jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shun‐Cai Zhao | Yi-Meng Huang | Yifan Yang | Ziran Zhao
Source: The Journal of Physical Chemistry Letters (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Multi-Time-Scale Time Encoding for CNN Prediction of Fenna–Matthews–Olson Energy-Transfer Dynamics Machine learning simulations of open quantum dynamics often rely on recursive predictors that accumulate error. We develop nonrecursive convolutional neural networks (CNNs) that map system parameters and a redundant time encoding directly to excitation energy transfer (EET) populations in the Fenna-Matthews-Olson (FMO) complex. The encoding-modified logistic plus tanh function normalizes time and resolves fast, transitional, and quasi-steady regimes, while physics-informed labels enforce population conservation and intersite consistency. Trained only on 0-7 ps reference trajectories generated with a Lindblad model in QuTiP, the network accurately predicts 0-100 ps dynamics across a range of reorganization energies, bath rates, and temperatures. Beyond 20 ps, the absolute relative error remains below 0.05, demonstrating stable long-time extrapolation. By avoiding step-by-step recursion, the method suppresses error accumulation and generalizes across time scales. These results show that redundant time encoding enables data-efficient inference of long-time quantum dissipative dynamics in realistic pigment-protein complexes and may aid the data-driven design of light-harvesting materials. Encoding (memory) ; Dynamics (music) ; Quantum ; Population ; Function (biology) ; Range (aeronautics) ; Convolutional neural network ; Limit (mathematics) ; Inference Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Rajath Alexander | Amit Kaushal | Jaspreet Singh | Kinshuk Dasgupta
Source: Carbon letters (article, 2025-01-20)
Field: Materials Science — Materials Chemistry

Open-atmosphere spinning of carbon nanotube fibers sans hydrogen flow by floating catalyst chemical vapor deposition: an insight into the mechanism Abstract This study introduces a novel method for synthesizing carbon nanotube (CNT) fibers using floating catalyst chemical vapor deposition (FC-CVD) in an open-atmosphere without the need for hydrogen as a carrier gas. Traditional FC-CVD techniques depend on hydrogen gas and require a harvest box with inert gas purging, which restricts scalability. Our approach utilizes nitrogen gas as the sole carrier, allowing for CNT fiber production without a harvest box. To understand the spinning process mechanism in an open-atmosphere, we conducted thermodynamic and computational fluid dynamics (CFD) analyses. Methanol was selected as the carbon source based on thermodynamic calculations, which revealed that at high temperatures, methanol forms CO and H 2 as thermodynamically stable species instead of carbon (C), thereby preventing soot formation. Moreover, methanol undergoes catalytic cracking exclusively in the presence of catalysts, further preventing soot formation. This approach allows operation at high partial pressure, even above the upper explosive limit (UEL), effectively preventing combustion. A 600 mm cooling zone was incorporated into the reactor to lower the outlet gas temperature below methanol's auto-ignition point, mitigating combustion risks. CFD calculations were employed to determine the necessary cooling zone length. Additionally, we developed a predictive model using the XGBoost machine learning method to efficiently map the parameter space for CNT fiber spinning, achieving an accuracy of 95.24%. The resulting CNT fibers demonstrate high electrical conductivity (240 ± 24 S/cm) and a low I D /I G ratio, indicating a high degree of crystallinity. Materials science ; Spinning ; Chemical vapor deposition ; Atmosphere (unit) ; Catalysis ; Carbon nanotube ; Chemical engineering ; Hydrogen ; Carbon nanotube supported catalyst ; Water vapor ; Mechanism (biology) ; Deposition (geology) ; Composite material ; Nanotechnology ; Organic chemistry ; Carbon nanofiber ; Meteorology ; Chemistry Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites ; Electrohydrodynamics and Fluid Dynamics
Authors: Ujjwal Pal | Sagar Varangane | Deepak S. Gavali | Bapan Biswas | Syed Asif Hussain | Kakarla Raghava Reddy | Annadanam V. Sesha Sainath | Jan Baeyens | Tejraj M. Aminabhavi
Source: Renewable and Sustainable Energy Reviews (article, 2025-11-30)
Field: Chemistry — Inorganic Chemistry

Next generation hydrogen from sunlight: ZIF-8 nanohybrids and the rise of sustainable catalysis Hydrogen production ; Water splitting ; Catalysis ; Photocatalysis ; Solar energy ; Hydrogen ; Density functional theory ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques ; Chemical Looping and Thermochemical Processes
Authors: Satish Kumar Adiga | Umesh V. Waghmare
Source: Computational Materials Science (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Accelerating the search for superconductors using machine learning Superconductivity ; Workflow ; Principal component analysis ; Cluster analysis ; Principal (computer security) ; Function (biology) ; Quantum Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Thomas van Hout | Oliver Loveday | Jordi Morales-Vidal | Santiago Morandi | Núria López
Source: Digital Discovery (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Evaluating the transfer learning from metals to oxides with GAME-Net-Ox GAME-Net-Ox, an extension of the GAME-Net graph neural network, enables fast prediction of adsorption energies for molecules with key organic functional groups on conductive and semiconductive rutile metal oxides and metal surfaces. Adsorption ; Metal ; Electrical conductor ; Rutile ; Molecule ; Key (lock) ; Transfer of learning Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Surface Chemistry and Catalysis
Authors: Bolin Yang | Zhilong Peng | Cun Zhang | Yin Yao | Shaohua Chen | Huajian Gao
Source: Physical Review Letters (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Reversible Phase Transition Enables Rapid Electrical Switching in Multilayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mi>MoTe</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> under Cyclic Strain MoTe_{2}, a promising material for flexible electronics and straintronics, exhibits rapid strain-induced conductivity changes. However, the underlying mechanisms remain poorly understood. Here, through deep-learning molecular dynamics and density-functional theory calculations, we identify a reversible phase transition between the semiconductive T_{b}^{'} phase and the semimetallic T^{*} phase as the key mechanism driving this phenomenon. This transition, which is highly sensitive to the direction of applied strain, provides valuable insights for optimizing MoTe_{2}-based high-frequency nanoelectronic devices and reducing fabrication-related failures. Phase transition ; Phase (matter) ; Materials science ; Physics ; Computer science ; Condensed matter physics ; Quantum mechanics 2D Materials and Applications ; Advanced Memory and Neural Computing ; Advanced Thermoelectric Materials and Devices
Authors: Xiangqin Cui | Xiaoxue Sun | Shuxin Xuan | Jinyu Liu | Dongfang Zhang | Jun Zhang | Xiaofei Fan | Xuesong Suo
Source: Agronomy (article, 2025-03-23)
Field: Chemistry — Analytical Chemistry

Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion Broccoli is a highly nutritious vegetable that is favored worldwide. Assessing and predicting the shelf life of broccoli holds considerable importance for effective resource optimization and management. The physicochemical parameters and spectral characteristics of broccoli are important indicators partially reflecting its shelf life. However, few studies have used spectral image information to predict and evaluate the shelf life of broccoli. In this study, multispectral imaging combined with multi-feature data fusion was used to predict and evaluate the shelf life of broccoli. Spectral data and textural features were extracted from multispectral images of broccoli and fused with the physicochemical parameters for analysis. Savitzky–Golay (SG) convolution smoothing and standard normal variate (SNV) and normalization (Norm) preprocessing methods were employed to preprocess the original spectral data and textural features, while a successive projection algorithm (SPA) was used to extract relevant feature bands. The physicochemical parameters for broccoli shelf life were predicted using three methods: support vector regression (SVR), random forest classification (RF), and 2D convolutional neural network (2D-CNN) models. Broccoli shelf life prediction models were evaluated using three classification methods: RF, 1D-CNN, and 2D-CNN. The results demonstrate that, among the models used for predicting and evaluating the shelf life of broccoli, the SPA+SG+RF classification model employing fused data Type C achieves the highest accuracy. Specifically, this method achieves accuracies of 88.98% and 88.64% for the training and validation sets, respectively. Multi-feature data fusion of spectral image information and physical and chemical parameters were combined with different machine learning methods to predict and evaluate the shelf life of broccoli. Multispectral image ; Shelf life ; Feature (linguistics) ; Sensor fusion ; Fusion ; Remote sensing ; Artificial intelligence ; Computer science ; Environmental science ; Pattern recognition (psychology) ; Geography ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Food Quality and Safety Studies ; Advanced Chemical Sensor Technologies
Authors: Gopalan Rajaraman | Rajanikanta Rana
Source: ChemRxiv (preprint, 2025-10-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Accurate Machine Learning Predictions of Magnetization Barriers and Relaxation Times in Dnh (n = 4–6) Dy(III) Single-Molecule Magnets Relaxation (psychology) ; Ab initio ; Anisotropy ; Spintronics ; Magnet ; Magnetization ; Ab initio quantum chemistry methods Magnetism in coordination complexes ; Machine Learning in Materials Science ; Advanced NMR Techniques and Applications
Authors: Yanwen Zhang | Ruoxing Shao | Yan Jiang | Haonan Fan | Zeyu Wang | Huijuan Shao | Hailiang Sun | Xuzhi Zhang | Yating Wang | Tao Jiang
Source: Molecular Catalysis (article, 2025-02-26)
Field: Chemistry — Organic Chemistry

Artificial neural networks assisted by DFT for boosting ligand design in ethylene tri-/tetramerization using silicon-bridged diphosphine (SBDP) ligands Ethylene ; Ligand (biochemistry) ; Boosting (machine learning) ; Silicon ; Catalysis ; Chemistry ; Organic chemistry ; Computer science ; Artificial intelligence ; Biochemistry ; Receptor Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Carbon dioxide utilization in catalysis
Authors: Hui Li | WenKe Lu | Chunlei Li | Jinyi Liu | Zihui Feng | Jiaming Liu | Lan Yang
Source: Computational Materials Science (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Structure-driven prediction and mechanism insights into piezoelectric performance of potassium sodium niobate via interpretable machine learning van der Waals force ; Support vector machine ; Piezoelectricity ; Feature (linguistics) ; Feature selection ; Energy (signal processing) ; Uncertainty quantification ; Test set Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Ferroelectric and Piezoelectric Materials
Authors: Diane Duroux | Paul P. Meyer | Giovanni Visoná | Niko Beerenwinkel
Source: GigaScience (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Generalizable machine learning models for rapid antimicrobial resistance prediction in unseen health care settings These results underscore the value of combining deep learning with chemical and spectral information to build generalizable, high-impact clinical artificial intelligence. Health care ; Deep learning ; Resistance (ecology) ; Patient care ; Value (mathematics) ; Predictive modelling ; Precision medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Healthcare
Authors: Shōgo Saito | R. Oka | Tetsuro Ikuta | Yuya Abe | Yonghyun Choi | Takumi Ohashi | Kevin Critchley | Jonghoon Choi | Stephen D. Evans | Masayoshi Tanaka | Mina Okochi
Source: ACS Applied Nano Materials (article, 2025-06-18)
Field: Materials Science — Biomaterials

Integration of Peptide Array Screening and Machine Learning for the Discovery of Peptides Directing Triangular Gold Nanoplate Formation Peptide-mediated one-pot synthesis of gold nanoparticles (AuNPs) with diverse morphologies has been explored using tetrachloroauric acid ions at room temperature. Among these, triangular gold nanoplates (AuNPLs) are particularly notable for their unique optical properties and potential applications in photothermal therapy. In this study, we integrated peptide array screening with machine learning to identify peptides and evaluate their AuNP synthesis potential. By analyzing the color characteristics of AuNPs synthesized by each peptide on the array, we trained machine learning models to predict biomineralization efficiency based on peptide sequences and the resulting AuNP optical characteristics. This approach led to the discovery of four peptide sequences, among which the BR2–3 peptide (RWGGGIGGWN) facilitated the formation of uniform triangular AuNPLs with a yield of 33%. While the overall synthesis efficiency remained comparable to previously identified peptides, our findings broaden the scope of peptide sequences that can direct controlled AuNPL formation, offering possibilities for tailored AuNP synthesis. Over 95% of these nanoparticles (triangular and decahedral) fell within the 10–100 nm size range, making them highly suitable for drug delivery systems (DDS) and other biomedical applications. AuNPs synthesized with BR2–3 exhibited higher cellular uptake compared to those synthesized with other peptides, suggesting enhanced internalization potential. This study demonstrates the feasibility of peptide array-based, machine learning-assisted peptide screening for the biomineralization of triangular AuNPLs, emphasizing their applicability in biosensing and biomedicine. Peptide ; Nanotechnology ; Colloidal gold ; Combinatorial chemistry ; Chemistry ; Materials science ; Biochemistry ; Nanoparticle Diatoms and Algae Research ; Advanced biosensing and bioanalysis techniques ; Advanced Proteomics Techniques and Applications
Authors: Yongzhou Xue | Xiaojuan Ni | Michael Titze | Shei Sia Su | Bo‐Han Wu | Liang Zhang | Chaohan Cui | Saikat Guha | Matt Eichenfield | Linran Fan
Source: Nature Communications (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

All-optical reconfiguration of single silicon-vacancy centers in diamond for non-volatile memories Strain engineering is vital for tuning the optical and spin properties of solid-state color centers, enhancing spin coherence and compensating emission wavelength shift. Here, we develop an all-optical approach to directly modify the local strain of color centers at the nanoscale by migrating the nearby defect. High-power pulsed optical irradiation triggers defect migration, which subsequently leads to the redistribution of the local crystal lattice of the host material. This redistribution alters the strain experienced by nearby color centers. Using silicon-vacancy centers in diamond, we validate this method and demonstrate a ground state splitting enhancement of up to 1.8 THz. Unlike conventional methods, our approach requires no external fields or nanostructure modifications, enabling non-volatile strain control and optical memory functionality across wide temperature ranges. Its local, permanent nature offers a scalable path for enhancing spin coherence in large-scale quantum systems and has potential applications in photonic machine learning. Control reconfiguration ; Vacancy defect ; Diamond ; Silicon ; Materials science ; Optoelectronics ; Nanotechnology ; Computer science ; Crystallography ; Chemistry ; Embedded system ; Composite material Diamond and Carbon-based Materials Research ; Photonic and Optical Devices ; Advanced Fiber Laser Technologies
Authors: Thomas Nevolianis | Jan G. Rittig | Alexander Mitsos | Kai Leonhard
Source: Journal of Cheminformatics (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Multi-fidelity graph neural networks for predicting toluene/water partition coefficients Accurate prediction of toluene/water partition coefficients of neutral species is crucial in drug discovery and separation processes; however, data-driven modeling of these coefficients remains challenging due to limited available experimental data. To address the limitation of available data, we apply multi-fidelity learning approaches leveraging a quantum chemical dataset (low fidelity) of approximately 9000 entries generated by COSMO-RS and an experimental dataset (high fidelity) of about 250 entries collected from the literature. We explore the transfer learning, feature-augmented learning, and multi-target learning approaches in combination with graph neural networks, validating them on two external datasets: one with molecules similar to training data (EXT-Zamora) and one with more challenging molecules (EXT-SAMPL9). Our results show that multi-target learning significantly improves predictive accuracy, achieving a root-mean-square error of 0.44 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>log</mml:mo> <mml:mi>P</mml:mi></mml:mrow> </mml:math> units for the EXT-Zamora, compared to a root-mean-square error of 0.63 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>log</mml:mo> <mml:mi>P</mml:mi></mml:mrow> </mml:math> units for single-task models. For the EXT-SAMPL9 dataset, multi-target learning achieves a root-mean-square error of 1.02 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>log</mml:mo> <mml:mi>P</mml:mi></mml:mrow> </mml:math> units, indicating reasonable performance even for more complex molecular structures. These findings highlight the potential of multi-fidelity learning approaches that leverage quantum chemical data to improve toluene/water partition coefficient predictions and address challenges posed by limited experimental data. We expect the applicability of the methods used beyond just toluene/water partition coefficients. Computer science ; Mean squared error ; Machine learning ; Artificial intelligence ; Artificial neural network ; Algorithm ; Statistics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrochemical Analysis and Applications
Authors: Xingyue Sun | Xueguang Shao | Wensheng Cai | Haohao Fu
Source: Communications in Computational Chemistry (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Breathing New Life into Classical Force Fields Classical force fields form the foundation of molecular dynamics simulations. Because classical force fields are limited to pairwise-additive Lennard-Jones potentials and fixed-charge electrostatics, they cannot accurately capture explicit polarization and charge-transfer effects, metal coordination bonds, or weak, directional nonbonded interactions such as $π$–$π$ stacking. Machine-learning force fields and polarizable force fields, which have been claimed to accurately characterize these types of interactions, have developed rapidly in recent years, raising an ongoing debate over whether further improvements to classical force fields are still necessary. Keeping this question in mind, we review various strategies proposed in recent years to improve the description of nonbonded interactions for classical force fields. These advances include the development of atomic charge models that accurately reflect molecular dipoles, water models that capture electrostatic screening and solvation effects, atom-pair-specific van der Waals parameters to mimic polarization, refined atom types that better represent chemical environments, and the introduction of virtual sites for accurately modeling of lone pairs. We also discuss the applicable scope of each strategy. Furthermore, by comparing classical force fields with polarizable and machine-learning-based force fields, we identify three major advantages of classical force fields: high computational efficiency, strong transferability, and ease of parameterization. Therefore, we join the ongoing debate and believe that the community will greatly benefit from further development of classical force fields. Through the improvement of the accuracy of characterizing nonbonded interactions, classical force fields can be widely applied in areas such as million-atom simulations and high-throughput screening. Force field (fiction) ; Polarizability ; van der Waals force ; Molecular dynamics ; Polarization (electrochemistry) ; Field (mathematics) ; Solvation ; Scope (computer science) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Crystallography and molecular interactions
Authors: Jialing Zhou | Huan Zeng | Caiqin Wu | Guilong Li | Chunmei Zhang | Kang Ye | Jian Wang
Source: Chemical Communications (review, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Research progress and prospects of metal–organic framework materials in corrosion protection Metal-organic frameworks (MOFs), with their tunable structures, large surface areas, and abundant functional groups, show great potential in corrosion protection. This review summarizes recent advances in MOF-based anticorrosion materials, covering synthesis methods, applications, challenges, and future directions. Preparation techniques such as microwave, room-temperature, hydrothermal/solvothermal, electrochemical, sonochemical, and mechanochemical syntheses are discussed, with analysis of their respective advantages and limitations. In applications, MOFs serve as nanofillers, corrosion inhibitors, nanocapsule-based self-healing agents, and superhydrophobic coatings. By enhancing barrier properties, mechanical strength, and enabling responsive inhibitor release, MOFs greatly improve corrosion resistance and self-healing capability. In addition to these advances, an analysis of cost and scalability is provided to assess the economic feasibility and industrial translation potential of MOF-based anticorrosion systems. Despite this progress, challenges remain in terms of production cost, scalability, and long-term stability. Future research should focus on clarifying MOF nucleation and structural regulation mechanisms, leveraging machine learning for material design, and developing cost-effective, eco-friendly, and scalable synthesis methods to advance the industrialization of high-performance MOF-based anticorrosion coatings. Corrosion ; Scalability ; Industrialisation ; Nucleation ; Production cost Metal-Organic Frameworks: Synthesis and Applications ; Corrosion Behavior and Inhibition ; Magnesium Oxide Properties and Applications