transform a non-linear composition-property mapping to a higher-linearity topology-property mapping to improve the extrapolation abilities of ML models. Our results demonstrate that the topology-informed ML approach maintains comparable prediction accuracy within the training domain while significantly improving performance in extrapolating the Young’s modulus of glasses beyond the training domain. Therefore, our topology-informed approach can offer a more efficient and expedited pathway towards the discovery of new glass materials in unexplored domains. Extrapolation ; Ternary operation ; Modulus ; Topology (electrical circuits) ; Materials science ; Computer science ; Mathematics ; Composite material ; Mathematical analysis ; Combinatorics Phase-change materials and chalcogenides ; Glass properties and applications ; Theoretical and Computational Physics
Authors: Riya Gola | Mahipal Singh | Shirish Joshi
Source: International Journal of Computational Materials Science and Engineering (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

Machine learning models to predict the stress–strain behavior of silver nanowires Metallic nanowires are the most extensively used and most promising component for next-generation nanoscale devices due to their increasing efficacy in various fields and most importantly in electronic devices. Computational methods are preferred for studying nanostructures because they offer atomic-level insights in a cost-effective, controllable and scalable manner beyond the limits of experimental techniques. Molecular dynamics simulation is a powerful method to understand the mechanical properties of nanostructures, with the help of virtual experimentation. Machine learning provides a very powerful approach for the analysis of previously recorded data to predict the future. Machine learning is a growing approach to model systems and predictions. This work offers a computational framework for the analysis and augmentation of molecular dynamics simulation data for the Silver Nanowires. The parameters like coefficient of efficiency, root mean squared errors and mean squared errors are evaluated for the machine learning models like Fine Tree, Fine Gaussian SVM, Bagged Trees and Rational Quadratic GPR. The dependence of the predictor variables on the response variable has also been discussed with Shapley importance values. Scalability ; Computational learning theory ; Mean squared error ; Nanowire ; Gaussian ; Gaussian process ; Online machine learning ; Nanoscopic scale ; Experimental data Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Advanced Physical and Chemical Molecular Interactions