jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mozhdeh Fathidoost | Yangyiwei Yang | Nathalie Thor | Jan Bernauer | Astrid Pundt | Ralf Riedel | Bai‐Xiang Xu
Source: Advanced Engineering Materials (article, 2024-03-01)
Field: Materials Science — Materials Chemistry

Thermal Conductivity Analysis of Polymer‐Derived Nanocomposite via Image‐Based Structure Reconstruction, Computational Homogenization, and Machine Learning Macroscopic thermal properties of engineered or inherent composites depend substantially on the composite structure and the interface characteristics. While it is acknowledged that unveiling such dependency relation is essential for materials design, the complexity involved in, e.g., microstructure representation and limited data impedes the research progress. Herein, this issue is tackled by machine learning techniques on image‐based microstructure and property data predicted from physics simulations, along with experimental validation. The methodology is demonstrated for the model system ultrahigh‐temperature ceramic nanocomposite. The structure is reconstructed from scanning electron microscope images, and is resolved by a diffuse‐interface representation, which is advantageous in handling complicated structure and interface properties. Subsequently, hierarchical finite element homogenization is carried out to evaluate the effective thermal conductivity. A thorough comparison between the computed results and experimentally measured data, conducted across diverse temperatures and varying interface thermal resistances, reveals a high level of agreement. The observed agreement allows for the inverse estimation of the interface thermal resistance, a parameter typically challenging to ascertain directly through experimental means. Utilizing comprehensive data, a machine learning surrogate model has been meticulously trained to accurately predict the effective thermal conductivity of composite structures with exceptional performance. Homogenization (climate) ; Materials science ; Nanocomposite ; Thermal conductivity ; Microstructure ; Thermal ; Ceramic ; Finite element method ; Composite number ; Composite material ; Interface (matter) ; Representation (politics) ; Inverse problem ; Mechanical engineering ; Biological system ; Computer science ; Artificial intelligence ; Structural engineering ; Thermodynamics ; Physics ; Mathematics ; Mathematical analysis ; Engineering Thermal properties of materials ; Composite Material Mechanics ; Heat Transfer and Optimization
Authors: Qi Kong | Yasushi Shibuta
Source: MATERIALS TRANSACTIONS (article, 2024-05-12)
Field: Materials Science — Materials Chemistry

Advancing Thermal Conductivity Prediction of Metallic Materials by Integrating Molecular Dynamics Simulation with Machine Learning Molecular dynamics (MD) simulation has an intrinsic limitation when calculating thermal conductivity of metals. That is, only phonon (lattice) thermal conductivity can be derived from trajectory of atoms, which is obtained by solving the Newton ’s equations of motion numerically. Therefore, significant contribution of electrons in metals remains unaccounted for. In this study, a Light Gradient Boosting Machine (LightGBM) regression model is employed to predict thermal conductivity of metals using heat flux calculated by MD simulation, electrical conductivity and others as input variables. The LightGBM model successfully predicts the complex non-linear Green-Kubo relation for thermal conductivity calculation even though the underlying physical mechanisms are not entirely clear. The model predicts various temperature dependences of thermal conductivity of metals accurately. Furthermore, the model trained with known compositions of Al-Cu alloys is proved to estimate the thermal conductivity of alloys with unknown compositions. The model also demonstrates a certain level of predictive ability for alloys with different compositions and temperatures. This study demonstrates the potential of a data-driven approach as an efficient method for uncovering complex relationships between incomplete data from MD simulations and true materials properties, especially in cases where the underlying physics is elusive. Materials science ; Thermal conductivity ; Molecular dynamics ; Metal ; Nanotechnology ; Metallurgy ; Composite material ; Computational chemistry Machine Learning in Materials Science
Authors: Hao Zhou | Shuxiang Zhou | Zilong Hua | Kaustubh Bawane | Tianli Feng
Source: Applied Physics Letters (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Impact of classical statistics on thermal conductivity predictions of BAs and diamond using machine learning molecular dynamics Machine learning interatomic potentials (MLIPs) have greatly enhanced molecular dynamics (MD) simulations, achieving near-first-principles accuracy in thermal conductivity studies. In this work, we reveal that this accuracy, observed in BAs and diamond at sub-Debye temperatures, stems from an accidental error cancelation: classical statistics overestimates specific heat while underestimating phonon lifetimes, balancing out in thermal conductivity predictions. However, this balance is disrupted when isotopes are introduced, leading MLIP-based MD to significantly underpredict thermal conductivity compared to experiments and quantum statistics-based Boltzmann transport equation. This discrepancy arises not from classical statistics affecting phonon–isotope scattering rates but from its impact on the interplay between phonon–isotope and phonon–phonon scattering in the normal scattering-dominated BAs and diamond. This work underscores the limitations of MLIP-based MD for thermal conductivity studies at sub-Debye temperatures. Thermal conductivity ; Phonon ; Boltzmann equation ; Diamond ; Scattering ; Phonon scattering ; Debye model ; Molecular dynamics ; Condensed matter physics ; Debye ; Work (physics) ; Materials science ; Physics ; Statistical physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Nuclear Materials and Properties
Authors: M. Karim | Md. Redwanul Karim | Sajid Muhaimin Choudhury
Source: Optics & Laser Technology (preprint, 2024-08-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synergizing deep learning and phase change materials for four-state broadband multifunctional metasurfaces in the visible range Achromatic lens ; Broadband ; Optics ; Amplitude ; Phase (matter) ; Computer science ; Materials science ; Optoelectronics ; Physics Metamaterials and Metasurfaces Applications ; Polydiacetylene-based materials and applications ; Optical Wireless Communication Technologies
Authors: Yubo Zhou | Yingxuan Dong | Haojun Ma | Junnan Lv | Qun Li
Source: Journal of Nuclear Materials (article, 2024-12-17)
Field: Materials Science — Materials Chemistry

Inversion of the fracture toughness of zirconium alloy cladding interface in nuclear fuel using splitting method via general regression neural network Alloy ; Cladding (metalworking) ; Zirconium ; Artificial neural network ; Inversion (geology) ; Fracture toughness ; Materials science ; Regression ; Zirconium alloy ; Geology ; Metallurgy ; Computer science ; Seismology ; Artificial intelligence ; Mathematics ; Statistics Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: ‬‬‬‬‬‬Kaveh Farshadfar | Kari Laasonen
Source: ACS Catalysis (article, 2024-09-16)
Field: Chemistry — Inorganic Chemistry

Comparison of the Efficiency of B–O and B–C Bond Formation Pathways in Borane-Catalyzed Carbene Transfer Reactions Using α-Diazocarbonyl Precursors: A Combined Density Functional Theory and Machine Learning Study Lewis acidic boranes, especially tris(pentafluorophenyl)borane [B(C<sub>6</sub>F<sub>5</sub>)<sub>3</sub>], have emerged as metal-free catalysts for carbene transfer reactions of α-diazocarbonyl compounds in a variety of functionalization reactions. The established mechanism for how borane facilitates carbene generation for these compounds in the scientific community is based on the formation of a B-O (C=O) intermediate (<i>path O</i>). Herein, we report an extensive DFT study that challenges the notion of a ubiquitous <i>path O</i>, revealing that B-C(=N=N) bond formation (<i>path C</i>) for certain diazocarbonyl substrates proves to be the preferred pathway. This study elucidates, through the introduction of 22 various substituents on each side of the α-diazocarbonyl backbone, how the electron-donating and -withdrawing properties of substituents influence the competition between these B-O and B-C pathways. To elucidate the impact of the electronic features of diazo substrates on the competition between the O and C pathways in the studied dataset, we employed a machine learning approach based on the Random Forest model. This analysis revealed that substrates with higher electron density on the diazo-attached carbon, lower electron density on the carbonyl carbon, and more stable HOMO orbitals tend to proceed via <i>path</i> <i>C</i>. Furthermore, this study not only demonstrates that borane efficiency in facilitating N<sub>2</sub> release is greatly affected by the nature of substituents on both sides of the α-diazocarbonyl functionality but also shows that for some substrates, borane is incapable of catalyzing the release of molecular nitrogen. Carbene ; Density functional theory ; Catalysis ; Chemistry ; Borane ; Organocatalysis ; Photochemistry ; Computational chemistry ; Organic chemistry ; Enantioselective synthesis Asymmetric Hydrogenation and Catalysis ; Cyclopropane Reaction Mechanisms ; Carbon dioxide utilization in catalysis
Authors: R. Zhou | Kun Luo | Ling Fei | Qi An
Source: ACS electrochemistry. (article, 2024-10-25)
Field: Materials Science — Materials Chemistry

Unraveling Superionic Conductivity in Na<sub>2</sub>B<sub>12</sub>H<sub>12</sub>: Molecular Dynamics Study of Phase Transition, Anion Reorientation, and Ionic Conductivity via Machine Learning Force Field Metal closo-hydroborates, particularly Na2B12H12, have emerged as promising solid-state electrolytes for sodium-ion batteries, owing to their high ionic conductivity at elevated temperatures. Despite their potential, the mechanisms underpinning their superionic conduction and phase transitions remain incompletely understood. In this study, we develop a machine learning force field (ML-FF) for Na2B12H12, enabling large-scale molecular dynamics simulations that capture the intricacies of its phase transition, anion reorientation dynamics, and ionic conductivity. Our simulations reveal a martensitic transformation from a monoclinic to a body-centered cubic (bcc) phase at ∼675 K, resulting in a dramatic enhancement in Na+ conductivity and a significant decrease in the activation energy for anion reorientation. Additionally, cooling simulations indicate an incomplete reverse phase transition due to rapid cooling, highlighting the complexities of phase stability in Na2B12H12. These findings emphasize the critical role of anion reorientation and the cooperative movement of cations and anions in facilitating superionic conduction. By leveraging the accuracy and efficiency of ML-FFs, this study provides unprecedented atomistic insights into the mechanisms driving high-temperature conductivity in Na2B12H12, offering pathways for the optimization of closo-hydroborates as next-generation solid-state electrolytes. Ionic conductivity ; Conductivity ; Ion ; Fast ion conductor ; Phase transition ; Molecular dynamics ; Phase (matter) ; Condensed matter physics ; Ionic bonding ; Field (mathematics) ; Materials science ; Chemical physics ; Force field (fiction) ; Chemistry ; Physics ; Physical chemistry ; Computational chemistry ; Electrolyte ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Nuclear Materials and Properties
Authors: Xiaohua Tian | Xiangyu Tong | Jian Li | Yulin Pan | Hong-Xing Li | Wenbin Zhao | Haipo Zhang | Changlong Tan
Source: Materials Today Communications (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

Interpretable machine learning for simultaneous designing martensitic transformation temperature and thermal hysteresis of high-entropy shape memory alloys Materials science ; Shape-memory alloy ; Diffusionless transformation ; Thermal hysteresis ; Hysteresis ; Thermodynamics ; Entropy (arrow of time) ; Thermal ; Martensite ; Transformation (genetics) ; Metallurgy ; Condensed matter physics ; Microstructure ; Phase transition Shape Memory Alloy Transformations ; High Entropy Alloys Studies ; Machine Learning in Materials Science
Authors: Nicéphore Bonnet | Nicola Marzari
Source: Journal of Chemical Theory and Computation (article, 2024-05-21)
Field: Materials Science — Materials Chemistry

Solvation Free Energies from Machine Learning Molecular Dynamics The present work proposes an extension to the approach of [Xi, C; et al. <i>J. Chem. Theory Comput.</i> <b>2022,</b> <i>18,</i> 6878] to calculate ion solvation free energies from first-principles (FP) molecular dynamics (MD) simulations of a hybrid solvation model. The approach is first re-expressed within the quasi-chemical theory of solvation. Then, to allow for longer simulation times than the original first-principles molecular dynamics approach and thus improve the convergence of statistical averages at a fraction of the original computational cost, a machine-learned (ML) energy function is trained on FP energies and forces and used in the MD simulations. The ML workflow and MD simulation times (≈200 ps) are adjusted to converge the predicted solvation energies within a chemical accuracy of 0.04 eV. The extension is successfully benchmarked on the same set of alkaline and alkaline-earth ions. Solvation ; Molecular dynamics ; Dynamics (music) ; Computer science ; Data science ; Statistical physics ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Pierre Kateb | Alice Fornaciari | Chakaveh Ahmadizadeh | Alexander V. Shokurov | Fabio Cicoira | Carlo Menon
Source: Advanced Intelligent Systems (article, 2024-11-01)
Field: Materials Science — Polymers and Plastics

High‐Performance Textile‐Based Capacitive Strain Sensors via Enhanced Vapor Phase Polymerization of Pyrrole and Their Application to Machine Learning‐Assisted Hand Gesture Recognition High-Performance Textile-Based Capacitive Strain Sensors In article number 2400292, Alexander Shokurov, Carlo Menon, and co-workers present high-performance textile-based capacitive strain sensors for wearable applications. Through vapor-phase polymerization of pyrrole, enhanced via addition of co-vapor and imidazole, good conductivity and robustness are achieved in a stretchable textile. A new insulation technique using polymer composites provides durability and dielectric coating. Intertwining such fibers together creates a stretchable capacitive sensor. Integrated into a textile glove, sensors precisely capture fine hand motions. A machine learning model classifies 12 gestures with 100% accuracy, showcasing its potential for wearable technology. Capacitive sensing ; Polymerization ; Textile ; Pyrrole ; Materials science ; Gesture ; Vapor phase ; Phase (matter) ; Computer science ; Composite material ; Chemistry ; Artificial intelligence ; Organic chemistry ; Polymer ; Physics ; Operating system Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Polydiacetylene-based materials and applications
Authors: Yinhan He | Zaiyi Zheng | Patrick Soga | Yaochen Zhu | Yushun Dong | Jundong Li
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Explaining Graph Neural Networks with Large Language Models: A Counterfactual Perspective on Molecule Graphs In recent years, Graph Neural Networks (GNNs) have become successful in molecular property prediction tasks such as toxicity analysis.However, due to the black-box nature of GNNs, their outputs can be concerning in high-stakes decision-making scenarios, e.g., drug discovery.Facing such an issue, Graph Counterfactual Explanation (GCE) has emerged as a promising approach to improve GNN transparency.However, current GCE methods usually fail to take domain-specific knowledge into consideration, which can result in outputs that are not easily comprehensible by humans.To address this challenge, we propose a novel GCE method, LLM-GCE, to unleash the power of large language models (LLMs) in explaining GNNs for molecular property prediction.Specifically, we utilize an autoencoder to generate the counterfactual graph topology from a set of counterfactual text pairs (CTPs) based on an input graph.Meanwhile, we also incorporate a CTP dynamic feedback module to mitigate LLM hallucination, which provides intermediate feedback derived from the generated counterfactuals as an attempt to give more faithful guidance.Extensive experiments demonstrate the superior performance of LLM-GCE.Our code is released on https://github.com/YinhanHe123/new_LLM4G NNExplanation. Counterfactual thinking ; Perspective (graphical) ; Computer science ; Artificial neural network ; Graph ; Graph theory ; Theoretical computer science ; Artificial intelligence ; Epistemology ; Mathematics ; Combinatorics ; Philosophy Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sophia Li | Emma Wang | Leia Pei | S. K. Deb | Prashanth Prabhala | Sai Hruday Reddy Nara | Raina Panda | Shiven Eltepu | Marx Akl | Larry McMahan | Edward Njoo
Source: Artificial Intelligence Chemistry (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Evaluation of machine learning models for the accelerated prediction of density functional theory calculated 19F chemical shifts based on local atomic environments The introduction of fluorine in compounds plays a crucial role in drug development as it greatly influences their final pharmacokinetic and dynamic properties. Due to the prevalence of fluorine in FDA-approved drugs in recent years, identifying the mechanisms driving their chemical transformations has become crucial in the drug discovery landscape. 19 F NMR spectroscopy is a powerful analytical technique that allows for the examination of fluorine-containing compounds, offering valuable information about their structure, dynamics, and reactivity. NMR spectra can be interpreted through the leveraging of Density Functional Theory (DFT). However, the screening of compounds and discovery of feasible drug candidates is limited due to its computational cost. Here, we present a machine learning approach to accelerate the prediction of DFT-calculated 19 F NMR chemical shifts. The fluorine atoms’ features in the models were derived from their local three-dimensional environments, representing their neighboring atoms within a radius of n Å away from the given fluorine atom in the compound. A comparative analysis of thirteen regression models was conducted using features extracted from 501 fluorinated compounds in our laboratory’s chemical inventory. Among the models, Gradient Boosting Regression (GBR) exhibited the highest performance, achieving a mean absolute error of 3.31 ppm with a local environment radius of 3 Å. This demonstrates a comparable accuracy to DFT calculations while reducing computational time from several hundred seconds to milliseconds. 3 Å was also found to be the most optimal radius across all models when encoding features for local atomic environments. Density functional theory ; Machine learning ; Computer science ; Statistical physics ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Shuai Liu | Ze-Lin Wang | Ling Zhang | Guoxiang Chen | Hang-Fan Yang | Xiaonan Liang | Jie Qiu
Source: Surfaces and Interfaces (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Investigations on symbol regression for improving the prediction accuracy of gas-metal adsorption energies in machine learning Materials science ; Adsorption ; Regression analysis ; Regression ; Metal ; Machine learning ; Thermodynamics ; Metallurgy ; Statistics ; Physical chemistry ; Computer science ; Mathematics ; Physics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Fuel Cells and Related Materials
Authors: Naile ANGIN | Murat Ertaş | Ömür Aras | Merve Genç
Source: Journal of Polymer Science (article, 2024-10-21)
Field: Materials Science — Biomaterials

A Novel Approach to the Development of Natural Resin‐Based Biopolymer in the Presence of a Reusable Catalyst: Characterization and Modeling of Material Properties ABSTRACT The rise in environmental and health concerns has led to increasing attention to nature‐derived materials. Natural resin (NR) is secreted by pine trees, and it is a great monomer source for synthesizing biopolymers. The objective of this study is to produce terpene rosin phenolic resin (TRPR) from NR, turpentine, and phenol by applying a novel polymerization technique. An environmentally friendly and reusable catalyst (Amberlyst15) was chosen instead of traditional ones. TRPR samples were chemically characterized using Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance spectroscopy (NMR), and gel permeation chromatography (GPC) analysis. The average molecular weight (Mw) of TRPR was detected as 560 g/mol. Artificial neural network (ANN) modeling was designed with three inputs (pressure, temperature, and terpene/NR ratio) and four outputs (reaction yield, acid value, saponification value, and softening point). The highest TRPR yield was obtained with a terpene/NR ratio of (1/2) at 80°C and under 3 atm. The lowest acid and saponification values were calculated as 90.54 and 100.11 mg KOH/g, respectively. The softening point of TRPR reached 80°C and it was suggested for use in the paper, ink, and adhesive industries. Biopolymer ; Characterization (materials science) ; Natural (archaeology) ; Catalysis ; Polymer science ; Materials science ; Chemistry ; Chemical engineering ; Organic chemistry ; Nanotechnology ; Composite material ; Polymer ; Engineering ; Geology biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Polymer composites and self-healing
Authors: Can Polat | Hasan Kurban | Mustafa Kurban
Source: Computational Materials Science (article, 2024-09-18)
Field: Materials Science — Materials Chemistry

QuantumShellNet: Ground-state eigenvalue prediction of materials using electronic shell structures and fermionic properties via convolutions Eigenvalues and eigenvectors ; Ground state ; Shell (structure) ; Materials science ; Electronic structure ; Statistical physics ; Condensed matter physics ; Physics ; Atomic physics ; Composite material ; Quantum mechanics Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Thermal properties of materials