jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Russlan Jaafreh | Tamer Abuhmed | Jung‐Gu Kim | Kotiba Hamad
Source: Journal of Materiomics (article, 2021-11-17)
Field: Materials Science — Materials Chemistry

Crystal structure guided machine learning for the discovery and design of intrinsically hard materials In this work, a machine learning (ML) model was created to predict intrinsic hardness of various compounds using their crystal chemistry. For this purpose, an initial dataset, containing the hardness values of 270 compounds and counterpart applied loads, was employed in the learning process. Based on various features generated using crystal information, an ML model, with a high accuracy (R2 = 0.942), was built using extreme gradient boosting (XGB) algorithm. Experimental validations conducted by hardness measurements of various compounds, including MSi2 (M = Nb, Ce, V, and Ta), Al2O3, and FeB4, showed that the XGB model was able to reproduce load-dependent hardness behaviors of these compounds. In addition, this model was also used to predict the behavior based on prototype crystal structures that are randomly substituted with elements. Materials science ; Crystal (programming language) ; Boosting (machine learning) ; Crystal structure ; Gradient boosting ; Crystal structure prediction ; Extreme learning machine ; Work (physics) ; Machine learning ; Process (computing) ; Biological system ; Computer science ; Crystallography ; Thermodynamics ; Physics ; Chemistry ; Artificial neural network ; Random forest Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced materials and composites
Authors: Changming Ke | Weigang He | Shixue Liu | Ru Xiao | Shi Liu | Zijing Lin
Source: The Journal of Physical Chemistry C (article, 2021-05-12)
Field: Materials Science — Materials Chemistry

Multiscale Catalyst Design for Steam Methane Reforming Assisted by Deep Learning Computational design of high-quality catalysts targeting specific operation conditions is a challenging task due to the mechanistic, structural, and environmental complexities across multiple length and time scales. A multiscale method of a catalyst design linking ab initio calculations, microkinetics, and multiphysics modeling was proposed to address this challenge. The chemistry-based analytical model derived from a microkinetic model assisted by first-principles-based deep neural networks efficiently bridged zero Kelvin ab initio microscopic descriptors and multiphysics modeling. We applied the multiscale method to the design of carbon-resistant steam methane reforming catalysts, successfully identifying a few cost-efficient bimetallic alloys for CH4 internal reforming solid oxide fuel cells. The multiphysics modeling demonstrates that catalysts of relatively low activity such as NiZn are actually beneficial for fuel efficiency, highlighting the importance of the multiphysics model for a multiscale computational catalyst design. Multiphysics ; Multiscale modeling ; Steam reforming ; Methane ; Catalysis ; Methane reformer ; Materials science ; Computer science ; Process engineering ; Nanotechnology ; Chemistry ; Engineering ; Thermodynamics ; Computational chemistry ; Physics ; Hydrogen production ; Finite element method Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Max Pinheiro | Fuchun Ge | Nicolas Ferré | Pavlo O. Dral | Mario Barbatti
Source: ChemRxiv (preprint, 2021-08-16)
Field: Materials Science — Materials Chemistry

Choosing the right molecular machine learning potential Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential's main features, and judge what they could expect from each one. Computer science ; Artificial intelligence ; Machine learning ; Observable ; Yield (engineering) ; Energy (signal processing) ; Potential energy ; Energy cost ; Quantum ; Data science ; Environmental economics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Yi Luo | Saientan Bag | Orysia Zaremba | Jacopo Andreo | Stefan Wuttke | Manuel Tsotsalas | Pascal Friederich
Source: ChemRxiv (preprint, 2021-08-03)
Field: Chemistry — Inorganic Chemistry

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: (i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, (ii) training and optimizing ML models by employing the MOF database, and (iii) predicting the synthesis conditions for new MOF structures. The ML models even at an initial stage exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. Rationalization (economics) ; Computer science ; Artificial intelligence ; Field (mathematics) ; Machine learning ; Data mining ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Tzu-Chia Chen | Marischa Elveny | A. Surendar | Adedoyin Isola Lawal | Angelina Olegovna Zekiy | Rabeya Anzum
Source: Modelling and Simulation in Materials Science and Engineering (article, 2021-07-23)
Field: Materials Science — Materials Chemistry

Developing a multilateral-based neural network model for engineering of high entropy amorphous alloys Abstract Due to a great number of composition-processing factors, it is very difficult to design high entropy amorphous alloys without performing manifold trial-and-error experimentations. To solve this problem, in this study we developed a machine learning-based approach, namely multilateral-based neural network, which is able to predict new high entropy amorphous compositions through estimating the highest glass forming ability and the critical casting thickness. In this approach, the entropy parameters were individually correlated to each input, which leads to the improvement of predictive model in evaluating the high entropy glassy alloys. As a case study, Ti 20 Zr 20 Hf 20 Be 20 Co 20 high entropy metallic glass (MG) was considered and the effects of added elements such as Y, Ni, Cr and V and Cu on the glass formation and critical casting thickness were investigated. According to the results, it is determined that the Y addition acts as a microalloying process in the base composition, while other elements improve the configurational entropy and the total negative heat of mixing, which lead to the engineering of equi-atomic high entropy MGs. Materials science ; Amorphous metal ; Amorphous solid ; Artificial neural network ; High entropy alloys ; Entropy (arrow of time) ; Configuration entropy ; Thermodynamics ; Alloy ; Metallurgy ; Statistical physics ; Artificial intelligence ; Computer science ; Crystallography ; Physics Phase-change materials and chalcogenides ; High Entropy Alloys Studies ; Metallic Glasses and Amorphous Alloys
Authors: J.S. Owusu-Danquah | Abdallah Bseiso | Srinivas Allena
Source: Neural Computing and Applications (article, 2021-10-29)
Field: Materials Science — Materials Chemistry

Artificial neural network models to predict the response of 55NiTi shape memory alloy under stress and thermal cycles Shape-memory alloy ; Artificial neural network ; Alloy ; Materials science ; Work (physics) ; Stress (linguistics) ; Computational Science and Engineering ; Computer science ; Actuator ; Biological system ; Mechanical engineering ; Artificial intelligence ; Composite material ; Machine learning ; Engineering Shape Memory Alloy Transformations
Authors: Vasiliki Fragkia | Isak Worre Foged | Anke Pasold
Source: Technology|Architecture + Design (article, 2021-07-03)
Field: Materials Science — Materials Chemistry

Predictive Information Modeling: Machine Learning Strategies for Material Uncertainty This article presents a new design framework for the specification and prototyping of geometrically and behaviorally complex materials with graded properties, coined predictive information modeling (PIM). The contribution is the development of new circular design workflows employing machine learning for predicting fabrication files based on performance and design requirements. The aim is linking endogenous capacities as well as exogenous environmental dynamics of graded materials, as an approach to material focused intelligent design systems. Using two experimental case studies, the research demonstrates PIM as an applied design framework for addressing (1) material uncertainty, (2) multi-scale data integration, and (3) cyclical fabrication workflows. Through the analysis of these models, we demonstrate research methods that are validated for design applications, review their implications, and discuss further trajectories. Computer science ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; Mineral Processing and Grinding ; Fault Detection and Control Systems
Authors: Pankaj Rajak | Nitish Baradwaj | Ken‐ichi Nomura | Aravind Krishnamoorthy | José Pedro Rino | Kohei Shimamura | Shogo Fukushima | Fuyuki Shimojo | Rajiv K. Kalia | Aiichiro Nakano | Priya Vashishta
Source: The Journal of Physical Chemistry Letters (article, 2021-06-24)
Field: Materials Science — Ceramics and Composites

Neural Network Quantum Molecular Dynamics, Intermediate Range Order in GeSe<sub>2</sub>, and Neutron Scattering Experiments A remarkable property of certain covalent glasses and their melts is intermediate range order, manifested as the first sharp diffraction peak (FSDP) in neutron-scattering experiments, as was exhaustively investigated by Price, Saboungi, and collaborators. Atomistic simulations thus far have relied on either quantum molecular dynamics (QMD), with systems too small to resolve FSDP, or classical molecular dynamics, without quantum-mechanical accuracy. We investigate prototypical FSDP in GeSe<sub>2</sub> glass and melt using neural-network quantum molecular dynamics (NNQMD) based on machine learning, which allows large simulation sizes with validated quantum mechanical accuracy to make quantitative comparisons with neutron data. The system-size dependence of the FSDP height is determined by comparing QMD and NNQMD simulations with experimental data. Partial pair distribution functions, bond-angle distributions, partial and neutron structure factors, and ring-size distributions are presented. Calculated FSDP heights agree quantitatively with neutron scattering data for GeSe<sub>2</sub> glass at 10 K and melt at 1100 K. Neutron diffraction ; Neutron scattering ; Quantum ; Molecular dynamics ; Scattering ; Small-angle neutron scattering ; Materials science ; Neutron ; Diffraction ; Physics ; Molecular physics ; Condensed matter physics ; Optics ; Nuclear physics ; Quantum mechanics Glass properties and applications ; Phase-change materials and chalcogenides ; Machine Learning in Materials Science
Authors: Yang Yang | Long Zhao | Chen-Xu Han | Xiangdong Ding | Turab Lookman | Jun Sun | Hongxiang Zong
Source: Journal of Materials Informatics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Taking materials dynamics to new extremes using machine learning interatomic potentials Journal of Materials Informatics (JMI) is an international peer-reviewed, open access journal. The journal provides a platform for presentation, publication, and exchange of materials informatics research, seeking to break down barriers among materials science and engineering, data science and engineering, and artificial intelligence. The journal also provides a venue for presenting innovative approaches to overcome the difficulties in unraveling the complexity of data associated with numerous factors including noise, uncertainty, and small amount, to achieve the quantum jump from data to knowledge. Informatics ; Materials informatics ; Data science ; Computer science ; Science and engineering ; Presentation (obstetrics) ; Jump ; Engineering informatics ; Engineering ethics ; Health informatics ; Engineering ; Physics ; Political science Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques
Authors: Xiangyue Liu | Gerard Meijer | Jesús Pérez‐Ríos
Source: RSC Advances (article, 2021-01-01)
Field: Chemistry — Spectroscopy

On the relationship between spectroscopic constants of diatomic molecules: a machine learning approach Through a machine learning approach, we show that the equilibrium distance, harmonic vibrational frequency and binding energy of diatomic molecules are related, independently of the nature of the bond of a molecule; they depend solely on the group and period of the constituent atoms. As a result, we show that by employing the group and period of the atoms that form a molecule, the spectroscopic constants are predicted with an accuracy of <5%, whereas for the A-excited electronic state it is needed to include other atomic properties leading to an accuracy of <11%. Diatomic molecule ; Molecule ; Excited state ; Atomic physics ; Chemistry ; Group (periodic table) ; Harmonic ; Computational chemistry ; Physics ; Quantum mechanics Spectroscopy and Laser Applications ; Advanced Chemical Physics Studies ; Chemical Thermodynamics and Molecular Structure
Authors: Tanuja Joshi | Hemlata Pundir | Subhash Chandra
Source: Journal of Biomolecular Structure and Dynamics (article, 2021-04-21)
Field: Chemistry — Organic Chemistry

Deep-learning based repurposing of FDA-approved drugs against<i>Candida albicans</i>dihydrofolate reductase and molecular dynamics study <i>Candida albicans</i> causes the fatal fungal bloodstream infection in humans called Candidiasis. Most of the <i>Candida</i> species are resistant to the antifungals used to treat them. Drug-resistant <i>C. albicans</i> poses very serious public health issues. To overcome this, the development of effective drugs with novel mechanism(s) of action is requisite. Drug repurposing is considered a viable alternative approach to overcome the above issue. In the present study, we have attempted to identify drugs that could target the essential enzyme, dihydrofolate reductase of <i>C. albicans</i> (<i>Ca</i>DHFR) to find out potent and selective antifungal antifolates. FDA-approved-drug-library from the Selleck database containing 1930 drugs was screened against <i>Ca</i>DHFR using deep-learning, molecular docking, X-score and similarity search methods. The screened compounds showing better binding with <i>Ca</i>DHFR were subjected to molecular dynamics simulation (MDS). The results of post-MDS analysis like RMSD, RMSF, RG, SASA, the number of hydrogen bonds and PCA suggest that Paritaprevir, Lumacaftor and Rifampin can make good interaction with <i>Ca</i>DHFR. Furthermore, analysis of binding free energy corroborated the stability of interactions as they had binding energy of -114.91 kJ mol<sup>-1</sup>, -79.22 kJ mol<sup>-1</sup> and -78.52 kJ mol<sup>-1</sup> for Paritaprevir, Lumacaftor and Rifampin respectively as compared to the reference (-63.10 kJ mol<sup>-1</sup>). From the results, we conclude that these drugs have great potential to inhibit <i>Ca</i>DHFR and would add to the drug discovery against candidiasis, and hence these drugs for repurposing should be explored further. Dihydrofolate reductase ; Drug repositioning ; Candida albicans ; Repurposing ; Drug ; Docking (animal) ; Chemistry ; Computational biology ; Pharmacology ; Biology ; Medicine ; Enzyme ; Biochemistry ; Microbiology Synthesis and biological activity ; Antifungal resistance and susceptibility ; Computational Drug Discovery Methods
Authors: Wei-Qi Yan | Yi‐An Zhu | Xinggui Zhou | Weikang Yuan
Source: Chinese Journal of Chemical Engineering (article, 2021-12-14)
Field: Materials Science — Materials Chemistry

Rational design of heterogeneous catalysts by breaking and rebuilding scaling relations Scaling ; Catalysis ; Computer science ; Limit (mathematics) ; Field (mathematics) ; Scaling limit ; Biochemical engineering ; Chemistry ; Mathematics ; Engineering Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Anna Lucia de Marco | Davide Bochicchio | Andrea Gardin | Giovanni Doni | Giovanni M. Pavan
Source: ChemRxiv (preprint, 2021-02-01)
Field: Materials Science — Biomaterials

Controlling Exchange Pathways in Dynamic Supramolecular Polymers by Controlling Defects Supramolecular fibers, composed of monomers that self-assemble directionally via non-covalent interactions, are ubiquitous in nature and of great interest in chemistry. In these structures, the constitutive monomers continuously exchange in-and-out the assembly according to a well-defined supramolecular equilibrium. However, unraveling the exchange pathways and their molecular determinants constitutes a non-trivial challenge. Here we combine coarse-grained modeling, enhanced sampling, and machine learning to investigate the key factors controlling the monomer exchange pathways in synthetic supramolecular polymers having an intrinsic dynamic behavior. We demonstrate how the competition of directional vs. non-directional interactions between the monomers controls the creation/annihilation of defects in the supramolecular polymers, from where monomers exchange proceeds. This competition determines the exchange pathway, dictating whether a fiber statistically swaps monomers from the tips or all along its length. Finally, thanks to their generality, our models allow the investigation of molecular approaches to control the exchange pathways in these dynamic assemblies. Supramolecular chemistry ; Supramolecular polymers ; Monomer ; Polymer ; Dynamic covalent chemistry ; Nanotechnology ; Chemistry ; Chemical physics ; Materials science ; Molecule ; Organic chemistry Supramolecular Self-Assembly in Materials ; Polymer composites and self-healing ; Supramolecular Chemistry and Complexes
Authors: Kamal Choudhary | Taner Yildirim | Daniel W. Siderius | A. Gilad Kusne | Austin McDannald | Diana L. Ortiz‐Montalvo
Source: arXiv (Cornell University) (preprint, 2021-12-19)
Field: Chemistry — Inorganic Chemistry

Graph Neural Network Predictions of Metal Organic Framework CO2 Adsorption Properties The increasing CO2 level is a critical concern and suitable materials are needed to capture such gases from the environment. While experimental and conventional computational methods are useful in finding such materials, they are usually slow and there is a need to expedite such processes. We use Atomistic Line Graph Neural Network (ALIGNN) method to predict CO2 adsorption in metal organic frameworks (MOF), which are known for their high functional tunability. We train ALIGNN models for hypothetical MOF (hMOF) database with 137953 MOFs with grand canonical Monte Carlo (GCMC) based CO2 adsorption isotherms. We develop high accuracy and fast models for pre-screening applications. We apply the trained model on CoREMOF database and computationally rank them for experimental synthesis. In addition to the CO2 adsorption isotherm, we also train models for electronic bandgaps, surface area, void fraction, lowest cavity diameter, and pore limiting diameter, and illustrate the strength and limitation of such graph neural network models. For a few candidate MOFs we carry out GCMC calculations to evaluate the deep-learning (DL) predictions. Adsorption ; Metal-organic framework ; Limiting ; Artificial neural network ; Computer science ; Materials science ; Graph ; Monte Carlo method ; Porosity ; Sorption isotherm ; Artificial intelligence ; Chemistry ; Physical chemistry ; Theoretical computer science ; Mathematics ; Mechanical engineering ; Engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Carbon Dioxide Capture Technologies
Authors: Shin Young Bae | Jun‐Seop Shin | Yeon Soo Kim | Jong Min Lee
Source: IFAC-PapersOnLine (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Decision tree analysis on the performance of zeolite-based SCR catalysts Urea-based selective catalytic reduction (SCR) is a promising method for removing NOx emissions. In the urea-based SCR method, zeolite-based catalysts are popularly used owing to their applicability over a wide range of temperatures compared to other catalysts. However, they still have several drawbacks such as inferior performance at low temperatures and thermal instability. This has subsequently led to numerous studies and experiments on the development of superior catalysts. While substantial experimental data exist on zeolite-based catalysts, extracting useful information from the data with a simple literature search is difficult owing to not only the amount of data but also complex correlations between feature, including preparation variables such as doped-metal loading and operational variables such as reaction temperature. Recently, extracting insights from a large database has become possible by utilizing machine learning tools. Among them, the decision tree can extract insights on the synthesis of the catalysts as the results derived from the models are intuitive and easy to interpret, unlike those from conventional discriminant machine learning models. In this study, classification models are obtained by training decision tree models with literature data on zeolite-based urea SCR catalysts, particularly the Beta and ZSM-5 types, and several experimental heuristics are extracted from the derived models. Zeolite ; Decision tree ; Catalysis ; Heuristics ; Computer science ; Linear discriminant analysis ; Selective catalytic reduction ; Chemometrics ; Machine learning ; Materials science ; Chemistry ; Artificial intelligence ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies