jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mateus B. P. Querne | M. Machado | Ronaldo C. Prati | Natan M. Regis | Matheus P. Lima | Juarez L. F. Da Silva
Source: The Journal of Physical Chemistry C (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Bridging Text Mining and Quantum Simulations for the Design of 2D Monochalcogenide Materials The formulation of a structured framework dedicated to the systematic review of the literature, identification of potential compounds, theoretical quantum chemistry characterization, and assessment of the significance of stability descriptors is essential for accelerating the discovery of two-dimensional materials for technological applications. In this work, we selected the two-dimensional monochalcogenides (MQ), which have attracted increased interest due to their potential applications in future devices, as prototype materials for the present investigation. Our framework started with a natural language processing analysis of more than 5400 articles, revealing a growing research interest in the two-dimensional MQ compounds, especially in electronics, energy, and fundamental studies. This led to the selection of 27 diverse MQ compounds across 13 distinct two-dimensional structural phases for density functional theory calculations, resulting in an extensive database of physicochemical properties. We evaluated formation enthalpies, uncovering clear stability trends (e.g., stability declines with heavier chalcogens, dynamic robustness of GeSe), and evaluated equilibrium lattice parameters, noting predictable expansions and significant anisotropies. Bader charge analysis offered insights into charge-transfer and ionicity trends. Our electronic structure analysis identified band gaps (direct or indirect, depending on the M element group and the weight of the chalcogen) and optical absorption anisotropies significantly influenced by crystal symmetry and spin orbit coupling. Importantly, band alignment calculations classified all possible heterojunctions as type I, II, or III, underscoring their extensive potential for future optoelectronic device development. Additionally, we incorporated machine learning, using a random forest approach, along with our density functional theory calculations to accurately forecast trends in energetic properties. This analysis identified the electronic charge as a highly significant stability descriptor among the 34 descriptors, underscoring its importance for future machine learning endeavors. Hence, this study offers a streamlined framework for the characterization and discovery of promising two-dimensional materials, highlighting the synergy between data-driven analysis and quantum-based simulations. Density functional theory ; Bridging (networking) ; Stability (learning theory) ; Heterojunction ; Lattice (music) ; Robustness (evolution) ; Electronic structure ; Phonon ; Band gap 2D Materials and Applications ; Machine Learning in Materials Science ; Expert finding and Q&A systems
Authors: Zhe Ding | Guo Li | Fang Bai | Chao Hua | Ping Lü | Jinyi Chen
Source: Chinese Journal of Chemical Engineering (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Toward the rational design for low-temperature hydrogenation of silicon tetrachloride: Mechanism and data-driven interpretable descriptor Silicon tetrachloride ; Mechanism (biology) ; Rational design ; Silicon ; Computational chemistry ; Combinatorial chemistry ; Chemistry ; Computer science ; Materials science ; Organic chemistry ; Nanotechnology ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ruoqian Fan | Yaohuang Huang | Lingchao Cai | Zhen Chen | Pavlo O. Dral | Weiwei Fang
Source: Organic Letters (article, 2025-12-03)
Field: Chemistry — Organic Chemistry

Breaking the Scalability Barrier: Diversifiable Pd-Catalyzed Domino Reaction En Route to Dibenzo[ <i>b,d</i> ]azepines Herein, we present an efficient synthesis of diverse dibenzo[<i>b,d</i>]azepines in high yields via Pd-catalyzed domino reactions of 2,2'-dihalobiphenyls with <i>N</i>-allyl alkylamines. The key to this success is in situ generation of a five-membered, chelated Pd(IV) species via the subsequent activation of two C-Br bonds at the initial step, supported by control experiments, density functional theory, and machine learning calculations. Domino ; Scalability ; Cascade reaction ; Key (lock) ; In situ ; Reaction conditions ; Control (management) ; Density functional theory Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Catalytic Alkyne Reactions
Authors: Gang Huang | Lawrence S. Chan | Hajime Yoshino | Ge ZHANG | Yuliang Jin
Source: ArXiv.org (preprint, 2025-06-07)
Field: Materials Science — Materials Chemistry

Liquid and solid layers in a thermal deep learning machine Based on deep neural networks (DNNs), deep learning has been successfully applied to many problems, but its mechanism is still not well understood -- especially the reason why over-parametrized DNNs can generalize. A recent statistical mechanics theory on supervised learning by a prototypical multi-layer perceptron (MLP) on some artificial learning scenarios predicts that adjustable parameters of over-parametrized MLPs become strongly constrained by the training data close to the input/output boundaries, while the parameters in the center remain largely free, giving rise to a solid-liquid-solid structure. Here we establish this picture, through numerical experiments on benchmark real-world data using a thermal deep learning machine that explores the phase space of the synaptic weights and neurons. The supervised training is implemented by a GPU-accelerated molecular dynamics algorithm, which operates at very low temperatures, and the trained machine exhibits good generalization ability in the test. Global and layer-specific dynamics, with complex non-equilibrium aging behavior, are characterized by time-dependent auto-correlation and replica-correlation functions. Our analyses reveal that the design space of the parameters in the liquid and solid layers are respectively structureless and hierarchical. Our main results are summarized by a data storage ratio -- network depth phase diagram with liquid and solid phases. The proposed thermal machine, which is a physical model with a well-defined Hamiltonian, that reduces to MLP in the zero-temperature limit, can serve as a starting point for physically interpretable deep learning. Deep learning ; Perceptron ; Artificial neural network ; Generalization ; Point (geometry) ; Deep belief network ; Thermal ; Benchmark (surveying) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Lei LI | Han CHEN | Jinsong BAI | Ruizhi ZHANG | Jian ZHANG | Dun WU
Source: SHILAP Revista de lepidopterología (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Improvement of the Design Program for Functionally Graded Materials with Controllable Loading and High-Throughput Optimization Design To achieve high-throughput optimization design of gradient materials, it is essential to establish accurate and rapid predictive capabilities for the loading performance of such materials. The rapid advancement of artificial intelligence technology combined with hardware development has gradually become a revolutionary research tool across various scientific fields. In materials science, machine learning methods play a significant role in high-throughput material design and performance prediction. This study introduces machine learning methods into the optimization design of functionally graded materials with controllable loading. By integrating computational results from physics-based models, a relatively accurate rapid prediction model was established, significantly enhancing optimization throughput. The multi-material fluid-elastoplastic computational program MLEP has undergone multiple rounds of validation in the experimental design and data interpretation of gradient materials, demonstrating high predictive accuracy for experimental results. Numerical experimental samples based on this program can be used to construct high-precision surrogate models. To extend MLEP’s applicability to a broader range of density-gradient material design and experimental prediction, the p-α model has been incorporated into the existing simulation framework. This model describes the mechanical behavior of low-density polymers under shock/quasi-isentropic loading, enabling the expansion of flyer plate density from approximately 0.5 g/cm3 to 15.0 g/cm3. Range (aeronautics) ; Construct (python library) ; Design of experiments ; Surrogate model ; Experimental data ; Performance prediction ; Material Design ; Model validation ; Engineering design process Nonlocal and gradient elasticity in micro/nano structures ; Elasticity and Material Modeling ; Composite Material Mechanics
Authors: Samantha Brozak | David Montes de Oca Zapiain | Brendan Donohoe | Tommy Ao | Nathan P. Brown | Marcus D. Knudson | Carianne Martinez | J. Matthew D. Lane
Source: Computational Materials Science (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Machine learning for domain transfer between simulated and experimental 2D X-ray diffraction patterns using generative adversarial networks Diffraction ; Leverage (statistics) ; Domain (mathematical analysis) ; Compression (physics) ; Generative grammar ; Experimental data ; Process (computing) ; Matching (statistics) Machine Learning in Materials Science ; High-pressure geophysics and materials ; Inorganic Chemistry and Materials
Authors: Liu Chen
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Harnessing AI-Driven Smart Computing for Next-Generation Sustainable Materials Sustainability ; Environmentally friendly ; Process (computing) ; Work (physics) ; Emerging technologies ; Efficient energy use ; Transformative learning ; Resource efficiency Machine Learning in Materials Science ; Material Selection and Properties ; MXene and MAX Phase Materials
Authors: Fredy John Porathur | Fabian Huber | Eduard Stadler | Dieter P. Gruber
Source:  (article, 2025-09-15)
Field: Materials Science — Materials Chemistry

Warpage Reduction of Laminate Substrates Through Metamodel-Based Optimization of Material Properties Managing substrate warpage is critical in the microelectronics industry, as excessive deformation can lead to strain accumulation in packages, when integrated with components such as ASICs, emitters, and detectors. While finite element analysis is traditionally used for material optimization, the complex structure of laminate substrates makes this approach computationally intensive. This study explores the use of machine learning-based metamodels to accelerate material optimization of laminate substrates. Artificial neural networks and Gaussian process regression models were developed to predict top-surface deformation during reflow processes. In a two-phase approach, Phase A uses the coefficient of thermal expansion as input, while Phase B incorporates coefficient of thermal expansion, elastic modulus, and Poisson's ratio. The best-performing metamodel in each phase is integrated with particle swarm optimization to identify optimal material properties. To our knowledge, this is the first study to jointly optimize coefficient of thermal expansion, elastic modulus, and Poisson's ratio using metamodels to achieve reduced substrate warpage. The proposed method not only reduces computational cost but also achieves higher accuracy than previously reported approaches, demonstrating its effectiveness in minimizing substrate warpage. Particle swarm optimization ; Finite element method ; Reduction (mathematics) ; Material properties ; Substrate (aquarium) ; Microelectronics ; Artificial neural network ; Metamodeling ; Thermal expansion Machine Learning in Materials Science ; Electronic Packaging and Soldering Technologies ; 3D IC and TSV technologies
Authors: Kinga Mlekodaj | Agnieszka Kornas | Julia Sobalska | Karolina A. Tarach | Lukáš Grajciar | Carlos Bornes | Dariusz Wardecki | Przemysław Rzepka | Thomas C. Hansen | Dalibor Kaucký | Olena Tynkevych | Kinga Góra‐Marek | Edyta Tábor
Source: Applied Catalysis B: Environmental (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Structural changes of iron active sites in zeolite FER during CO2 and CH4 conversion to oxygenates The direct co-conversion of $CO_{2}$ and $CH_{4}$ into oxygenates offers an atom-efficient strategy for the simultaneous valorization of two major greenhouse gases. This work presents an integrated spectroscopic, diffraction, and computational investigation of Fe-containing ferrierite (Fe-FER) as a bifunctional catalyst capable of selectively transforming $CO_{2}$ and $CH_{4}$ into methanol and acetic acid, with methanol further converted into olefins. In situ FTIR–MS studies reveal sequential formation of methanol and acetic acid, indicating that under mild conditions, $CH_{4}$ is first oxidized to methanol on redox-active Fe centers, followed by methanol carbonylation on Brønsted acid sites. At elevated temperatures, methanol-to-olefin conversion becomes competitive. Mössbauer spectroscopy shows that the interaction of Fe(II) with $CO_{2}$ decreases the electron density around Fe nuclei, resulting in partial oxidation and coordination changes between tetrahedral and octahedral environments. Neutron powder diffraction confirms Fe displacement within the 6-membered ring plane upon $CO_{2}$ interaction, while subsequent $CH_{4}$ exposure restores Fe(II) species in tetrahedral coordination. Density functional theory, combined with machine-learning interatomic potentials, identifies hydrocarbonate species as key intermediates inducing Fe oxidation and local structural distortion, consistent with experimental observations. The cooperative interplay of redox-active and acidic sites enables coupled activation of $CO_{2}$ and oxidation of $CH_{4}$ with complete atom utilization. Overall, two types of Fe(II) species with distorted tetrahedral symmetry are involved in $CO_{2}/CH_{4}$ activation, whereas other Fe(II) species remain inactive, establishing Fe-FER as a structurally flexible and electronically adaptive catalyst for sustainable $C_{1}$ conversion chemistry. Methanol ; Catalysis ; Oxygenate ; Ferrierite ; Bifunctional ; Acetic acid ; Octahedron ; Zeolite Catalytic Processes in Materials Science ; Chemical Looping and Thermochemical Processes ; Catalysis and Oxidation Reactions
Authors: Jakub Martinka | Mikołaj Martyka | Biman Medhi | Jiří Pittner | Pavlo O. Dral
Source: arXiv (Cornell University) (preprint, 2025-12-22)
Field: Materials Science — Materials Chemistry

Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics Nonadiabatic molecular dynamics is a key technique for investigating a broad range of photochemical and photophysical processes. Among the established approaches, surface hopping schemes are widely used and can be easily integrated with various quantum chemistry programs or machine learning models. We present a flexible framework in MLatom that includes a newly implemented Tully's fewest-switches surface hopping algorithm and its time-dependent Baeck--An variant. The capabilities of this framework are demonstrated through three representative examples corresponding to typical stages of a surface hopping study. First, we focus on methods providing energy, energy gradients and nonadiabatic couplings. We show that the flexibility of user-defined custom models can save computational time and that it is useful for benchmarking machine learning models. Next, we compare curvature-driven surface hopping schemes and show that Landau--Zener approach outperforms the time-dependent Baeck--An scheme. Finally, we showcase easy-to-use analysis tools for both individual trajectories and trajectory ensembles. This framework enables accelerated development of machine learning models and provides deeper insight into nonadiabatic dynamics. It is available as a part of the open-source MLatom package. Surface hopping ; Flexibility (engineering) ; Surface (topology) ; Focus (optics) ; Benchmarking ; Range (aeronautics) ; Trajectory ; Key (lock) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Ruoqian Fan (14144034) | Yaohuang Huang (22772091) | Lingchao Cai (1661113) | Zhen Chen (129176) | Pavlo O. Dral (545078) | Weiwei Fang (1879066)
Source: Figshare (article, 2025-12-03)
Field: Chemistry — Organic Chemistry

Breaking theScalability Barrier: Diversifiable Pd-CatalyzedDomino Reaction En Route to Dibenzo[<i>b,d</i>]azepines Herein, we present an efficient synthesis of diverse dibenzo[<i>b,d</i>]azepines in high yields via Pd-catalyzed domino reactions of 2,2’-dihalobiphenyls with <i>N</i>-allyl alkylamines. The key to this success is in situ generation of a five-membered, chelated Pd(IV) species via the subsequent activation of two C–Br bonds at the initial step, supported by control experiments, density functional theory, and machine learning calculations. Domino ; Scalability ; Key (lock) ; Control (management) ; Third generation Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions ; Catalytic Alkyne Reactions
Authors: Amit Chaudhari | Kushagra Agrawal | Andrew J. Logsdail
Source: ChemRxiv (preprint, 2025-06-20)
Field: Materials Science — Materials Chemistry

Machine learning generalised DFT+U projectors in a numerical atom-centred orbital framework Accurate electronic structure simulations of strongly correlated metal oxides are crucial for the atomic level understanding of heterogeneous catalysts, batteries and photovoltaics; but remain challenging to perform in a computationally tractable manner. Hubbard corrected density functional theory (DFT+U) in a numerical atom-centred orbital framework, has been shown to address this challenge but is susceptible to numerical instability when simulating common transition metal oxides (TMOs) e.g., TiO2, and rare-earth metal oxides (REOs) e.g., CeO2, necessitating the development of advanced DFT+U parameterisation strategies. In this work, the numerical instabilities of DFT+U are traced to the default atomic Hubbard projector, which we refine for Ti 3d orbitals in TiO2 using Bayesian optimisation, with a cost function and constraints defined using symbolic regression (SR) and support vector machines, respectively. The optimised Ti 3d Hubbard projector enables the numerically stable simulation of electron polarons at intrinsic and extrinsic defects in both anatase and rutile TiO2, with comparable accuracy to hybrid-DFT at several orders of magnitude lower computational cost. We extend the method by defining a general first-principles approach for optimising Hubbard projectors, based on reproducing orbital occupancies calculated using hybrid-DFT. Using a hierarchical SR-defined cost function that depends on DFT-predicted orbital occupancies, basis set parameters and atomic material descriptors, a generalised workflow for the one-shot computation of Hubbard U values and projectors is presented. The method transferability is shown for 10 prototypical TMOs and REOs, with demonstrable accuracy for unseen materials, that extends to complex battery cathode materials like LiCo(1-x)Mg(x)O(2-x). The work highlights the integration of advanced machine learning algorithms to develop cost-effective and transferable workflows for DFT+U parameterisation, enabling more accurate and efficient simulations of strongly correlated metal oxides. Density functional theory ; Hubbard model ; Hybrid functional ; Basis set ; Projector ; Atom (system on chip) ; Computer science ; Materials science ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Jakub Martinka | Mikołaj Martyka | Biman Medhi | Jiří Pittner | Pavlo O. Dral
Source: ArXiv.org (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics Nonadiabatic molecular dynamics is a key technique for investigating a broad range of photochemical and photophysical processes. Among the established approaches, surface hopping schemes are widely used and can be easily integrated with various quantum chemistry programs or machine learning models. We present a flexible framework in MLatom that includes a newly implemented Tully's fewest-switches surface hopping algorithm and its time-dependent Baeck--An variant. The capabilities of this framework are demonstrated through three representative examples corresponding to typical stages of a surface hopping study. First, we focus on methods providing energy, energy gradients and nonadiabatic couplings. We show that the flexibility of user-defined custom models can save computational time and that it is useful for benchmarking machine learning models. Next, we compare curvature-driven surface hopping schemes and show that Landau--Zener approach outperforms the time-dependent Baeck--An scheme. Finally, we showcase easy-to-use analysis tools for both individual trajectories and trajectory ensembles. This framework enables accelerated development of machine learning models and provides deeper insight into nonadiabatic dynamics. It is available as a part of the open-source MLatom package. Surface hopping ; Flexibility (engineering) ; Surface (topology) ; Focus (optics) ; Benchmarking ; Range (aeronautics) ; Trajectory ; Key (lock) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Feicheng Huan (16763088) | Feng Shi (305643) | Gaoyang Luo (16826985) | Xiang Pan (5774024) | Jingnan Zheng (8958215) | Jianguo Wang (1400488)
Source: Figshare (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Surface-DependentInterfacial Concentration of OxygenConfined within Pd Interlayers: Molecular Dynamics with a Neural NetworkPotential Obtaining a detailed understanding of the interfacial dynamics of oxygen on palladium surfaces is crucial for industrial applications. However, it remains challenging to develop reaction–transport coupling mechanisms to enhance the activity and stability of Pd-based catalysts in confined environments. Herein, by integrating the established global neural network (G-NN) potential and molecular dynamics (MD) simulations, the interfacial concentrations of confined O<sub>2</sub> molecules within Pd interlayers were investigated systematically under various conditions. The developed reactive NN potential, rigorously validated against DFT benchmarks with an average error of 0.026 eV/atom, demonstrated precise structural discrimination capabilities among three Pd surfaces and subsequently produced reasonable catalytic structures. The Pd(100) surface exhibited the highest reactivity, followed by Pd(211), with the lowest on Pd(111). These differences show strong correlations with a reduced interlayer distance (approximately 1 nm) and the degree of surface reconstruction patterns through a comprehensive analysis of mean square displacement and reaction rate. Density distribution, in conjunction with radial distribution function analyses, further demonstrates how the interlayer confinement effect, as well as surface-specific atomic arrangements, remarkably regulate the interfacial concentration of oxygen. This work provides universal guidance for elucidating the macroscopic mechanism linking the bulk and interfacial concentrations in confined systems through large-scale simulations. Molecular dynamics ; Catalysis ; Work (physics) ; Mean squared displacement ; Palladium ; Molecule ; Oxygen ; Coupling (piping) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction