jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kang Liao | Shiyu Dong | Zheng Cheng | Wei Li | Shuhua Li
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Combined fragment-based machine learning force field with classical force field and its application in the NMR calculations of macromolecules in solutions We have developed a combined fragment-based machine learning (ML) force field and molecular mechanics (MM) force field for simulating the structures of macromolecules in solutions, and then compute its NMR chemical shifts with the generalized energy-based fragmentation (GEBF) approach at the level of density functional theory (DFT). In this work, we first construct Gaussian approximation potential based on GEBF subsystems of macromolecules for MD simulations and then a GEBF-based neural network (GEBF-NN) with deep potential model for the studied macromolecule. Then, we develop a GEBF-NN/MM force field for macromolecules in solutions by combining the GEBF-NN force field for the solute molecule and ff14SB force field for solvent molecules. Using the GEBF-NN/MM MD simulation to generate snapshot structures of solute/solvent clusters, we then perform the NMR calculations with the GEBF approach at the DFT level to calculate NMR chemical shifts of the solute molecule. Taking a heptamer of oligopyridine-dicarboxamides in chloroform solution as an example, our results show that the GEBF-NN force field is quite accurate for this heptamer by comparing with the reference DFT results. For this heptamer in chloroform solution, both the GEBF-NN/MM and classical MD simulations could lead to helical structures from the same initial extended structure. The GEBF-DFT NMR results indicate that the GEBF-NN/MM force field could lead to more accurate NMR chemical shifts on hydrogen atoms by comparing with the experimental NMR results. Therefore, the GEBF-NN/MM force field could be employed for predicting more accurate dynamical behaviors than the classical force field for complex systems in solutions. Force field (fiction) ; Macromolecule ; Molecule ; Chemistry ; Molecular dynamics ; Computational chemistry ; Chemical shift ; Density functional theory ; Field (mathematics) ; Chloroform ; Solvent models ; Carbon-13 NMR ; Chemical physics ; Physical chemistry ; Physics ; Solvation ; Stereochemistry ; Organic chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Xiaobing Hu | Jiajun Zhao | Junjie Li | Zhijun Wang | Yi-Ming Chen | Jincheng Wang
Source: Advanced Theory and Simulations (article, 2022-01-24)
Field: Materials Science — Materials Chemistry

Global‐Oriented Strategy for Searching Ultrastrength Martensitic Stainless Steels Abstract An intelligent alloy design strategy integrating machine learning and adaptive sampling is proposed and successfully applied to an example of martensitic stainless steel. Short iterative experiments prove the feasibility of this strategy in quickly finding new alloys with hardness higher than that of the current best and reasonably augmenting training data to create a high‐confidence prediction model. A credible relationship between the composition and hardness is demonstrated by the proposed model and the most promising candidate in the designed space is identified. In contrast to the traditional approaches, this strategy can meet the goal of global quest as it offers the advantages of flexibility, reliability, and efficiency. The suggested strategy can be extended to guide the experimental design of other materials. Flexibility (engineering) ; Reliability (semiconductor) ; Computer science ; Martensite ; Key (lock) ; Machine learning ; Artificial intelligence ; Materials science ; Metallurgy ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Titanium Alloys Microstructure and Properties
Authors: Seyed Mohamad Moosavi | Balázs Álmos Novotny | Daniele Ongari | Elias Moubarak | Mehrdad Asgari | Özge Kadioglu | Charithea Charalambous | Andres Ortega Guerrero | Amir H. Farmahini | Lev Sarkisov | Susana García | Frank Noé | Berend Smit
Source: ChemRxiv (preprint, 2022-04-18)
Field: Chemistry — Inorganic Chemistry

A Data-Science Approach to Predict the Heat Capacity of Nanoporous Materials The heat capacity of a material is a fundamental property that is of significant practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine-learning approach to accurately predict the heat capacity of these materials, i.e., zeolites, metal-organic frameworks, and covalent-organic frameworks. The accuracy of our prediction is confirmed with novel experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials the heat requirement is reduced by as much as a factor of two using the correct heat capacity. Heat capacity ; Flue gas ; Work (physics) ; Process engineering ; Materials science ; Carbon fibers ; Sorbent ; Nanoporous ; Process (computing) ; Adsorption ; Computer science ; Thermodynamics ; Mechanical engineering ; Waste management ; Composite material ; Nanotechnology ; Chemistry ; Engineering ; Organic chemistry ; Physics Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Carbon Dioxide Capture Technologies
Authors: Reinis Ignatāns | Maxim Ziatdinov | Rama K. Vasudevan | Mani Valleti | Vasiliki Tileli | Sergei V. Kalinin
Source: Advanced Functional Materials (article, 2022-03-04)
Field: Materials Science — Materials Chemistry

Latent Mechanisms of Polarization Switching from In Situ Electron Microscopy Observations Abstract In situ scanning transmission electron microscopy enables observation of the domain dynamics in ferroelectric materials as a function of externally applied bias and temperature. The resultant data sets contain a wealth of information on polarization switching and phase transition mechanisms. However, identification of these mechanisms from observational data sets has remained a problem due to a large variety of possible configurations, many of which are degenerate. Here, an approach based on a combination of deep learning‐based semantic segmentation, rotationally invariant variational autoencoder (VAE), and non‐negative matrix factorization to enable learning of a latent space representation of the data with multiple real‐space rotationally equivalent variants mapped to the same latent space descriptors is introduced. By varying the size of training sub‐images in the VAE, the degree of complexity in the structural descriptors is tuned from simple domain wall detection to the identification of switching pathways. This yields a powerful tool for the exploration of the dynamic data in mesoscopic electron, scanning probe, optical, and chemical imaging. Moreover, this work adds to the growing body of knowledge of incorporating physical constraints into the machine and deep‐learning methods to improve learned descriptors of physical phenomena. Materials science ; Artificial intelligence ; Mesoscopic physics ; Pattern recognition (psychology) ; Biological system ; Computer science ; Autoencoder ; Polarization (electrochemistry) ; Artificial neural network ; Physics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Electronic and Structural Properties of Oxides
Authors: Shuo Yang | Chenxi Zhao | Jing Ren | Ke Zheng | Zhengzhong Shao | Shengjie Ling
Source: Nanoscale (article, 2022-01-01)
Field: Materials Science — Biomaterials

Acquiring structural and mechanical information of a fibrous network through deep learning Fibrous networks play an essential role in the structure and properties of a variety of biological and engineered materials, such as cytoskeletons, protein filament-based hydrogels, and entangled or crosslinked polymer chains. Therefore, insight into the structural features of these fibrous networks and their constituent filaments is critical for discovering the structure-property-function relationships of these material systems. In this paper, a fibrous network-deep learning system (FN-DLS) is established to extract fibrous network structure information from atomic force microscopy images. FN-DLS accurately assesses the structural and mechanical characteristics of fibrous networks, such as contour length, number of nodes, persistence length, mesh size and fractal dimension. As an open-source system, FN-DLS is expected to serve a vast community of scientists working on very diverse disciplines and pave the way for new approaches on the study of biological and synthetic polymer and filament networks found in current applied and fundamental sciences. Protein filament ; Materials science ; Self-healing hydrogels ; Fractal dimension ; Biological network ; Function (biology) ; Network structure ; Deep learning ; Dimension (graph theory) ; Polymer ; Nanotechnology ; Computer science ; Polymer science ; Biological system ; Artificial intelligence ; Fractal ; Composite material ; Theoretical computer science ; Bioinformatics ; Polymer chemistry ; Mathematics Supramolecular Self-Assembly in Materials ; Hydrogels: synthesis, properties, applications ; Cellular Mechanics and Interactions
Authors: Aloïs Castellano | François Bottin | J. Bouchet | Antoine Levitt | Gabriel Stoltz
Source: Physical review. B./Physical review. B (article, 2022-10-20)
Field: Materials Science — Materials Chemistry

<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi></mml:mrow></mml:math> <i>initio</i> canonical sampling based on variational inference Finite temperature calculations, based on ab initio molecular dynamics (AIMD) simulations, are a powerful tool able to predict material properties that cannot be deduced from ground state calculations. However, the high computational cost of AIMD limits its applicability for large or complex systems. To circumvent this limitation we introduce a method named machine learning assisted canonical sampling, which accelerates the sampling of the Born-Oppenheimer potential surface in the canonical ensemble. Based on a self-consistent variational procedure, the method iteratively trains a machine learning interatomic potential to generate configurations that approximate the canonical distribution of positions associated with the ab initio potential energy. By proving the reliability of the method on anharmonic systems, we show that the method is able to reproduce the results of AIMD with an ab initio accuracy at a fraction of its computational cost. Ab initio ; Algorithm ; Anharmonicity ; Computer science ; Statistical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Crystallography and molecular interactions ; Protein Structure and Dynamics
Authors: Pankaj Kumar | Vinit Sharma | Sharmila N. Shirodkar | Pratibha Dev
Source: Physical Review Materials (article, 2022-09-19)
Field: Materials Science — Materials Chemistry

Predicting phase preferences of two-dimensional transition metal dichalcogenides using machine learning Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of several possible structures, with the most common being the trigonal prismatic and octahedral symmetry phases. Since the structure determines the electronic properties, being able to predict phase preferences of TMDs from just the knowledge of the constituent atoms is highly desired, but has remained a long-standing problem. In this study, we applied high-throughput quantum mechanical computations with machine learning algorithms to solve this old problem. Our analysis provides insights into determining physiochemical factors that dictate the phase preference of a TMD, identifying and going beyond the attributes considered by earlier researchers in predicting crystal structures. A knowledge of these underlying physiochemical factors not only helps us to rationalize, but also to accurately predict structural preferences. We show that machine learning algorithms are powerful tools that can be used not only to find new materials with targeted properties, but also to find connections between elemental attributes and the target property/properties that were not previously obvious. Materials science ; Transition metal ; Phase transition ; Phase (matter) ; Condensed matter physics ; Metallurgy ; Engineering physics ; Catalysis 2D Materials and Applications ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Jinchuan Long | Gangfeng Xiao | Qinxiang Xia | Xinyun Wang
Source: Journal of Magnesium and Alloys (article, 2022-12-06)
Field: Materials Science — Biomaterials

Study of microstructure evolution of magnesium alloy cylindrical part with longitudinal inner ribs during hot flow forming by coupling ANN-modified CA and FEA Hot flow forming (HFF) is a promising forming technology to manufacture thin-walled cylindrical part with longitudinal inner ribs (CPLIRs) made of magnesium (Mg) alloys, which has wide applications in the aerospace field. However, due to the thermo-mechanical coupling effect and the existence of stiffened structure, complex microstructure evolution and uneven microstructure occur easily at the cylindrical wall (CW) and inner rib (IR) of Mg alloy thin-walled CPLIRs during the HFF. In this paper, a modified cellular automaton (CA) model of Mg alloy considering the effects of deformation conditions on material parameters was developed using the artificial neural network (ANN) method. It is found that the ANN-modified CA model exhibits better predictability for the microstructure of hot deformation than the conventional CA model. Furthermore, the microstructure evolution of ZK61 alloy CPLIRs during the HFF was analyzed by coupling the modified CA model and finite element analysis (FEA). The results show that compared with the microstructure at the same layer of the IR, more refined grains and less sufficient DRX resulted from larger strain and strain rate occur at that of the CW; various differences of strain and strain rate in the wall-thickness exist between the CW and IR, which leads to the inhomogeneity of microstructure rising firstly and declining from the inside layer to outside layer; the obtained Hall-Petch relationship between the measured microhardness and predicted grain sizes at the CW and the IR indicates the reliability of the coupled FEA-CA simulation results. Microstructure ; Materials science ; Magnesium alloy ; Coupling (piping) ; Deformation (meteorology) ; Composite material ; Alloy ; Indentation hardness ; Finite element method ; Strain rate ; Metallurgy ; Structural engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metallurgy and Material Forming
Authors: Kareem S. Aggour | Andrew J. Detor | Alfredo Gabaldon | Varish Mulwad | Abha Moitra | Paul Cuddihy | Virendra Kumar
Source: Integrating materials and manufacturing innovation (article, 2022-12-01)
Field: Materials Science — Materials Chemistry

Compound Knowledge Graph-Enabled AI Assistant for Accelerated Materials Discovery Abstract Materials scientists are facing increasingly challenging multi-objective performance requirements to meet the needs of modern systems such as lighter-weight and more fuel-efficient aircraft engines, and higher heat and oxidation-resistant steam turbines. While so-called second wave statistical machine learning techniques are beginning to accelerate the materials development cycle, most materials science applications are data-deprived when compared to the vastness and complexity of the search space of possible solutions. In line with DARPA’s vision of third wave AI approaches, we believe a combination of data-driven statistical machine learning and domain knowledge will be required to achieve a true revolution in materials discovery. To that end, we envision and have begun reducing to practice a system that fuses three forms of knowledge—factual scientific knowledge, physics-based and/or data-driven analytical models, and domain expert knowledge—into a single ‘Compound Knowledge Graph’ in which contextual reasoning and adaptation can be performed to answer increasingly complex questions. We believe this Compound Knowledge Graph-based system can be the nucleus of a collaborative AI assistant that supports stateful natural language back-and-forth dialogs between materials scientists and the AI to accelerate the development and discovery of new materials. This paper details our vision, summarizes our progress to date on a steam turbine blade coating use case, and outlines our thoughts on the key challenges in making this vision a reality. Computer science ; Domain knowledge ; Artificial intelligence ; Data science ; Knowledge extraction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Nguyen Thuy Chung | Yoon-Sik So | Jin-Soek Yoo | Jung‐Gu Kim
Source: Scientific Reports (article, 2022-11-24)
Field: Materials Science — Materials Chemistry

Machine learning modeling of predictive external corrosion rates of spent nuclear fuel carbon steel canister in soil Soil corrosion is always a critical concern to corrosion engineering because of the economic influence of soil infrastructures as has been and has recently been the focus of spent nuclear fuel canisters. Besides corrosion protection, the corrosion prediction of the canister is also important. Advanced knowledge of the corrosion rate of spent nuclear fuel canister material in a particular environment can be extremely helpful in choosing the best protection method. Applying machine learning (ML) to corrosion rate prediction solves all the challenges because of the number of variables affecting soil corrosion. In this study, several algorithms of ML, including series individual, boosting, bagging artificial neural network (ANN), series individual, boosting, bagging Chi-squared automatic interaction detection (CHAID) tree decision, linear regression (LR) and an ensemble learning (EL) merge the best option that collects from 3 algorithm methods above. From the performance of each model to find the model with the highest accuracy is the ensemble stacking method. Mean absolute error performance matrices are shown in Fig. 15. Besides applying ML, the significance of the input variables was also determined through sensitivity analysis using the feature importance criterion, and the carbon steel corrosion rate is the most sensitive to temperature and chloride. Corrosion ; Artificial neural network ; Computer science ; Decision tree ; Spent nuclear fuel ; Random forest ; Boosting (machine learning) ; Ensemble learning ; Artificial intelligence ; Machine learning ; Environmental science ; Materials science ; Nuclear engineering ; Engineering ; Metallurgy Corrosion Behavior and Inhibition ; Concrete Corrosion and Durability ; Non-Destructive Testing Techniques
Authors: Viet Hung Ho | Cao Thang Nguyen | Hoang D. Nguyen | Hyun Suk Oh | Myoungsu Shin | Sung Youb Kim
Source: ACS Applied Nano Materials (article, 2022-07-25)
Field: Materials Science — Materials Chemistry

Hydrogenated Graphene with Tunable Poisson’s Ratio Using Machine Learning: Implication for Wearable Devices and Strain Sensors The Poisson’s ratio of two-dimensional materials such as graphene can be tailored by surface hydrogenation. The density and distribution of hydrogenation may significantly affect the Poisson’s ratio of the graphene structure. Therefore, optimization of the distribution of hydrogenation is useful to achieve the structure with a targeted Poisson’s ratio. For this purpose, we developed an inverse design algorithm based on machine learning using the XGBoost method to reveal the relationship between the Poisson’s ratio and distribution of hydrogenation. Based on this relationship, we can optimize the hydrogenated graphene structure to have a low Poisson’s ratio. Instead of performing molecular dynamic simulations for all possible structures, we could find the optimal structures using the search algorithm and save significant computational resources. This algorithm could successfully discover structures with low Poisson’s ratios around −0.5 after only 1600 simulations in a large design space of approximately 5.2 × 106 possible configurations. Moreover, the optimal structures were found to exhibit excellent flexibility under compression of around −65% without failure and can be used in many applications such as flexible strain sensors. Our results demonstrate the applicability of machine learning to the efficient development of new metamaterials with desired properties. Graphene ; Poisson distribution ; Poisson's ratio ; Inverse ; Auxetics ; Materials science ; Flexibility (engineering) ; Computer science ; Algorithm ; Nanotechnology ; Composite material ; Mathematics ; Geometry ; Statistics Machine Learning in Materials Science ; Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Min‐Jeong Lee | Ji-Yoon Kim | Paul Kim | In-Seo Lee | Medard Edmund Mswahili | Young-Seob Jeong | Guang J. Choi
Source: Pharmaceutics (article, 2022-02-16)
Field: Chemistry — Physical and Theoretical Chemistry

Novel Cocrystals of Vonoprazan: Machine Learning-Assisted Discovery Vonoprazan (VPZ) is the first-in-class potassium-competitive acid blocker (P-CAB), and has many advantages over proton pump inhibitors (PPIs). It is administered as a fumarate salt for the treatment of acid-related diseases, including reflux esophagitis, gastric ulcer, and duodenal ulcer, and for eradication of <i>Helicobacter pylori</i>. To discover novel cocrystals of VPZ, we adopted an artificial neural network (ANN)-based machine learning model as a virtual screening tool that can guide selection of the most promising coformers for VPZ cocrystals. Experimental screening by liquid-assisted grinding (LAG) confirmed that 8 of 19 coformers selected by the ANN model were likely to create new solid forms with VPZ. Structurally similar benzenediols and benzenetriols, i.e., catechol (CAT), resorcinol (RES), hydroquinone (HYQ), and pyrogallol (GAL), were used as coformers to obtain phase pure cocrystals with VPZ by reaction crystallization. We successfully prepared and characterized three novel cocrystals: VPZ-RES, VPZ-CAT, and VPZ-GAL. VPZ-RES had the highest solubility among the novel cocrystals studied here, and was even more soluble than the commercially available fumarate salt of VPZ in solution at pH 6.8. In addition, novel VPZ cocrystals had superior stability in aqueous media than VPZ fumarates, demonstrating their potential for improved pharmaceutical performance. Chemistry ; Pyrogallol ; Combinatorial chemistry ; Organic chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Chemical and Physical Properties in Aqueous Solutions