jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amirali Shateri | Zhiyin Yang | Jianfei Xie
Source: Energy & Fuels (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Molecular Dynamics Studies on Predicting Thermophysical Properties of Ethanol–Octane Blends This paper presents an innovative approach to predicting thermophysical properties of ethanol–octane blends by integrating molecular dynamics (MD) simulations with machine learning (ML) algorithms. The work addresses the growing interest in ethanol–gasoline blends as alternative fuels and the need for efficient computational methods to analyze their properties. Using MD simulations and various ML models such as Decision Tree Regression (DTR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR), the behavior of 660-molecule systems of ethanol–octane mixtures was modeled. The OPLS-AA force field was employed to accurately represent the molecular interactions. Among the ML models, DTR demonstrated the highest accuracy in predicting atomic displacements and velocities. The integration of MD with ML promises rapid and accurate predictions, with error rates consistently below 2.5% across different ethanol concentrations and timesteps. Notably, the ML model showcases remarkable speedup in computational efforts, approximately 1.8, 2.7, and 3.4 times faster for E10, E20 and E85 blends respectively compared with the traditional MD simulations. This approach not only enhances the understanding of ethanol–octane blend properties but also demonstrates the potential for ML to accelerate the complex molecular simulations. The findings of this study have significant implications for the design and optimization of alternative fuels, targeting the sustainable energy demand. Molecular dynamics ; Octane ; Gasoline ; Force field (fiction) ; Octane rating ; Biological system ; Random forest ; Decision tree ; Kriging ; Computer science ; Gaussian ; Chemistry ; Machine learning ; Artificial intelligence ; Computational chemistry ; Organic chemistry Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Computational Drug Discovery Methods
Authors: Xinyuan Xu | Li‐Gao Liu | Li‐Cheng Xu | Shuo‐Qing Zhang | Xin Hong
Source: Journal of the American Chemical Society (article, 2025-03-28)
Field: Chemistry — Organic Chemistry

Transfer Learning-Enabled Ligand Prediction for Ni-Catalyzed Atroposelective Suzuki–Miyaura Cross-Coupling Based on Mechanistic Similarity: Leveraging Pd Knowledge for Ni Discovery The rational design of novel molecular catalysts often confronts challenges due to complex structure-performance relationships. Emerging data-driven approaches provide revolutionary solutions, yet the application of machine learning to new catalyst development inevitably faces a low-data regime with limited effective structure-performance modelings available. In this study, we present a transfer learning strategy to facilitate knowledge transfer from well-documented Pd catalysis to a novel, underexplored Ni system. By synergistically modeling extensive Pd catalysis data with limited Ni/Sadphos data, our approach accurately predicted novel Sadphos ligands, enabling the first atroposelective Ni-catalyzed Suzuki-Miyaura cross-coupling reaction. The synthetic utility of the machine learning-predicted ligand was further demonstrated in its broad synthetic scope, gram-scale synthesis, and precise control of dual axial chiralities in ternaphthalene through the sequential coupling under Ni and Pd catalysis. Additionally, density functional theory calculations were employed to reveal the reaction mechanism and stereochemical model of this new Ni catalyst, validating the proposed mechanistic connection between Ni and Pd. This work demonstrates how machine learning models can effectively leverage mechanistic connectivity, applying extensive structure-performance relationship data from the literature to predict new catalysts, providing a novel strategy for the rational design of molecular catalysts from a few-shot learning perspective. Chemistry ; Coupling (piping) ; Similarity (geometry) ; Catalysis ; Ligand (biochemistry) ; Combinatorial chemistry ; Artificial intelligence ; Receptor ; Organic chemistry ; Biochemistry ; Computer science Axial and Atropisomeric Chirality Synthesis ; Molecular spectroscopy and chirality ; Catalytic Cross-Coupling Reactions
Authors: Johannes Karwounopoulos | M. Bieniek | Zhiyi Wu | Adam L. Baskerville | Gerhard König | Benjamin P. Cossins | Geoffrey P. F. Wood
Source: Journal of Chemical Theory and Computation (article, 2025-01-03)
Field: Materials Science — Materials Chemistry

Evaluation of Machine Learning/Molecular Mechanics End-State Corrections with Mechanical Embedding to Calculate Relative Protein–Ligand Binding Free Energies The development of machine-learning (ML) potentials offers significant accuracy improvements compared to molecular mechanics (MM) because of the inclusion of quantum-mechanical effects in molecular interactions. However, ML simulations are several times more computationally demanding than MM simulations, so there is a trade-off between speed and accuracy. One possible compromise are hybrid machine learning/molecular mechanics (ML/MM) approaches with mechanical embedding that treat the intramolecular interactions of the ligand at the ML level and the protein-ligand interactions at the MM level. Recent studies have reported improved protein-ligand binding free energy results based on ML/MM using ANI-2x with mechanical embedding, arguing that intramolecular interactions like torsion potentials of the ligand are often the limiting factor for accuracy. This claim is evaluated based on 108 relative binding free energy calculations for four different benchmark systems. As an alternative strategy, we also tested a tool that fits the MM dihedral potentials to the ML level of theory. Fitting was performed with the ML potentials ANI-2x and AIMNet2, and, for the benchmark system TYK2, also with quantum-mechanical calculations using ωB97M-D3(BJ)/def2-TZVPPD. Overall, the relative binding free energy results from MM with Open Force Field 2.2.0, MM with ML-fitted torsion potentials, and the corresponding ML/MM end-state corrected simulations show no statistically significant differences in the mean absolute errors (between 0.8 and 0.9 kcal mol<sup>-1</sup>). This can probably be explained by the usage of the same MM parameters to calculate the protein-ligand interactions. Therefore, a well-parametrized force field is on a par with simple mechanical embedding ML/MM simulations for protein-ligand binding. In terms of computational costs, the reparametrization of poor torsional potentials is preferable over employing computationally intensive ML/MM simulations of protein-ligand complexes with mechanical embedding. Also, the refitting strategy leads to lower variances of the protein-ligand binding free energy results than the ML/MM end-state corrections. For free energy corrections with ML/MM, the results indicate that better convergence and more advanced ML/MM schemes will be required for applications in computer-guided drug discovery. Molecular mechanics ; Embedding ; Computer science ; State (computer science) ; Ligand (biochemistry) ; Statistical physics ; Physics ; Chemistry ; Artificial intelligence ; Algorithm ; Quantum mechanics ; Molecular dynamics ; Biochemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Xinhao Wang | Yihang Feng | Yi Wang | Zhu Hong-lin | Dongjin Song | Cangliang Shen | Yangchao Luo
Source: Journal of Agriculture and Food Research (article, 2025-02-11)
Field: Chemistry — Analytical Chemistry

Enhancing optical non-destructive methods for food quality and safety assessments with machine learning techniques: A survey Food quality and safety are critical to global health and economic stability, but traditional assessment methods, such as chemical assays and microbial culturing, are often destructive, time-consuming, and unsuitable for real-time and high-throughput applications. Optical non-destructive techniques, including imaging methods (e.g., red-green-blue (RGB) imaging, hyperspectral imaging (HSI)) and spectral methods (e.g., near-infrared (NIR) spectroscopy), offer real-time, precise, and non-invasive assessments while preserving sample integrity. However, the complex datasets generated by these techniques require advanced machine learning (ML) models for effective analysis. These methods generate complex, multidimensional datasets that align with ML approaches, unlocking advanced capabilities in data interpretation and decision-making. By integrating optical non-destructive techniques with ML models, ranging from classical algorithms like random forests (RF) and support vector machines (SVM) to deep learning architectures such as convolutional neural networks (CNNs), notable progress has been achieved in automating feature extraction, classification, and prediction tasks. This integration enhances the precision, scalability, and applicability of food quality and safety assessments, enabling tasks such as real-time grading, sorting, and microbial detection in diverse food systems. Advanced models like YOLO further expand the potential for real-time object detection in dynamic settings such as smart farms and food processing lines. Despite these advances, challenges remain in addressing the variability of food matrices, real-time processing limitations, and the need to integrate data from multiple optical models. This survey explores the integration of ML with optical non-destructive methods to enhance food quality and safety assessments, highlighting recent advancements and future opportunities. • Overview of imaging and spectral techniques for food evaluation. • Development of the workflow for machine learning in optical data analysis. • Machine learning integration to improve optical nondestructive methods in food assessments. • Evaluation of classical and deep learning approaches for optical non-destructive methods. Quality (philosophy) ; Food safety ; Computer science ; Nondestructive testing ; Artificial intelligence ; Food science ; Medicine ; Chemistry ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Harshit Sharma | Gaurav Arora | Raj Kumar | Suman Debnath | Suchart Siengchin
Source: Discover Materials (article, 2025-01-04)
Field: Materials Science — Materials Chemistry

Machine learning-based study of hardness in polypropylene/carbon nanotube and low-density polyethylene/carbon nanotube composites In the present work, the hardness prediction of polypropylene/carbon nanotubes (PP/CNT) and low-density polyethylene/carbon nanotubes (LDPE/CNT) composite materials, processed by microwave technique, has been explored using machine learning models i.e. (Random Forest, Support Vector Regression, K-Nearest Neighbors, Linear Regression, and Neural Network). Four input vectors have been used in the construction of proposed network, such as CNT concentration, power, pressure applied, and exposure time. Hardness prediction is one output that has been evolved from the proposed work. This study presents the prediction of hardness based on machine learning models for both PP/CNT and LDPE/CNT composite materials, and the results show that the Random Forest model consistently performs better than the others models in context with performance metrics like Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Rate of determination (R2) values. Investigations have been performed on resampling strategies, showing that the jackknife approach enhances model precision and robustness in the case of LDPE/CNT composites. For PP/CNT composite material, it has been noticed that Random Forest gives the highest value of R2 (0.94), whereas Random Forest has the lowest R2 value 0.18 for LDPE/CNT composite material. Random Forest is the most reliable model for predicting the characteristics of PP/CNT composite material due to its ability to handle complex datasets. The LDPE/CNT composite material demonstrates superior prediction accuracy, with a maximum error of just 1.61%, making it a better option for high-precision applications due to enhanced mechanical interactions and improved CNT dispersion. Polypropylene ; Carbon nanotube ; Composite material ; Materials science ; Polyethylene ; Nanotube ; Carbon nanotube metal matrix composites ; Low-density polyethylene Carbon Nanotubes in Composites ; Additive Manufacturing and 3D Printing Technologies ; Polymer Nanocomposite Synthesis and Irradiation
Authors: V.S. Shaisundaram | S. Saravanakumar | Rajesh Mohan | Vishnupriya Gurunathan
Source: Polymer Composites (article, 2025-03-14)
Field: Materials Science — Polymers and Plastics

Assessment of mechanical, thermal, and sliding wear performance of chemically treated alumina‐filled biocomposites using machine learning and response surface methodology Abstract This study examines how NaOH treatment and alumina filler affect the mechanical properties, water absorption, thermal degradation, and sliding wear of epoxy composites reinforced with pineapple leaf fiber. NaOH treatment greatly improved the composites' tensile, flexural, and impact strengths by strengthening the bond between the fiber and matrix. Furthermore, the incorporation of alumina filler further elevated the mechanical properties. The composite with 10% alumina showed peak values of 41.4 MPa in tensile strength, 63.8 MPa in flexural strength, and 37.6 kJ/m 2 in impact strength. Because hygroscopic parts were removed from the treated composites, they absorbed much less water. The 15% alumina composite had the lowest absorption at 18% after 192 h. Thermal degradation analysis showed that NaOH treatment improved thermal stability, with the 15% alumina composite having the highest char residue (15.3%) at 700°C. Sliding wear tests showed that alumina reinforcement significantly reduced specific wear rate (SWR) and coefficient of friction (COF). The improved 15% alumina composite had an SWR of 0.2598 × 10 −5 mm 3 /Nm and a COF of 0.103 when sliding at 120 cm/s, with a 45 N load and over 1500 m of distance. A scanning electron microscopy study found that untreated composites experienced severe abrasive wear, while treated and reinforced composites exhibited mild adhesive wear. The study shows that treating PALF composites with NaOH and adding alumina enhance their mechanical, thermal, and tribological properties, making them suitable for high‐performance industrial applications. Highlights Alumina filler improved tensile (41.4 MPa) and flexural strength (63.8 MPa). NaOH‐treated composites absorbed 18% less moisture, enhancing durability. Thermal stability improved, with 15.3% char residue at 700°C for 15% alumina. Optimized composite achieved the lowest wear rate (0.2598 × 10 −5 mm 3 /Nm). Artificial neural network and response surface methodology accurately predicted and optimized composite wear behavior. Materials science ; Composite material ; Thermal ; Thermodynamics Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Polymer Nanocomposites and Properties
Authors: Yuxiang Gao | Fenglin Deng | Ri He | Zhicheng Zhong
Source: Nature Communications (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

Spontaneous curvature in two-dimensional van der Waals heterostructures Two-dimensional (2D) van der Waals heterostructures consist of different 2D crystals with diverse properties, constituting the cornerstone of the new generation of 2D electronic devices. Yet interfaces in heterostructures inevitably break bulk symmetry and structural continuity, resulting in delicate atomic rearrangements and novel electronic structures. In this paper, we predict that 2D interfaces undergo "spontaneous curvature", which means when two flat 2D layers approach each other, they inevitably experience out-of-plane curvature. Based on deep-learning-assisted large-scale molecular dynamics simulations, we observe significant out-of-plane displacements up to 3.8 Å in graphene/BN bilayers induced by curvature, producing a stable hexagonal moiré pattern, which agrees well with experimentally observations. Additionally, the out-of-plane flexibility of 2D crystals enables the propagation of curvature throughout the system, thereby influencing the mechanical properties of the heterostructure. These findings offer fundamental insights into the atomic structure in 2D van der Waals heterostructures and pave the way for their applications in devices. van der Waals force ; Curvature ; Physics ; Quantum mechanics ; Geometry ; Mathematics ; Molecule Graphene research and applications ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Leiji Li | Shiyu He | Fei Xiao | Yi Zeng | Yang Liu | Ying Zhou | Xiaorong Cai | Xuejun Jin
Source: Progress in Materials Science (article, 2025-03-27)
Field: Materials Science — Materials Chemistry

Cooling innovations: Elastocaloric shape memory alloys, manufacturing, simulation, and refrigerator Materials science ; Refrigerator car ; Shape-memory alloy ; Metallurgy ; Mechanical engineering ; Engineering Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Creativity in Education and Neuroscience
Authors: Ruiming Wen | Yu Han | Xuemei Tang | Wei Zeng | Sihang Zhang | Long Wu
Source: Trends in Food Science & Technology (article, 2025-04-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing food safety with SERS: The role of noble metal nanomaterials in detecting food contaminants Noble metal ; Food safety ; Nanomaterials ; Contamination ; Nanotechnology ; Environmental science ; Environmental chemistry ; Materials science ; Metal ; Chemistry ; Metallurgy ; Food science ; Biology Gold and Silver Nanoparticles Synthesis and Applications ; Melamine detection and toxicity ; Advanced Nanomaterials in Catalysis
Authors: Waqas Ahmad | Jamal Kazmi | Muhammad Zubair Nawaz | Amine El Moutaouakil | Jinying Zhang | Yu. Yu. Illarionov | Zhiming Wang
Source: Advanced Functional Materials (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Interface Engineering in Van der Waals Heterostructures: Enhancing Photodetector Efficiency through Structural and Functional Modifications Abstract 2D materials and their van der Waals (vdWs) heterostructures have shown great potential for developing promising applications in optoelectronics, especially for photodetector devices. However, to fully harness their capabilities, precise control over the interfaces between these 2D materials in vdWs heterostructures is pivotal. Interface engineering in vdWs heterostructures has emerged as a key approach in material science as it enables the fine‐tuning of the interlayer interactions, their band alignments, and it can control the charge transfer dynamics, ultimately enhancing the photodetector device performance. This review first highlights advanced figures of merit for evaluating photodetector performance and then provides an overview of the fundamentals of vdWs heterostructures, with a focus on their optoelectronic properties. The recent advancements in interface engineering, including surface passivation, defect engineering, and contact engineering, all of which contribute to improving the optical parameters in the photodetector device applications, are provided. New functionalities such as polarization detection and multi‐spectral imaging, which open up exciting possibilities in the domain of optoelectronics, are discussed. Lastly, we provide conclusions and future research directions, highlighting the integration of machine learning techniques for optimizing photodetection technology. Despite challenges related to interface quality, scalability, and long‐term stability, interface engineering remains a crucial strategy for advancing photodetector performance, enabling innovations and breakthroughs in optoelectronics. Photodetector ; Photodetection ; Heterojunction ; Interface (matter) ; van der Waals force ; Figure of merit 2D Materials and Applications ; Graphene research and applications ; Thermal properties of materials
Authors: Chenjie Yang | Shuangshuang Ji | Shun Shen | Hailong Yu | Chunhui Deng
Source: TrAC Trends in Analytical Chemistry (article, 2025-02-14)
Field: Chemistry — Spectroscopy

Advances in nanomaterials based laser desorption/ionization mass spectrometry for metabolic analysis Mass spectrometry ; Nanomaterials ; Desorption ; Ionization ; Surface-enhanced laser desorption/ionization ; Laser ; Chemistry ; Mass spectrometry imaging ; Analytical Chemistry (journal) ; Materials science ; Environmental chemistry ; Nanotechnology ; Chromatography ; Sample preparation in mass spectrometry ; Physics ; Electrospray ionization ; Physical chemistry ; Optics ; Ion ; Adsorption ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Yehlin Cho | Martin Pačesa | Zhidian Zhang | Bruno E. Correia | Sergey Ovchinnikov
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-04-06)
Field: Materials Science — Materials Chemistry

Boltzdesign1: Inverting All-Atom Structure Prediction Model for Generalized Biomolecular Binder Design Abstract Deep learning in structure prediction has revolutionized protein research, enabling large-scale screening, novel hypothesis generation, and accelerated experimental design across biological domains. Recent advances, including RoseTTAFold-AA and AlphaFold3, have extended structure prediction models to work with small molecules, nucleic acids, ions, and covalent modifications. We present BoltzDesign1, which inverts the Boltz-1 model, an open source reproduction of AlphaFold3, to enable the design of protein binders for diverse molecular targets without requiring model finetuning. By utilizing only the Pairformer and Confidence modules, our method significantly reduces computational costs while achieving outstanding in silico success rates and diversity in binder generation. Optimizing directly on the distogram allows us to shape the probability distribution of atomic distances, rather than adjusting a single structure, steering the design toward sequences that yield robust structures with well-defined energy minima. By leveraging a fully atomic model trained on a wide variety of macromolecules, we can generate diverse heterocomplexes with flexible ligand conformations—a capability not currently matched by existing methods. This approach enables the design of novel protein interactions with potential applications in biosensors, enzyme engineering, therapeutic development, and biotechnological innovations. Atom (system on chip) ; Computer science ; Parallel computing Machine Learning in Materials Science
Authors: Nuozhou Yi | Zhen Wang | Peidi Zhou | Qiaohang Guo | Chan Zheng | Ming‐Hua You | Huamin Chen | Mingcen Weng
Source: Chemical Engineering Journal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multi-Functional Nb4C3T -silk nanofiber composites for self-powered sensors and soft actuators Nanofiber ; Composite material ; Materials science ; Actuator ; SILK ; Electrospinning ; Polymer ; Computer science MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Advanced Materials and Mechanics