jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xia Liang | Johan Klarbring | William J. Baldwin | Zhenzhu Li | Gábor Cśanyi | Aron Walsh
Source: arXiv (Cornell University) (preprint, 2023-05-19)
Field: Materials Science — Materials Chemistry

Structural Dynamics Descriptors for Metal Halide Perovskites Metal halide perovskites have shown extraordinary performance in solar energy conversion technologies. They have been classified as "soft semiconductors" due to their flexible corner-sharing octahedral networks and polymorphous nature. Understanding the local and average structures continues to be challenging for both modelling and experiments. Here, we report the quantitative analysis of structural dynamics in time and space from molecular dynamics simulations of perovskite crystals. The compact descriptors provided cover a wide variety of structural properties, including octahedral tilting and distortion, local lattice parameters, molecular orientations, as well as their spatial correlation. To validate our methods, we have trained a machine learning force field (MLFF) for methylammonium lead bromide (CH$_3$NH$_3$PbBr$_3$) using an on-the-fly training approach with Gaussian process regression. The known stable phases are reproduced and we find an additional symmetry-breaking effect in the cubic and tetragonal phases close to the phase transition temperature. To test the implementation for large trajectories, we also apply it to 69,120 atom simulations for CsPbI$_3$ based on an MLFF developed using the atomic cluster expansion formalism. The structural dynamics descriptors and Python toolkit are general to perovskites and readily transferable to more complex compositions. Tetragonal crystal system ; Molecular dynamics ; Chemical physics ; Halide ; Perovskite (structure) ; Materials science ; Octahedron ; Chemistry ; Statistical physics ; Crystal structure ; Physics ; Crystallography ; Computational chemistry Machine Learning in Materials Science ; Perovskite Materials and Applications
Authors: Shoichiro Nakamura | Masahito Segawa | Akinori Yamanaka
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Non-equilibrium multi-phase-field simulation for rapid solidification process in SUS316L stainless steel during additive manufacturing A solidification during an additive manufacturing (AM) process is occurred under a highly non-equilibrium condition such as high cooling rate and large thermal gradient. In this study, we use the non-equilibrium multi-phase-field (NEMPF) model coupled with the CALPHAD-based thermodynamic database to simulate the solidification behavior in a SUS316L stainless steel during AM. Although the NEMPF model is capable of describing the microstructure evolution and the partitioning of solute atoms at the solid-liquid interface under the non-equilibrium condition, its computational cost is high. Therefore, we reduce the computational cost of the NEMPF simulation by replacing the CALPHAD-based thermodynamic calculation with the machine learning method where the trained neural network estimates the chemical free energy and chemical potential as a function of temperature and chemical composition at the solid-liquid interface. In this paper, we show the rapid solidification process in SUS316L stainless steel simulated by the NEMPF coupled with the trained neural network. The results show that the dendrites without secondary arms form and the solute atoms segregate at the interfacial region. Materials science ; Composite material Solidification and crystal growth phenomena ; Additive Manufacturing Materials and Processes ; Machine Learning in Materials Science
Authors: William Poole | Thomas E. Ouldridge | Manoj Gopalkrishnan
Source: arXiv (Cornell University) (preprint, 2023-11-02)
Field: Materials Science — Materials Chemistry

Autonomous Learning of Generative Models with Chemical Reaction Network Ensembles Can a micron sized sack of interacting molecules autonomously learn an internal model of a complex and fluctuating environment? We draw insights from control theory, machine learning theory, chemical reaction network theory, and statistical physics to develop a general architecture whereby a broad class of chemical systems can autonomously learn complex distributions. Our construction takes the form of a chemical implementation of machine learning's optimization workhorse: gradient descent on the relative entropy cost function. We show how this method can be applied to optimize any detailed balanced chemical reaction network and that the construction is capable of using hidden units to learn complex distributions. This result is then recast as a form of integral feedback control. Finally, due to our use of an explicit physical model of learning, we are able to derive thermodynamic costs and trade-offs associated to this process. Computer science ; Gradient descent ; Artificial intelligence ; Process (computing) ; Class (philosophy) ; Entropy (arrow of time) ; Artificial neural network ; Machine learning ; Physics Machine Learning in Materials Science ; Gene Regulatory Network Analysis ; Computational Drug Discovery Methods
Authors: Ayieko, Antony A. | Michael O. Atambo | George Amolo
Source: arXiv (Cornell University) (preprint, 2023-10-27)
Field: Materials Science — Materials Chemistry

High Throughput Screening of Ternary Nitrides with Convolutional Neural Networks The development of new materials is a core aspect of advancement in synthesis and application for industry. There is a vast number of possible chemical permutations of the basic elements that can be explored to synthesize materials that possess attractive catalytic, mechanical and electrical properties that may not be easily accessible to traditional experimental methods for various reasons, including cost and time considerations. Nitrides, as examples, require very stringent and precise conditions to successfully synthesize making their experimental exploration very slow. In this paper, we employ the use of machine learning algorithms to predict the bulk properties of Ternary Metal Nitrides (TMN), specifically their bulk modulus which is correlated with the hardness of the material. We were able to develop a consistent model with encouraging accuracy, that was able to predict the bulk moduli of materials that previously did not have computed values. The model was trained on $10^3$ ternary materials with known elastic properties and defined structures, and was able to predict the bulk modulus of $\thickapprox 1,000$ Ternary Metal Nitrides (TMNs) to $\thickapprox 80\%$ accuracy. This approach is orders of magnitude faster than the traditional computational approaches like density functional theory (DFT)\cite{dft-paper} which makes exploratory identification of materials with promising properties fast. We propose that such models be used to select interesting candidates for high throughput computation from first principles. Ternary operation ; Throughput ; Nitride ; Computer science ; Bulk modulus ; Computation ; Moduli ; Convolutional neural network ; Materials science ; Algorithm ; Nanotechnology ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Pier Paolo Prosini
Source: Crystals (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Exploitation of the Concept of Vicariance to Predict the Space Group of Lithiated Manganese or Cobalt Oxides In this work, a machine learning program was used to predict the crystal structure of lithiated manganese or cobalt oxides based only on their chemical composition. The composition and crystal structure of lithiated iron oxides were used as trial matrix. To assign the crystal structure, the Euclidean distance between the stoichiometric coefficients of the elements of the compound under testing and the trial compound was calculated. The softmax function was used to convert this distance into a probability distribution. The compound under test was assigned the space group of the training compound that appeared with the highest percentage. The logarithmic cross-entropy loss was used in evaluating the forecast results. The results showed that the program, for logarithmic cross-entropy loss values between 0.2 and 0.3, can predict the crystalline group with an accuracy of about 0.67. In the same range, sensitivity and precision values are placed in a range between 0.6 and 0.8, respectively, and the F1_Score reaches values above 0.62. Cobalt ; Manganese ; Crystal structure ; Mathematics ; Group (periodic table) ; Materials science ; Chemistry ; Crystallography ; Inorganic chemistry ; Physics ; Metallurgy ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Daniel Farfán | Carolina Gómez-Márquez | Dania Sandoval-Nuñez | Omar Paredes | J. Moralès
Source:  (article, 2023-10-05)
Field: Materials Science — Materials Chemistry

SELFIES RxNet: Empowering Reaction Synthesis Through Symbolic Notations and AI We present a Deep Learning generative self-supervised chemical reaction model based on Bidirectional and Auto-Regressive Transformer (BART) architecture. We made a model that is able to generate and predict reactions in SMILES representation to produce valid molecules only. Notation ; Computer science ; Transformer ; Generative grammar ; Artificial intelligence ; Architecture ; Representation (politics) ; Natural language processing ; Theoretical computer science ; Engineering ; Arithmetic ; Electrical engineering ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: G. Anand
Source: arXiv (Cornell University) (preprint, 2023-02-16)
Field: Materials Science — Materials Chemistry

GAASP: Genetic Algorithm Based Atomistic Sampling Protocol for High-Entropy Materials High-Entropy Materials are composed of multiple elements on comparatively simpler lattices. Due to the multicomponent nature of such materials, the atomic scale sampling is computationally expensive due to the combinatorial complexity. We propose a genetic algorithm based methodology for sampling such complex chemically-disordered materials. Genetic Algorithm based Atomistic Sampling Protocol (GAASP) variants can generate low and well as high-energy structures. GAASP low-energy variant in conjugation with metropolis criteria avoids the premature convergence as well as ensures the detailed balance condition. GAASP can be employed to generate the low-energy structures for thermodynamic predictions as well as diverse structures can be generated for machine learning applications. Sampling (signal processing) ; Computer science ; Entropy (arrow of time) ; Statistical physics ; Convergence (economics) ; Genetic algorithm ; Algorithm ; Physics ; Machine learning ; Thermodynamics Machine Learning in Materials Science ; nanoparticles nucleation surface interactions ; Advanced Chemical Physics Studies
Authors: Wei Wu | Congjian Wang | Wenjuan Bian | Dong Ding
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Root Cause Analysis of Degradation in Protonic Ceramic Electrochemical Cells with Interfacial Electrical Sensors Protonic ceramic electrochemical cells (PCEC), including solid oxide fuel/electrolysis cell, present promising routes to energy harvest and value-added chemical production. Despite tremendous efforts, PCEC still encounters several challenges, including poor thermal/chemical/mechanical compatibility and inferior solid-solid contact at the interfaces between electrolytes and electrodes. In this presentation, we will demonstrate in-line electrochemical characterization of interfacial electrical sensor embedded PCEC that can lead to fast identification of the failure mechanism of electrode supported PCECs. Upon the acquisition of quantitative contributions from different cell components to total degradation, data-driven machine learning is employed to further predict the long-term performance of the full cell up to 3000 hours. This presents novel insights into the degradation mechanism analysis in the solid-state electrochemical units, allowing ones to better modify the electrolyte and electrode materials and the interface between them, and develop more robust PCEC systems. The technology can be appliable to other electrochemical systems, including higher temperature counterpart (e.g., oxygen ion conducting solid oxide electrolysis cells, o-SOEC) Electrolysis ; Materials science ; Oxide ; Electrochemistry ; Electrode ; Ceramic ; Electrolyte ; Degradation (telecommunications) ; Chemical engineering ; Electrolytic cell ; Electrochemical cell ; Nanotechnology ; Computer science ; Composite material ; Chemistry ; Metallurgy Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Christopher D. Taylor
Source:  (article, 2023-03-19)
Field: Materials Science — Materials Chemistry

A Data-Driven Approach to Predicting Oxidation Rate Constants for Alloys Abstract High temperature alloys span multiple classes of materials including low alloy steels, stainless steels, nickel-chromium alloys, superalloys, aluminides, and, of more recent interest high entropy alloys, among others. Whereas many high temperature alloys deviate from the parabolic oxide growth law, the parabolic rate constant kp remains a useful indicator of the oxidation susceptibility for a given material. To design new classes of materials, and help with materials selection, it would be useful to directly predict the oxidation rate constants from materials features, such as composition and microstructure. With this goal in mind, parabolic rate constants have been collected from the literature for 75 alloys exposed to temperatures between 900 and 3000°F. Environments incorporated into the analysis include lab air, ambient and supercritical carbon dioxide, supercritical water, and steam. Predictive models for the oxidation rate constant were developed using machine learning and analyzed to provide insights into the leading factors producing corrosion resistance in these materials. Computer science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; High-Temperature Coating Behaviors
Authors: Qimeng Sun | Junbo Yang | Shizhao Wang | Wei Shen | Jianping Shi | Yi Song
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Spectroscopic Ellipsometry to Characterize the Transition-Metal Dichalcogenides Single Crystals Doping Concentration Ellipsometry ; Doping ; Transition metal ; Materials science ; Metal ; Analytical Chemistry (journal) ; Nanotechnology ; Inorganic chemistry ; Condensed matter physics ; Crystallography ; Chemistry ; Optoelectronics ; Metallurgy ; Thin film ; Physics ; Catalysis ; Environmental chemistry ; Organic chemistry 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Perovskite Materials and Applications
Authors: Edoardo Di Napoli | Xinzhe Wu | Thomas Bornhake | Piotr Kowalski
Source: arXiv (Cornell University) (preprint, 2023-03-07)
Field: Materials Science — Materials Chemistry

Computing formation enthalpies through an explainable machine learning method: the case of Lanthanide Orthophosphates solid solutions In the last decade, the use of Machine and Deep Learning (MDL) methods in Condensed Matter physics has seen a steep increase in the number of problems tackled and methods employed. A number of distinct MDL approaches have been employed in many different topics; from prediction of materials properties to computation of Density Functional Theory potentials and inter-atomic force fields. In many cases the result is a surrogate model which returns promising predictions but is opaque on the inner mechanisms of its success. On the other hand, the typical practitioner looks for answers that are explainable and provide a clear insight on the mechanisms governing a physical phenomena. In this work, we describe a proposal to use a sophisticated combination of traditional Machine Learning methods to obtain an explainable model that outputs an explicit functional formulation for the material property of interest. We demonstrate the effectiveness of our methodology in deriving a new highly accurate expression for the enthalpy of formation of solid solutions of lanthanides orthophosphates. Computation ; Lanthanide ; Computer science ; Artificial intelligence ; Density functional theory ; Work (physics) ; Opacity ; Property (philosophy) ; Enthalpy ; Machine learning ; Algorithm ; Thermodynamics ; Physics ; Chemistry ; Computational chemistry ; Ion ; Epistemology ; Quantum mechanics ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Arnaud Allera | Alexandra M. Goryaeva | Paul Lafourcade | Jean‐Bernard Maillet | Mihai‐Cosmin Marinica
Source: arXiv (Cornell University) (preprint, 2023-07-03)
Field: Materials Science — Materials Chemistry

Neighbors Map: an Efficient Atomic Descriptor for Structural Analysis Accurate structural analysis is essential to gain physical knowledge and understanding of atomic-scale processes in materials from atomistic simulations. However, traditional analysis methods often reach their limits when applied to crystalline systems with thermal fluctuations, defect-induced distortions, partial vitrification, etc. In order to enhance the means of structural analysis, we present a novel descriptor for encoding atomic environments into 2D images, based on a pixelated representation of graph-like architecture with weighted edge connections of neighboring atoms. This descriptor is well adapted for Convolutional Neural Networks and enables accurate structural analysis at a low computational cost. In this paper, we showcase a series of applications, including the classification of crystalline structures in distorted systems, tracking phase transformations up to the melting temperature, and analyzing liquid-to-amorphous transitions in pure metals and alloys. This work provides the foundation for robust and efficient structural analysis in materials science, opening up new possibilities for studying complex structural processes, which can not be described with traditional approaches. Computer science ; Representation (politics) ; Enhanced Data Rates for GSM Evolution ; Encoding (memory) ; Convolutional neural network ; Graph ; Artificial intelligence ; Theoretical computer science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Ion-surface interactions and analysis
Authors: Dr. Sarika Sharma
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-04-01)
Field: Chemistry — Inorganic Chemistry

Nanostructured Solutions For Global Decarbonization: A Comprehensive Analysis Of Metal-Organic Frameworks (MOFs) In Direct Air Capture (DAC) When atmospheric carbon dioxide (CO₂) concentrations increase rapidly due to human activities, the need for scalable carbon removal technology has grown considerably. Direct Air Capture (DAC) is a successful negative emission approach which is able to remove CO₂ by extracting it from the air and cleaning molecules to its target level even in an atmosphere. However, due to very low atmospheric CO₂ concentrations (∼400 ppm) and moisture, for DAC materials to be used, high selectivity and stability are essential attributes and low regeneration energy. To improve the application of DAC in dilute environments, metal-organic frameworks (MOFs) are a new class of very porous nanomaterials whose unique chemical and structural properties have made them promising for DAC applications. Functionalized MOFs (i.e., amine-grafted materials and hybrid ultra microporous structures) exhibit improved adsorption performance and are able to capture CO₂ even in humid conditions. Recent progress of MOF-based DAC technology in solid sorbents and membranes, and the relationship of molecular structure and CO₂ capture efficiency, also contribute to this. In this review, we also discuss the best computational and machine learning approaches for rapid screening and optimization of MOFs to ensure high-performance materials from a wide range of chemical compounds. Many challenges such as moisture sensitivity, high synthesis costs, structural instability, and energy-intensive regeneration are among the critical difficulties for MOF-based DAC systems to be achieved at large scale. To eliminate such limitations, innovative design solutions such as surface functionalization as well as scalable synthesis routes with high productivity of materials, and process integration should be adopted. Nanostructured MOFs represent a new path to global decarbonization with efficient and flexible DAC systems. Further interdisciplinary research in materials science, process engineering, and techno-economic analysis will translate laboratory results into carbon capture technologies that are compatible with net-zero climate targets at the scale of commercial production. Microporous material ; Adsorption ; Scalability ; Nanomaterials ; Carbon fibers ; Process (computing) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications
Authors: Dr. Sarika Sharma
Source: Open MIND (article, 2023-04-01)
Field: Chemistry — Inorganic Chemistry

Nanostructured Solutions For Global Decarbonization: A Comprehensive Analysis Of Metal-Organic Frameworks (MOFs) In Direct Air Capture (DAC) When atmospheric carbon dioxide (CO₂) concentrations increase rapidly due to human activities, the need for scalable carbon removal technology has grown considerably. Direct Air Capture (DAC) is a successful negative emission approach which is able to remove CO₂ by extracting it from the air and cleaning molecules to its target level even in an atmosphere. However, due to very low atmospheric CO₂ concentrations (∼400 ppm) and moisture, for DAC materials to be used, high selectivity and stability are essential attributes and low regeneration energy. To improve the application of DAC in dilute environments, metal-organic frameworks (MOFs) are a new class of very porous nanomaterials whose unique chemical and structural properties have made them promising for DAC applications. Functionalized MOFs (i.e., amine-grafted materials and hybrid ultra microporous structures) exhibit improved adsorption performance and are able to capture CO₂ even in humid conditions. Recent progress of MOF-based DAC technology in solid sorbents and membranes, and the relationship of molecular structure and CO₂ capture efficiency, also contribute to this. In this review, we also discuss the best computational and machine learning approaches for rapid screening and optimization of MOFs to ensure high-performance materials from a wide range of chemical compounds. Many challenges such as moisture sensitivity, high synthesis costs, structural instability, and energy-intensive regeneration are among the critical difficulties for MOF-based DAC systems to be achieved at large scale. To eliminate such limitations, innovative design solutions such as surface functionalization as well as scalable synthesis routes with high productivity of materials, and process integration should be adopted. Nanostructured MOFs represent a new path to global decarbonization with efficient and flexible DAC systems. Further interdisciplinary research in materials science, process engineering, and techno-economic analysis will translate laboratory results into carbon capture technologies that are compatible with net-zero climate targets at the scale of commercial production. Microporous material ; Adsorption ; Scalability ; Nanomaterials ; Carbon fibers ; Process (computing) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Covalent Organic Framework Applications