jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tingpeng Tao | Shu Li | D. Chen | Shuai Li | Dong-Rong Liu | Xin Liu | Minghua Chen
Source: Modelling and Simulation in Materials Science and Engineering (article, 2024-01-09)
Field: Materials Science — Materials Chemistry

Structural descriptors evaluation for MoTa mechanical properties prediction with machine learning Abstract Considering all possible crystal structures is essential in computer simulations of alloy properties, but using density functional theory (DFT) is computationally impractical. To address this, four structural descriptors were evaluated using machine learning (ML) models to predict formation energy, elasticity and hardness of MoTa alloys. A total of 612 configurations were generated by the Clusters Approach to Statistical Mechanics software and their corresponding material properties were calculated by DFT. As input features of ML models, the CORR and SOAP performed best ( R 2 &gt; 0.90, some up to 0.99), followed by Atomic-centred Symmetry Functions, while Coulomb matrix performed worst. Furthermore, SOAP shows excellent performance in extrapolation for larger supercell structures of the MoTa alloy system and transfer learning for the MoNb alloy system. Extrapolation ; Materials science ; Alloy ; Density functional theory ; Elasticity (physics) ; Supercell ; Computational science ; Statistical physics ; Machine learning ; Computer science ; Computational chemistry ; Composite material ; Mathematics ; Mathematical analysis ; Physics Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; MXene and MAX Phase Materials
Authors: Amir Omranpour | Pablo Montero de Hijes | Jörg Behler | Christoph Dellago
Source: arXiv (Cornell University) (preprint, 2024-01-31)
Field: Materials Science — Materials Chemistry

Perspective: Atomistic Simulations of Water and Aqueous Systems with Machine Learning Potentials As the most important solvent, water has been at the center of interest since the advent of computer simulations. While early molecular dynamics and Monte Carlo simulations had to make use of simple model potentials to describe the atomic interactions, accurate ab initio molecular dynamics simulations relying on the first-principles calculation of the energies and forces have opened the way to predictive simulations of aqueous systems. Still, these simulations are very demanding, which prevents the study of complex systems and their properties. Modern machine learning potentials (MLPs) have now reached a mature state, allowing to overcome these limitations by combining the high accuracy of electronic structure calculations with the efficiency of empirical force fields. In this Perspective we give a concise overview about the progress made in the simulation of water and aqueous systems employing MLPs, starting from early work on free molecules and clusters via bulk liquid water to electrolyte solutions and solid-liquid interfaces. Perspective (graphical) ; Aqueous solution ; Chemical physics ; Computer science ; Statistical physics ; Artificial intelligence ; Chemistry ; Physics ; Physical chemistry Machine Learning in Materials Science
Authors: Xingyu Wen | Wenshuai Bai | Dayu Zhang
Source: The Journal of Physical Chemistry B (article, 2024-05-08)
Field: Materials Science — Materials Chemistry

Machine-Learning-Based Kinetic-Free Hybrid Gray-Box Model to Predict Sulfuric-Acid-Catalyzed Esterification Reaction The sulfuric-acid-catalyzed esterification reaction of 2-butanol and propionic anhydride is a vital industrial process. In this paper, several experiments are conducted via reaction calorimetry to validate that both sulfuric and propionic acids have discernible catalytic effects on the reaction. This finding complicates the accurate description of the reaction kinetics through traditional methods. So this paper turns to a kinetic-free black-box model, Gaussian process regression (GPR) model via 24 experiments, as a more adaptable approach. Besides, the best GPR model is combined with traditional heat balance model to generate a hybrid gray-box model, which can give complete knowledge of reaction process. The hybrid gray-box model finally achieves maximum root-mean-square error (RMSE) of 0.0069 for conversion, 0.6535 for temperature, and 10.6087 for heat flow rate, underscoring its pretty good predictive ability. Sulfuric acid ; Catalysis ; Chemistry ; Kinetic energy ; Biological system ; Reaction rate ; Thermodynamics ; Computer science ; Process engineering ; Organic chemistry ; Engineering ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fault Detection and Control Systems
Authors: Arjun Neyyathala | Edvin Fako | Sandip De | Daria Gashnikova | Florian Maurer | Jan‐Dierk Grunwaldt | Stephan A. Schunk | Schirin Hanf
Source: Small Structures (article, 2024-11-28)
Field: Materials Science — Materials Chemistry

From Poison to Promotor: Spatially Isolated Metal Sites in Supported Rhodium Sulfides as Hydroformylation Catalysts The hydroformylation of alkenes is a cornerstone transformation for the chemical industry, central for both functionalizing and extending the carbon backbone of an alkene. In this study, silica‐supported crystalline rhodium sulfide nanoparticles are explored as heterogeneous catalysts in hydroformylation reactions, and it is found that Rh x S y systems ( x = 17, y = 15 or x = 2, y = 3 with 1 wt% Rh on SiO 2 ) greatly outperform metallic Rh nanoparticles. These systems prove to be exceptionally competitive when benchmarked against other cutting‐edge catalysts in terms of activity, with Rh 17 S 15 /SiO 2 being the superior catalyst candidate. By employing local environment descriptors, unsupervised machine learning and density functional theory, the structure‐performance relationships are examined. The results highlight that the presence of S in close proximity to the catalytic site unlocks the tunability of the surface catalytic properties. This allows for the substrate affinity to be modulated, in particular for Rh 17 S 15 , with adsorption energies rivalling those of pristine Rh and improved spatial resolution. Hydroformylation ; Rhodium ; Catalysis ; Metal ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Hydrodesulfurization Studies
Authors: Yibo Qiao | Zhouzhouzhou Mei | Yuening Luo | Yining Chen
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Ra-Unet: A New Deep Learning Segmentation Method for Semiconductor Wafer Defect Analysis on Fine-Grained Scanning Electron Microscope (Sem) Images Wafer ; Segmentation ; Semiconductor ; Semiconductor device fabrication ; Scanning electron microscope ; Computer science ; Deep learning ; Artificial intelligence ; Materials science ; Residual ; Pattern recognition (psychology) ; Nanotechnology ; Electronic engineering ; Optoelectronics ; Engineering ; Algorithm ; Composite material Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Takayoshi Yoshimura | Hiromoto Kato | Shunto Oikawa | Taichi Inagaki | Shigehito Asano | Tetsunori Sugawara | T. Miyao | Takamitsu Matsubara | Hiroharu Ajiro | Mikiya Fujii | Yu‐ya Ohnishi | Miho Hatanaka
Source: ChemRxiv (preprint, 2024-08-16)
Field: Materials Science — Materials Chemistry

CopDDB: a descriptor database for copolymers and its applications to machine learning Polymer informatics, which involves applying data-driven science to polymers, has attracted considerable research interest. However, developing adequate descriptors for polymers, particularly copolymers, to facilitate machine learning (ML) models with limited data sets remains a challenge. To address this issue, we computed sets of parameters, including reaction energies and activation barriers of elementary reactions in the early stage of radical polymerization, for 2500 radical–monomer pairs derived from 50 commercially available monomers and constructed an open database named “Copolymer Descriptor Database.” Furthermore, we built ML models using our descriptors as explanatory variables and physical properties such as the reactivity ratio, monomer conversion, monomer composition ratio, and molecular weight as objective variables. These models achieved high predictive accuracy, demonstrating the potential of our descriptors to advance the field of polymer informatics. Copolymer ; Monomer ; Polymer ; Informatics ; Computer science ; Database ; Polymerization ; Reactivity (psychology) ; Field (mathematics) ; Machine learning ; Materials science ; Chemistry ; Artificial intelligence ; Mathematics ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Fanke Zeng | Wanglai Cen
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning-accelerated structure prediction of PtSnO nanoclusters under working conditions Credible property exploration or prediction can not be achieved without well-established compositions and structures of catalysts under working conditions. We construct surrogate models <i>via</i> combination of machine learning (ML), genetic algorithm (GA) and <i>ab initio</i> thermodynamics (AITD) to accelerate global optimization of PtSn binary metal oxides, which are typically used for CO<sub>2</sub>-assisted propane dehydrogenation to propylene. This challenging case illustrates that the subtle oxidized states of PtSnO clusters can be predicted in a large chemical space including a wide range of reaction conditions. The oxidation patterns, phase diagrams and atomic charge distributions of the PtSnO clusters have been discussed. The Sn decorating mechanism to Pt in PtSnO has been explained. These results also indicate that the oxidation of PtSn clusters is more feasible under working conditions, and that previous understanding obtained only with a fully reduced PtSn alloy may be incomplete. Nanoclusters ; Materials science ; Computer science ; Machine learning ; Artificial intelligence ; Nanotechnology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electronic and Structural Properties of Oxides
Authors: Xinru Hua | Rasool Ahmad | José Blanchet | Wei Cai
Source: arXiv (Cornell University) (preprint, 2024-01-13)
Field: Materials Science — Materials Chemistry

Accelerated Sampling of Rare Events using a Neural Network Bias Potential In the field of computational physics and material science, the efficient sampling of rare events occurring at atomic scale is crucial. It aids in understanding mechanisms behind a wide range of important phenomena, including protein folding, conformal changes, chemical reactions and materials diffusion and deformation. Traditional simulation methods, such as Molecular Dynamics and Monte Carlo, often prove inefficient in capturing the timescale of these rare events by brute force. In this paper, we introduce a practical approach by combining the idea of importance sampling with deep neural networks (DNNs) that enhance the sampling of these rare events. In particular, we approximate the variance-free bias potential function with DNNs which is trained to maximize the probability of rare event transition under the importance potential function. This method is easily scalable to high-dimensional problems and provides robust statistical guarantees on the accuracy of the estimated probability of rare event transition. Furthermore, our algorithm can actively generate and learn from any successful samples, which is a novel improvement over existing methods. Using a 2D system as a test bed, we provide comparisons between results obtained from different training strategies, traditional Monte Carlo sampling and numerically solved optimal bias potential function under different temperatures. Our numerical results demonstrate the efficacy of the DNN-based importance sampling of rare events. Rare events ; Importance sampling ; Scalability ; Sampling (signal processing) ; Monte Carlo method ; Computer science ; Event (particle physics) ; Artificial neural network ; Statistical physics ; Variance (accounting) ; Function (biology) ; Algorithm ; Machine learning ; Statistics ; Mathematics ; Physics Machine Learning in Materials Science ; Nuclear physics research studies ; Markov Chains and Monte Carlo Methods
Authors: Christopher R. Taylor | Patrick W. V. Butler | Graeme M. Day
Source: ChemRxiv (preprint, 2024-05-20)
Field: Materials Science — Materials Chemistry

Predictive crystallography at scale: mapping, validating, and learning from 1,000 crystal energy landscapes Computational crystal structure prediction (CSP) is an increasingly powerful technique in materials discovery, due to its ability to reveal trends and permit insight across the possibility space of crystal structures of a candidate molecule, beyond simply the observed structure(s). In this work, we demonstrate the reliability and scalability of CSP methods for small, rigid organic molecules by performing in-depth CSP investigations for over 1000 such compounds, the largest survey of its kind to-date. We show that this highly-efficient force-field-based CSP approach is superbly predictive, locating 99.4\% of observed experimental structures, and ranking a large majority of these (74\%) as among the most stable possible structures (to within uncertainty due to thermal effects). We present two examples of insights such large predicted datasets can permit, examining the space group preferences of organic molecular crystals and rationalising empirical rules concerning the spontaneous resolution of chiral molecules. Finally, we exploit this large and diverse dataset for developing transferable machine-learned energy potentials for the organic solid state, training a neural network lattice energy correction to force field energies that offers substantial improvements to the already impressive energy rankings, and a MACE equivariant message-passing neural network for crystal structure reoptimisation. We conclude that the excellent performance and reliability of the CSP workflow enables the creation of very large datasets of broad utility and explanatory power in materials design. Crystal structure prediction ; Computer science ; Scalability ; Chemical space ; Exploit ; Force field (fiction) ; Artificial intelligence ; Energy landscape ; Machine learning ; Reliability (semiconductor) ; Crystal structure ; Chemistry ; Power (physics) ; Physics ; Crystallography Machine Learning in Materials Science ; Crystallography and molecular interactions ; Computational Drug Discovery Methods
Authors: JiaYan Wen | Haichao Hu | Ji An | Tian Han | J. Hu
Source: Journal of Superconductivity and Novel Magnetism (article, 2024-06-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Hard Magnetic Properties for Melt-Spun Nd2Fe14B and Nd2Fe14B/Fe3B Based on Machine Learning Materials science ; Machine learning ; Magnet ; Gradient boosting ; Decision tree ; Support vector machine ; Composite number ; Alloy ; Artificial intelligence ; Boosting (machine learning) ; Random forest ; Mechanical engineering ; Computer science ; Composite material ; Engineering Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Machine Learning in Materials Science
Authors: Gordana Marković | J. Ruzic | Miroslav Sokić | D. Milojkov | Vaso Manojlović
Source: Journal of Mining and Metallurgy Section B Metallurgy (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys Biocompatible titanium alloys possess a balanced set of improved mechanical properties and good biocompatibility, making them crucial materials in biomedical engineering. There is an increasing demand for these new alloys with superior properties. Furthermore, there is a need to understand the relationship between parameters and properties, and machine learning is being applied to make the whole process cheaper and more efficient. The aim of this study is to develop accurate machine learning models for predicting mechanical properties: modulus of elasticity, tensile strength, and yield strength, specifically using the Extra Trees Regressor model. Compared to the previous results, an improvement of the elastic modulus prediction model was observed after the inclusion of data on heat treatment parameters and Poisson?s ratio, as seen in the reduced MAE from 7.402 to 7.160 GPa. Models were built to predict the values of tensile strength and yield strength, where iron and tin were shown as most important features respectively, while the correlation coefficients for the test set were 0.893 and 0.868. Ultimate tensile strength ; Materials science ; Biocompatible material ; Composite material ; Elastic modulus ; Titanium ; Yield (engineering) ; Young's modulus ; Modulus ; Titanium alloy ; Metallurgy ; Biomedical engineering ; Alloy ; Engineering Titanium Alloys Microstructure and Properties ; Bone Tissue Engineering Materials ; Additive Manufacturing Materials and Processes
Authors: Denys Y. Kononenko | U. Rößler | Jeroen van den Brink | Oleg Janson
Source: Materials Today Physics (article, 2024-05-29)
Field: Materials Science — Materials Chemistry

Data-driven estimation of transfer integrals in undoped cuprates Undoped cuprates are an abundant class of magnetic insulators, in which the synergy of rich chemistry and sizable quantum fluctuations leads to a variety of magnetic behaviors. Understanding the magnetism of these materials is impossible without the knowledge of the underlying spin model. The typically dominant antiferromagnetic superexchanges can be accurately estimated from the respective electronic transfer integrals. Density functional theory calculations mapped onto an effective one-orbital model in the Wannier basis are an accurate, albeit computationally cumbersome method to estimate such transfer integrals in cuprates. We demonstrate that instead an Artificial Neural Network (ANN), trained on the results of high-throughput calculations, can predict the transfer integrals using the crystal structure as the only input. Descriptors of the ANN model encode the spatial configuration and the chemical composition of the local crystalline environment. A virtual toolbox employing our model can be readily employed to determine leading superexchange paths as well as for rapidly assessing the relevant spin model in yet unknown cuprates. Materials science ; Cuprate ; Transfer (computing) ; Condensed matter physics ; Doping ; Optoelectronics ; Computer science ; Physics Machine Learning in Materials Science ; Nuclear reactor physics and engineering ; Reservoir Engineering and Simulation Methods
Authors: Türkan Altun | Musa Kazım Acar | İlkay Hilal Gübbük
Source: ACS Omega (article, 2024-12-25)
Field: Chemistry — Organic Chemistry

Preparation and Characterization of the MMT@Fe<sub>3</sub>O<sub>4</sub>@Ag Nanocomposite for Catalytic Degradation of Methyl Yellow: Reaction Parameters and Mechanism Based on the Artificial Neuron Network The montmorillonite@iron oxide@silver (MMT@Fe<sub>3</sub>O<sub>4</sub>@Ag) nanocomposite, which is recyclable and exhibits high catalytic activity, was evaluated for the degradation of methyl yellow (MY), a carcinogenic azo dye. For this purpose, MMT@Fe<sub>3</sub>O<sub>4</sub> was first synthesized via the coprecipitation method and then Ag was doped to MMT@Fe<sub>3</sub>O<sub>4</sub> via the chemical reduction method. MMT, MMT@Fe<sub>3</sub>O<sub>4</sub>, and MMT@Fe<sub>3</sub>O<sub>4</sub>@Ag were characterized by various techniques including scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, vibrating sample magnetometer, and thermal gravimetric analysis. The results illustrated that MMT@Fe<sub>3</sub>O<sub>4</sub>@Ag exhibited a higher catalytic ability than MMT@Fe<sub>3</sub>O<sub>4</sub> toward decolorization of MY with a degradation efficiency of 100% in 10 min at pH 7.1 in the presence of sodium borohydride (NaBH<sub>4</sub>). Further, some parameters like the amount of NaBH<sub>4</sub>, initial dye concentration, and pH were also studied to determine optimum reaction conditions. MMT@Fe<sub>3</sub>O<sub>4</sub>@Ag could be easily separated and recycled from the reaction medium using an external magnet. Thus, the Ag-doped MMT@Fe<sub>3</sub>O<sub>4</sub> nanocomposite proved to have good catalytic activity, high MY degradation rate and reusability, and easy separation and simple synthesis method. These properties make it a promising catalyst for the treatment of wastewater containing organic pollutants. In addition, artificial neural network (ANN) simulation, which is a mathematical model with an artificial intelligence algorithm, was used for the degradation process. This model was evaluated with the parameters used in the experiment as the input and output layers. Last, the degradation of MY with the synthesized catalyst into different products was demonstrated by high-performance liquid chromatography (HPLC) analysis. Catalysis ; Nanocomposite ; Degradation (telecommunications) ; Characterization (materials science) ; Materials science ; Reaction mechanism ; Chemical engineering ; Mechanism (biology) ; Chemistry ; Nanotechnology ; Organic chemistry ; Computer science ; Physics ; Telecommunications ; Engineering Nanomaterials for catalytic reactions ; Water Quality Monitoring and Analysis ; Pigment Synthesis and Properties
Authors: Ariadni Boziki | Frédéric Ngono Mebenga | Philippe Fernandes | Alexandre Tkatchenko
Source: arXiv (Cornell University) (preprint, 2024-09-10)
Field: Materials Science — Materials Chemistry

A Journey with THeSeuSS: Automated Python Tool for Modeling IR and Raman Vibrational Spectra of Molecules and Solids Vibrational spectroscopy is an indispensable analytical tool that provides structural fingerprints for molecules, solids, and interfaces thereof. This study introduces THeSeuSS (THz Spectra Simulations Software) - an automated computational platform that efficiently simulates IR and Raman spectra for both periodic and non-periodic systems. Utilizing DFT and DFTB, THeSeuSS offers robust capabilities for detailed vibrational spectra simulations. Our extensive evaluations and benchmarks demonstrate that THeSeuSS accurately reproduces both previously calculated and experimental spectra, enabling precise comparisons and interpretations of vibrational features across various test cases, including H2O and glycine molecules in the gas phase, as well as solid ammonia and solid ibuprofen. Designed with a user-friendly interface and seamless integration with existing computational chemistry tools, THeSeuSS enhances the accessibility and applicability of advanced spectroscopic simulations, supporting research and development in chemical, pharmaceutical, and material sciences. Future updates aim to expand its methodological diversity by incorporating machine learning techniques to analyze larger and more complex systems, solidifying THeSeuSS's role as an essential tool in the computational chemist's arsenal. Python (programming language) ; Raman spectroscopy ; Molecule ; Spectral line ; Computer science ; Materials science ; Physical chemistry ; Physics ; Chemistry ; Programming language ; Optics ; Organic chemistry ; Quantum mechanics Solid-state spectroscopy and crystallography ; Inorganic Fluorides and Related Compounds ; Advanced NMR Techniques and Applications