jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jian Zhang | Haochun Zhang | Yuan Zhang | Xikui Ma | Weifeng Li | Gang Zhang
Source: physica status solidi (b) (article, 2024-06-02)
Field: Materials Science — Materials Chemistry

Phonon and Thermal Properties of Silicon Carbide: A Comparison of Empirical and Machine Learning Potentials Silicon carbide (SiC), as a third‐generation semiconductor material, has attracted significant research attention. Various empirical potentials and machine learning potentials have been developed, but there are few comparative studies on phonon and thermal properties. Herein, the Tersoff and Vashishta empirical potentials, as well as the Bayesian force field constructed by the FLARE framework using principled Gaussian process uncertainties (FLARE BFF), for a comparative study, are selected. The phonon dispersion relation, phonon density of states, Grüneisen constants, and the average phonon‐weighted Grüneisen constants are calculated using different potentials, and it is found that the FLARE BFF potential has the highest accuracy with respect to the first‐principles calculations. Furthermore, the thermal conductivity using molecular dynamics simulation with different potentials is calculated. The calculation results using the FLARE BFF potential closely match the experimental reports at high temperature, but the longest computing time is required. This study can facilitate the understanding of thermal properties of SiC. Silicon carbide ; Phonon ; Thermal ; Materials science ; Silicon ; Carbide ; Condensed matter physics ; Engineering physics ; Composite material ; Optoelectronics ; Thermodynamics ; Physics Thermal properties of materials ; Advancements in Semiconductor Devices and Circuit Design ; Semiconductor materials and interfaces
Authors: Yi Yang | Xu Zhang | Yiming Mo | Wenguan Wang
Source: Research Square (preprint, 2024-03-26)
Field: Materials Science — Materials Chemistry

Retrosynthesis prediction enhanced by in-silico reaction data augmentation <title>Abstract</title> Recent advances in machine learning (ML) have expedited retrosynthesis research by assisting chemists to design experiments more efficiently. However, all ML-based methods consume substantial amounts of paired training data (i.e., chemical reaction: pr<italic>oduct-reactant(s)</italic> pair), which is costly to obtain. Moreover, companies view reaction data as a valuable asset and restrict the accessibility to researchers. These issues prevent the creation of more powerful retrosynthesis models due to their data-driven nature. As a response, we exploit easy-to-access unpaired data (i.e., one component of <italic>product-reactant(s)</italic> pair) for generating in-silico paired data to facilitate model training. Specifically, we present RetroWISE, a self-boosting framework that employs a base model inferred from real paired data to perform <italic>in-silico</italic> reaction generation and augmentation using unpaired data, ultimately leading to a superior model. On three benchmark datasets, RetroWISE achieves the best overall performance against state-of-the-art models (e.g., +8.6% top-1 accuracy on the USPTO-50K test dataset). Moreover, it consistently improves the prediction accuracy of rare transformations. These results show that RetroWISE overcomes the training bottleneck by in-silico reactions, thereby paving the way toward more effective ML-based retrosynthesis models. Benchmark (surveying) ; Retrosynthetic analysis ; Computer science ; Boosting (machine learning) ; Bottleneck ; In silico ; Exploit ; Artificial intelligence ; Training set ; Machine learning ; Data mining ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Genomics and Phylogenetic Studies
Authors: Aslak Fellman | Jesper Byggmästar | Fredric Granberg | K. Nordlund | Flyura Djurabekova
Source: arXiv (Cornell University) (preprint, 2024-08-28)
Field: Materials Science — Materials Chemistry

Fast and accurate machine-learned interatomic potentials for large-scale simulations of Cu, Al and Ni Machine learning (ML) has become widely used in the development of interatomic potentials for molecular dynamics simulations. However, most ML potentials are still much slower than classical interatomic potentials and are usually trained with near equilibrium simulations in mind. In this work, we develop ML potentials for Cu, Al and Ni using the Gaussian approximation potential (GAP) method. Specifically, we create the low-dimensional tabulated versions (tabGAP) of the potentials, which allow for two orders of magnitude higher computational efficiency than the GAPs, enabling simulations of large multi-million atomic systems. The ML potentials are trained using diverse curated databases of structures and include fixed external repulsive potentials for short-range interactions. The potentials are extensively validated and used to simulate a wide range of fundamental materials properties, such as stacking faults and threshold displacement energies. Furthermore, we use the potentials to simulate single-crystal uniaxial compressive loading in different crystal orientations with both pristine simulation cells and cells containing pre-existing defects. Scale (ratio) ; Interatomic potential ; Computer science ; Statistical physics ; Materials science ; Physics ; Molecular dynamics ; Computational chemistry ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques
Authors: A. Sexton | Heikki Suhonen | Mathias K. Huss‐Hansen | Hanna Demchenko | Jakob Kjelstrup‐Hansen | Matthias Schwartzkopf | Matti Knaapila
Source: Scientific Reports (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Multi-scale correlation of impact-induced defects in carbon fiber composites using X-ray scattering and machine learning Impact-induced defects in carbon fiber-reinforced polymers (CFRPs)-spanning from nanometer to macroscopic length scales-can be monitored using an aggregate of X-ray-based methods, but this is impractical in typical field conditions. We report on a low-velocity impacted CFRP, which is mapped using small- and wide-angle X-ray scattering and X-ray computed tomography, and employ machine learning for correlating material parameterizations derived from these techniques. The observed 1 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>μ</mml:mi></mml:math> m to 1 mm-sized defects are parameterized in terms of relative density and fiber orientation indicative of fiber failures (kink bands), and the nanometer sized defects in terms of crystal size and unit cell frustration. The 30 to 300 nm defects are parameterized by a power-law scattering decay, differentiating fractal-like behaviors. We find three spatial domains experimentally and by K-means Clustering: Domains of severe damage (with a visual dent), intact domains (without visual or measurable defects) and a transition domain (defects measurable by X-rays). How the parameters are correlated and how they overlap between the domains are discussed. All parameters are able to point to the detrimental fiber breakage in the severe damage domain, and scattering decay also in the transition domain, for example. How individual parameters determined from one experimental technique can be predicted from that of another is also described. Composite material ; Scale (ratio) ; Scattering ; Correlation ; Materials science ; Carbon fibers ; Small-angle X-ray scattering ; Fiber ; Composite number ; Mathematics ; Physics ; Optics ; Geometry Graphite, nuclear technology, radiation studies ; Fiber-reinforced polymer composites ; Machine Learning in Materials Science
Authors: Tengfei Luo | Zhihao Xu | Wenjie Shang | Seongmin Kim | Eungkyu Lee
Source: Research Square (preprint, 2024-08-27)
Field: Materials Science — Materials Chemistry

QALO: Quantum Annealing-assisted Lattice Optimization Quantum annealing ; Simulated annealing ; Annealing (glass) ; Alloy ; Quantum ; Lattice (music) ; Materials science ; Computer science ; Ground state ; Quantum computer ; Algorithm ; Statistical physics ; Physics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; High Entropy Alloys Studies ; Chalcogenide Semiconductor Thin Films
Authors: Kevin Curtis | Samuel O. Odoh
Source: Journal of Computational Chemistry (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Machine Learning‐Corrected Simplified Time‐Dependent <scp>DFT</scp> for Systems With Inverted Single‐t‐o‐Triplet Gaps of Interest for Photocatalytic Water Splitting Hydrogen gas (H<sub>2</sub>) can be produced via entirely solar-driven photocatalytic water splitting (PWS). A promising set of organic materials for facilitating PWS are the so-called inverted singlet-triplet, INVEST, materials. Inversion of the singlet (S<sub>1</sub>) and triplet (T<sub>1</sub>) energies reduces the population of triplet states, which are otherwise destructive under photocatalytic conditions. Moreover, when INVEST materials possess dark S<sub>1</sub> states, the excited state lifetimes are maximized, facilitating energy transfer to split water. In the context of solar-driven processes, it is also desirable that these INVEST materials absorb near the solar maximum. Many aza-triangulenes possess the desired INVEST property, making it beneficial to describe an approach for systematically and efficiently predicting the INVEST property as well as properties that make for efficient photocatalytic water splitting, while exploring the large chemical space of the aza-triangulenes. Here, we utilize machine learning to generate post hoc corrections to simplified Tamm-Dancoff approximation density functional theory (sTDA-DFT) for singlet and triplet excitation energies that are within 28-50 meV of second-order algebraic diagrammatic construction, ADC(2), as well as the singlet-to-triplet, ΔE<sub>S1T1</sub>, gaps of PWS systems. Our Δ-ML model is able to recall 85% of the systems identified by ADC(2) as candidates for PWS. Further, with a modest database of ADC(2) excitation energies of 4025 aza-triangulenes, we identified 78 molecules suitable for entirely solar-driven PWS. Photocatalysis ; Water splitting ; Hydrogen ; Chemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Chemical Physics Studies
Authors: Sayed Hoseini | Gaoyuan Zhang | Dominik Polke | Alvin Immanuel Surjana | Lasse Wagner | Christian Schmitz | Christoph Quix
Source:  (article, 2024-05-12)
Field: Materials Science — Materials Chemistry

Coatings Intelligence: Data-driven Automation for Chemistry 4.0 Organic coatings and paints play a vital role in safeguarding surfaces, but improving their properties typically requires a large number of carefully planned experiments. High-throughput screening (HTS), employing industrial robots, allows automated preparation and testing, thus speeding up research and development on materials. Challenges remain in hardware interfaces, data management, and plant control system adaptation. Machine Learning (ML) is increasingly applied to various applications in chemistry for experiment suggestions and analysis; thereby paving the way for a higher degree of automation in HTS. This work presents an integrated system composed of an automation platform to perform HTS, a characterization module to evaluate the material, and a DL model to accelerate the development of new coatings by evaluating their scratch resistance using sensor data independent of images. The model is trained on data managed by a data lake. This integration showcases the interplay between chemistry, automation, and data science as a basis for smart manufacturing. Automation ; Computer science ; Data science ; Chemistry ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Cheng Li | Qingkai Liang | Yumei Zhou | Dezhen Xue
Source: Materials Genome Engineering Advances (article, 2024-12-11)
Field: Materials Science — Materials Chemistry

A knowledge‐based materials descriptor for compositional dependence of phase transformation in NiTi shape memory alloys Abstract This study presents ∆τ, a novel descriptor that captures the compositional dependence of phase transformation temperature (Ap) in NiTi‐based shape memory alloys (SMAs). Designed to address the complexity of multicomponent SMAs, ∆τ was integrated into symbolic regression (SR) and kernel ridge regression (KRR) models, yielding substantial improvements in predicting key functional properties: transformation temperature, enthalpy, and thermal hysteresis. Using the KRR model with ∆τ, we explored the NiTiHfZrCu compositional space, identifying six promising alloys with high Ap (&gt;250°C), large enthalpy (&gt;27 J/g), and low thermal hysteresis. Experimental validation confirmed the model's accuracy with the alloys showing high‐temperature transformation behavior and low hysteresis, suitable for high‐performance applications in aerospace and nuclear industries. These findings underscore the power of domain‐informed descriptors like ∆τ in enhancing machine learning‐driven materials design. Nickel titanium ; Shape-memory alloy ; Transformation (genetics) ; Materials science ; Phase (matter) ; R-Phase ; Metallurgy ; Martensite ; Chemistry ; Microstructure Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing
Authors: Fernando Arrais Romero Dias Lima | Marcellus G.F. de Moraes | Martha A. Grover | Evaristo C. Biscaia | Argimiro R. Secchi | Maurício B. de Souza
Source: IFAC-PapersOnLine (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Controlling Paracetamol Unseeded Batch Crystallization with NMPC and Inverse Model In this work, two model-based controllers were developed, one based on a nonlinear model-based controller (NMPC) using a population balance model (PBM) and another using a machine learning approach based on a neural network inverse model-based controller (NNIMC). The performance of the two model-based controllers was compared for different scenarios to obtain optimal temperature policies for controlling the mass yield and crystals’ size for the unseeded batch cooling crystallization of paracetamol. The results show that both strategies are effective for crystallization control, presenting comparable results for the controlled variables in different scenarios. The controllers were also tested by applying random noise in the state variables. In these cases, the NNIMC presented advantages in having a lower computational cost for optimum control action calculations and less control effort regarding the manipulated variable’s variation to reach values for the control variables at the end of the batch close to the NMPC and the setpoints. Crystallization ; Inverse ; Process engineering ; Materials science ; Chemistry ; Chemical engineering ; Environmental science ; Mathematics ; Engineering Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Process Optimization and Integration
Authors: Florian Kiehas | Martin Reiter | Juan Pablo Torres | Michael Jerabek | Zoltán Major
Source: Frontiers in Materials (article, 2024-01-25)
Field: Materials Science — Materials Chemistry

Polyolefin ductile-brittle transition temperature predictions by machine learning Polymers show a transition from ductile-to brittle fracture behavior at decreasing temperatures. Consequently, the material toughness has to be determined across wide temperature ranges in order to determine the Ductile-Brittle Transition Temperature This usually necessitates multiple impact experiments. We present a machine-learning methodology for the prediction of DBTTs from single Instrumented Puncture Tests Our dataset consists of 7,587 IPTs that comprise 181 Polyethylene and Polypropylene compounds. Based on a combination of feature engineering and Principal Component Analysis, relevant information of instrumentation signals is extracted. The transformed data is explored by unsupervised machine learning algorithms and is used as input for Random Forest Regressors to predict DBTTs. The proposed methodology allows for fast screening of new materials. Additionally, it offers estimations of DBTTs without thermal specimen conditioning. Considering only IPTs tested at room temperature, predictions on the test set hold an average error of 5.3°C when compared to the experimentally determined DBTTs. Polyolefin ; Brittleness ; Materials science ; Transition temperature ; Composite material ; Condensed matter physics ; Physics Machine Learning in Materials Science ; Injection Molding Process and Properties ; Polymer crystallization and properties
Authors: Wejden Gazehi | Rania Loukil | Mongi Besbes
Source:  (article, 2024-12-11)
Field: Chemistry — Analytical Chemistry

Enhanced Nanoparticle Classification Through Optimized Artificial Neural Networks This study investigates the effectiveness of Deep Learning techniques in classifying nanoparticles, with a par-ticular focus on how the depth of neural networks influences model performance. Artificial Neural Networks (ANN) were utilized in simulations, exploring configurations ranging from a single hidden layer to four hidden layers to identify the optimal architecture for nanoparticle classification using MATLAB- NN Toolbox. Evaluation metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), the Correlation Coefficient (R), and the Coefficient of Determination <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(R^{2})$</tex>. Notably, the minimum value of the MSE achieved was <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5.6814 \times 10^{-13}$</tex> with the Bayesian Regularization (BR) technique, while the maximum values of Rand <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> were 0.9989 and 0.9977, respectively, with the Levenberg-Marquardt (LM) technique. The results offer insights into model robustness and contribute to selecting an optimal algorithm for analyzing polymeric nanoparticles. This study highlights the potential of ANN to advance precision in nanomaterial classification tasks, supporting further development in this field. Artificial neural network ; Nanoparticle ; Computer science ; Artificial intelligence ; Nanotechnology ; Materials science Spectroscopy and Chemometric Analyses ; Mineral Processing and Grinding ; Digital Imaging for Blood Diseases
Authors: Changsheng Zhu | Shuo Liu | Zihao Gao | Lijun Wang | Jintao Miao
Source: Materials Today Communications (article, 2024-07-31)
Field: Materials Science — Materials Chemistry

Spatiotemporal evolution of grain microstructure: A CNN perspective Materials science ; Microstructure ; Perspective (graphical) ; Grain size ; Grain boundary ; Metallurgy ; Artificial intelligence ; Computer science Solidification and crystal growth phenomena ; Theoretical and Computational Physics ; Material Dynamics and Properties
Authors: J. Theocharis | Panagiotis Krokidas | Vassilis Gkatsis | George Giannakopoulos
Source:  (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Multi-fidelity Bayesian Optimization for Efficiently Sampling the Design Space of Functionalized Nanoporous Materials The development of materials with tailored properties is often viewed as the holy grail of chemistry, due to the rather complex structure-property correlations. Artificial Intelligence (AI) and Machine Learning (ML) predictive models have been introduced in the field, as means of unveiling intricate said correlations. However, the application of these models in the physical sciences is hindered by the high cost and substantial time required to generate the necessary data for training AI algorithms. Given this backdrop of data scarcity, methods that efficiently sample and explore an unknown material's design space can significantly reduce laboratory costs and time. Bayesian optimization ; Nanoporous ; Fidelity ; Bayesian probability ; Computer science ; High fidelity ; Sampling (signal processing) ; Space (punctuation) ; Materials science ; Artificial intelligence ; Nanotechnology ; Engineering ; Telecommunications ; Electrical engineering Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science
Authors: Efstratios Nikidis | N. Kyriakopoulos | R. Tohid | Kyriakos Kachrimanis | Joseph Kioseoglou
Source: Nanoscale (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Harnessing machine learning for efficient large-scale interatomic potential for sildenafil and pharmaceuticals containing H, C, N, O, and S In this study a cutting-edge approach to producing accurate and computationally efficient interatomic potentials using machine learning algorithms is presented. Specifically, the study focuses on the application of Allegro, a novel machine learning algorithm, running on high-performance GPUs for training potentials. The choice of training parameters plays a pivotal role in the quality of the potential functions. To enable this methodology, the "Solvated Protein Fragments" dataset, containing nearly 2.7 million Density Functional Theory (DFT) calculations for many-body intermolecular interactions involving protein fragments and water molecules, encompassing H, C, N, O, and S elements, is considered as the training dataset. The project optimizes computational efficiency by reducing the initial dataset size according to the intended application. To assess the efficacy of the approach, the sildenafil citrate, iso-sildenafil, aspirin, ibuprofen, mebendazole and urea, representing all five relevant elements, serve as the test bed. The results of the Allegro-trained potentials demonstrate outstanding performance, benefiting from the combination of an appropriate training dataset and parameter selection. This notably enhanced computational efficiency when compared to the computationally intensive DFT method aided by GPU acceleration. Validation of the produced interatomic potentials is achieved through Allegro's own evaluation mechanism, yielding exceptional accuracy. Further verification is carried out through LAMMPS molecular dynamics simulations. Structural optimization by energy minimization and NPT Molecular Dynamics simulations are performed for each potential, assessing relaxation processes and energy reduction. Additional structures, including urea, ammonia, uracil, oxalic acid, and acetic acid, are tested, highlighting the potential's versatility in describing systems containing the aforementioned elements. Visualization of the results confirms the scientific accuracy of each structure's relaxation. The findings of this study demonstrate strong scaling and the potential for applications in pharmaceutical research, allowing the exploration of larger molecular structures not previously amenable to computational analysis at this level of accuracy The success of the machine learning approach underscores its potential to revolutionize computational solid-state physics. Sildenafil ; Interatomic potential ; Scale (ratio) ; Chemistry ; Computer science ; Materials science ; Computational chemistry ; Physics ; Molecular dynamics ; Medicine ; Quantum mechanics ; Internal medicine Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Madhavi Sridhar | D Mahesh | N. V. Raju | Snigdha Sen | Jobish Johns
Source: Journal of the Indian Academy of Wood Science (article, 2024-11-30)
Field: Materials Science — Polymers and Plastics

Analysing and predicting dielectric properties of bamboo fiber filled polypropylene composites using artificial neural network Composite material ; Bamboo ; Materials science ; Polypropylene ; Artificial neural network ; Dielectric ; Fiber ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Dielectric materials and actuators ; Textile materials and evaluations