jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sajjad Karimi | Jianyong Yu
Source: Polymer Composites (article, 2024-11-06)
Field: Materials Science — Polymers and Plastics

Utilizing a combination of experimental and machine learning methods to predict and correlate between accelerated and natural aging of <scp>CFRP</scp>/<scp>AL</scp> adhesive joints under hygrothermal conditions Abstract This study investigates how carbon fiber reinforced polymer (CFRP)‐to‐aluminum adhesive joints behave under accelerated aging conditions with hygrothermal exposure and compares these findings against naturally aged samples to evaluate material reliability in challenging environments. The CFRP‐to‐aluminum adhesive joints were manufactured and subjected to natural aging for durations ranging from 1 to 3 years with 6‐month intervals, as well as accelerated aging (hygrothermal) for periods ranging from 100 to 1200 h, with intervals of 50 h. Subsequently, the mechanical properties of the joints were evaluated using a three‐point bending test. To forecast natural aging times from accelerated aging data, five machine learning models were utilized: artificial neural network, support vector regression, linear regression, polynomial regression, and random forest regression. Hygrothermal aging significantly degraded the matrix, causing a shift in failure modes from cohesive to mixed types (cohesive, adhesive, and fiber tear failures), leading to a notable decline in bending strength. The study observed a 23.13% strength reduction in samples aged naturally for 3 years and a 24.33% decrease in those subjected to 1000 h of accelerated aging. The random forest regressor demonstrated superior accuracy in predicting natural aging times across different accelerated aging periods. Through the application of machine learning models, this study introduces a novel approach to forecast natural aging durations using data from accelerated aging experiments. This method shows potential for optimizing joints and composite structures, ultimately improving their durability and minimizing the likelihood of failures during operational use. Highlights Studied hygrothermal effects on accelerated aging of carbon fiber reinforced polymer/Aluminum (AL) adhesive joints. Noted strength reduction from hygrothermal aging. Used five machine learning models; random forest regression had the highest accuracy. Analyzed correlation between natural and accelerated aging of dissimilar adhesive joints. Materials science ; Composite material ; Adhesive ; Structural engineering ; Engineering ; Layer (electronics) Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Polymer crystallization and properties
Authors: Debsindhu Bhowmik | Pei Zhang | Zachary Fox | Stephan Irle | John Gounley
Source: Patterns (article, 2024-03-14)
Field: Materials Science — Materials Chemistry

Enhancing molecular design efficiency: Uniting language models and generative networks with genetic algorithms This study examines the effectiveness of generative models in drug discovery, material science, and polymer science, aiming to overcome constraints associated with traditional inverse design methods relying on heuristic rules. Generative models generate synthetic data resembling real data, enabling deep learning model training without extensive labeled datasets. They prove valuable in creating virtual libraries of molecules for material science and facilitating drug discovery by generating molecules with specific properties. While generative adversarial networks (GANs) are explored for these purposes, mode collapse restricts their efficacy, limiting novel structure variability. To address this, we introduce a masked language model (LM) inspired by natural language processing. Although LMs alone can have inherent limitations, we propose a hybrid architecture combining LMs and GANs to efficiently generate new molecules, demonstrating superior performance over standalone masked LMs, particularly for smaller population sizes. This hybrid LM-GAN architecture enhances efficiency in optimizing properties and generating novel samples. Generative grammar ; Computer science ; Artificial intelligence ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Kristof M. Bal | A. Collas
Source: Crystal Growth & Design (article, 2024-04-15)
Field: Chemistry — Physical and Theoretical Chemistry

Are Elastic Properties of Molecular Crystals within Reach of Density Functional Theory? Accuracy, Robustness, and Reproducibility of Current Approaches Solid form selection and design of crystalline small molecule active pharmaceutical ingredients (APIs) would benefit from computational prediction and rationalization of the mechanical properties. Before such practical applications can be considered, the robustness and reproducibility of the computed properties with respect to the chosen level of theory must be understood. In this work, elastic constants of eight molecular crystals, with an emphasis on APIs, have been calculated using dispersion-corrected density functional theory (DFT). The different DFT methods considered do not, in general, consistently predict the absolute magnitudes of the elastic moduli, which disagree by over 50% for some crystals. Relative properties such as elastic anisotropy are more robust, mostly consistent between models, and in qualitative agreement with experiment. Calculated anisotropies could also be rationalized in terms of the structural features of the crystal. Overall, this work reveals that DFT-computed elastic properties may not offer a ground truth in absolute terms. Future applications of DFT in the context of high-throughput material screening or training of machine learning models will therefore require judicious selection of target properties and evaluation metrics. Only after methodological limitations are properly identified can more in-depth investigations be undertaken to assess the feasibility of applying DFT in pharmaceutical development. Density functional theory ; Robustness (evolution) ; Anisotropy ; Statistical physics ; Materials science ; Elastic modulus ; Computer science ; Moduli ; Context (archaeology) ; Thermodynamics ; Computational chemistry ; Chemistry ; Physics ; Optics ; Quantum mechanics Crystallography and molecular interactions ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Mesut Ersin Sönmez | Kadir Sabancı | Nevzat Aydın
Source: European Food Research and Technology (article, 2024-02-15)
Field: Chemistry — Analytical Chemistry

Convolutional neural network-support vector machine-based approach for identification of wheat hybrids Abstract Selecting wheat hybrids is vital for enhancing crop yield, adapting to changing climates, and ensuring food security. These hybrids align with market demands and sustainable farming practices, contributing to efficient crop management. Traditional methods for wheat hybrid selection, such as molecular techniques, are costly and time-consuming, and are prone to human error. However, advancements in artificial intelligence and machine learning offer non-destructive, objective, and more efficient solutions. This study is explored the classification of wheat varieties and hybrids using two deep learning models, MobileNetv2 and GoogleNet. These models are achieved impressive classification accuracy, with MobileNetv2 reaching 99.26% and GoogleNet achieving 97.41%. In the second scenario, the deep features obtained from these models are classified with Support Vector Machine (SVM). In the classification made with the MobileNetv2-SVM hybrid model, an accuracy of 99.91% is achieved. This study is provided rapid and accurate wheat variety and hybrid identification method, as well as contributing to breeding programs and crop management. Convolutional neural network ; Support vector machine ; Identification (biology) ; Hybrid ; Artificial neural network ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Kyle Bystrom | Stefano Falletta | Boris Kozinsky
Source: Journal of Chemical Theory and Computation (article, 2024-08-23)
Field: Materials Science — Materials Chemistry

Training Machine-Learned Density Functionals on Band Gaps The systematic underestimation of band gaps is one of the most fundamental challenges in semilocal density functional theory (DFT). In addition to hindering the application of DFT to predicting electronic properties, the band gap problem is intimately related to self-interaction and delocalization errors, which make the study of charge transfer mechanisms with DFT difficult. To expand the range of available tools for addressing the band gap problem, we design an approach for machine learning density functionals based on Gaussian processes to explicitly fit single-particle energy levels. We also introduce nonlocal features of the density matrix that are expressive enough to fit these single-particle levels. Combining these developments, we train a machine-learned functional for the exact exchange energy that predicts molecular energy gaps and reaction energies of a wide range of molecules in excellent agreement with reference hybrid DFT calculations. In addition, while being trained solely on molecular data, our model predicts reasonable formation energies of polarons in solids, showcasing its transferability and robustness. We discuss how this approach can be generalized to full exchange-correlation functionals, thus paving the way to the design of state-of-the-art functionals for the prediction of electronic properties of molecules and materials. Density functional theory ; Hybrid functional ; Computer science ; Band gap ; Gaussian ; Range (aeronautics) ; Delocalized electron ; Robustness (evolution) ; Transferability ; Statistical physics ; Polaron ; Orbital-free density functional theory ; Physics ; Machine learning ; Materials science ; Quantum mechanics ; Chemistry ; Electron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Haoyu Dong | Shuang Lin | Guoqiang Fang | Yiran Liu | Huachuang Li | Li Wang
Source: Sensors and Actuators B Chemical (article, 2024-01-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Faceted gold nanocrystal film over nanosphere for sensitive SERS distinction of fungicides by deep learning analysis Nanocrystal ; Materials science ; Facet (psychology) ; Nanotechnology ; Reproducibility ; Substrate (aquarium) ; Chemistry ; Chromatography Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Jesi Lee | Dean J. Tantillo | Lee‐Ping Wang | Oliver Fiehn
Source: Journal of Chemical Information and Modeling (article, 2024-09-27)
Field: Chemistry — Spectroscopy

Predicting Collision-Induced-Dissociation Tandem Mass Spectra (CID-MS/MS) Using Ab Initio Molecular Dynamics Compound identification is at the center of metabolomics, usually by comparing experimental mass spectra against library spectra. However, most compounds are not commercially available to generate library spectra. Hence, for such compounds, MS/MS spectra need to be predicted. Machine learning and heuristic models have largely failed except for lipids. Here, quantum chemistry software can be used to predict mass spectra. However, quantum chemistry predictions for collision induced dissociation (CID) mass spectra in LC-MS/MS are rare. We present the CIDMD (Collision-Induced Dissociation via Molecular Dynamics) framework to model CID-based MS/MS spectra. It uses first-principles molecular dynamics (MD) to simulate the physical process of molecular collisions in CID tandem mass spectrometry. First, molecular ions are constructed at specific protonation sites. Using density functional theory, these protonated ions are targeted by argon collider gas atoms at user-specified velocities. Subsequent bond breakages are simulated over time for at least 1,000 fs. Each simulation is repeated multiple times from various collisional directions. Fragmentations are accumulated over those repeated collisions to generate CIDMD in silico mass spectra. Twelve small metabolites (<205 Da) were selected to test the accuracy of this framework in comparison to experimental MS/MS spectra. When testing different protomers, collider velocities, number of simulations, simulation time and impact factor b cutoffs, we yielded 261 predicted mass spectra. These in silico spectra resulted in entropy similarity scores of an average 624 ± 189 for all 261 spectra compared to their corresponding experimental spectra, which improved to 828 ± 77 when using optimal parameters of the most probable protomers for 12 molecules. With increasing molecular mass, higher velocities achieved better results. Similarly, different protomers showed large differences in fragmentation; hence, with increasing numbers of protomers and tautomers, the average CIDMD prediction accuracy decreased. Mechanistic details showed that specific fragment ions can be produced from different protomers via multiple fragmentation pathways. We propose that CIDMD is a suitable tool to predict mass spectra of small metabolites like produced by the gut microbiome. Collision-induced dissociation ; Dissociation (chemistry) ; Tandem mass spectrometry ; Ab initio ; Chemistry ; Tandem ; Mass spectrum ; Collision ; Spectral line ; Molecular dynamics ; Fragmentation (computing) ; Computational chemistry ; Mass spectrometry ; Analytical Chemistry (journal) ; Materials science ; Physical chemistry ; Chromatography ; Physics ; Computer science ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Francesco Zonta | Sergio Pantano
Source: Mechanobiology in Medicine (article, 2024-07-26)
Field: Materials Science — Materials Chemistry

From sequence to mechanobiology? Promises and challenges for AlphaFold 3 Interactions between macromolecules orchestrate many mechanobiology processes. However, progress in the field has often been hindered by the monetary and time costs of obtaining reliable experimental structures. In recent years, deep-learning methods, such as AlphaFold, have democratized access to high-quality predictions of the structural properties of proteins and other macromolecules. The newest implementation, AlphaFold 3, significantly expands the applications of its predecessor, AlphaFold 2, by incorporating reliable models for small molecules and nucleic acids and enhancing the prediction of macromolecular complexes. While several limitations still exist, the continuous improvement of machine learning methods like AlphaFold is producing a significant revolution in the field. The possibility of easily accessing structural predictions of biomolecular complexes may create substantial impacts in mechanobiology. Indeed, structural studies are at the basis of several applications in the field, such as drug discovery for mechanosensing proteins, development of mechanotherapy, understanding the mechanotransduction mechanisms and the mechanistic basis of diseases, or designing biomaterials for tissue engineering. Mechanobiology ; Computer science ; Field (mathematics) ; Biochemical engineering ; Nanotechnology ; Computational biology ; Biology ; Engineering ; Materials science Enzyme Structure and Function ; Protein Structure and Dynamics ; RNA and protein synthesis mechanisms
Authors: Jacob J. Wardzala | Daniel S. King | Lawal Adewale Ogunfowora | Brett M. Savoie | Laura Gagliardi
Source: ACS Central Science (article, 2024-03-27)
Field: Materials Science — Materials Chemistry

Organic Reactivity Made Easy and Accurate with Automated Multireference Calculations In organic reactivity studies, quantum chemical calculations play a pivotal role as the foundation of understanding and machine learning model development. While prevalent black-box methods like density functional theory (DFT) and coupled-cluster theory (e.g., CCSD(T)) have significantly advanced our understanding of chemical reactivity, they frequently fall short in describing multiconfigurational transition states and intermediates. Achieving a more accurate description necessitates the use of multireference methods. However, these methods have not been used at scale due to their often-faulty predictions without expert input. Here, we overcome this deficiency with automated multiconfigurational pair-density functional theory (MC-PDFT) calculations. We apply this method to 908 automatically generated organic reactions. We find 68% of these reactions present significant multiconfigurational character in which the automated multiconfigurational approach often provides a more accurate and/or efficient description than DFT and CCSD(T). This work presents the first high-throughput application of automated multiconfigurational methods to reactivity, enabled by automated active space selection algorithms and the computation of electronic correlation with MC-PDFT on-top functionals. This approach can be used in a black-box fashion, avoiding significant active space inconsistency error in both single- and multireference cases and providing accurate multiconfigurational descriptions when needed. Chemical space ; Coupled cluster ; Reactivity (psychology) ; Density functional theory ; Computer science ; Computational chemistry ; Space (punctuation) ; Statistical physics ; Chemistry ; Physics ; Quantum mechanics ; Drug discovery ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: F. Himmet | Gökhan Sürücü | S.B. Lişesivdin | Özge Sürücü | Gözde Altuntaş | Bülent Bostan | A. Gençer
Source: International Journal of Hydrogen Energy (article, 2024-06-30)
Field: Materials Science — Materials Chemistry

Innovative 2D materials for efficient photocatalysis: A comparative study for WSi2N4, WGe2N4, and their janus counterpart WSiGeN4 monolayers Janus ; Photocatalysis ; Monolayer ; Nanotechnology ; Materials science ; Chemistry ; Catalysis ; Organic chemistry 2D Materials and Applications ; MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques
Authors: Zhanpeng Sun | Xiang Sun | Zijun Qi | Qijun Wang | Rui Li | Lijie Li | Gai Wu | Wei Shen | Sheng Liu
Source: Diamond and Related Materials (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

Investigating thermal transport across the AlN/diamond interface via the machine learning potential Diamond ; Interface (matter) ; Thermal ; Materials science ; Nanotechnology ; Computer science ; Optoelectronics ; Engineering physics ; Physics ; Composite material ; Thermodynamics Thermal properties of materials ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Dat-Nguyen Vo | Meng Qi | Chang‐Ha Lee | Xunyuan Yin
Source: Applied Energy (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Advanced integration strategies and machine learning-based superstructure optimization for Power-to-Methanol Superstructure ; Power (physics) ; Methanol ; Artificial intelligence ; Computer science ; Engineering ; Machine learning ; Chemistry ; Structural engineering ; Physics Machine Learning in Materials Science ; Catalysts for Methane Reforming ; Hybrid Renewable Energy Systems
Authors: Haowei Zhou | Li Xiao | Zhaochen Xi | Man Li | Jieyan Zhang | Chao Li | Zhongming Liu | Moustafa A. Darwish | Tao Zhou | Di Zhou
Source: Materials Today Physics (article, 2024-12-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning-driven interface engineering for enhanced microwave absorption in MXene films Materials science ; Interface (matter) ; Absorption (acoustics) ; Microwave ; Molecular engineering ; Engineering physics ; Optoelectronics ; Nanotechnology ; Chemical engineering ; Composite material ; Computer science ; Wetting ; Engineering ; Telecommunications Electromagnetic wave absorption materials ; Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Bohayra Mortazavi | Masoud Shahrokhi | Fazel Shojaei | Timon Rabczuk | Xiaoying Zhuang
Source: Computational Materials Today (article, 2024-12-18)
Field: Materials Science — Materials Chemistry

A machine learning-assisted exploration of the structural stability, electronic, optical, heat conduction and mechanical properties of C3N4 graphitic carbon nitride monolayers Among all members of the extensive family of two-dimensional (2D) materials, graphitic carbon nitrides of the C 3 N 4 stand out as one of the most successful nanomembranes, owing to their exceptional electro-optical and chemical properties, accompanied by remarkable achievements in their large scale synthesis. Nevertheless, the complex corrugated nature of their most stable structures poses a challenge in the accurate evaluation of their electronic, optical, mechanical and thermal properties from a theoretical point of view. To address the aforementioned challenge, we herein employed a combination of the machine learning interatomic potentials (MLIPs) and density functional theory (DFT) calculations. The MLIPs facilitated the detection of dynamically stable configurations and subsequently allowed us to accurately evaluate the lattice thermal conductivity and mechanical properties of four different C 3 N 4 lattices, one of which is theoretically predicted in this work. Using the HSE06-based DFT calculations, the electronic band structure, optical properties and photocatalytic potential of the considered nanomembranes were also investigated. Thanks to the robustness of the proposed combined MLIP-DFT approach, this study not only presents the first comprehensive understanding of the stability, thermal conductivity, mechanical strength, electronic and optical properties of the C 3 N 4 nanosheets, but also highlights the crucial influence of the structural corrugations on the resulting theoretical predictions for the nanoporous 2D material properties. Graphitic carbon nitride ; Materials science ; Monolayer ; Carbon nitride ; Nitride ; Thermal conduction ; Stability (learning theory) ; Composite material ; Nanotechnology ; Computer science ; Chemistry ; Layer (electronics) ; Machine learning ; Organic chemistry Graphene research and applications ; 2D Materials and Applications ; Thermal properties of materials