jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew G. Boebinger | Ayana Ghosh | Kevin M. Roccapriore | Sudhajit Misra | Kai Xiao | Stephen Jesse | Maxim Ziatdinov | Sergei V. Kalinin | Raymond R. Unocic
Source: arXiv (Cornell University) (preprint, 2023-10-12)
Field: Materials Science — Surfaces, Coatings and Films

When the atoms dance: exploring mechanisms of electron-beam induced modifications of materials with machine-learning assisted high temporal resolution electron microscopy Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS2. An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam. Scanning transmission electron microscopy ; Materials science ; Cathode ray ; Nanotechnology ; Fabrication ; Process (computing) ; Computer science ; Electron ; Transmission electron microscopy ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Neatithorn Keasangam | Jirasuta Chungprempree | Jitima Preechawong | Manit Nithitanakul | Pornsri Sapsrithong
Source: Materials science forum (article, 2023-04-27)
Field: Materials Science — Materials Chemistry

Acid-Activated Organobentonite-Based Highly Porous Foams via Polymerized High Internal Phase Emulsion: Preparation, Characterization and Machine Learning Prediction Preparation, characterization, and machine learning prediction of characteristics of acid-treated organobentonite-based highly porous foams via polymerized high internal phase emulsion were reported in this work. The effect of acid-treated organobentonite (AC-BTN) as an inorganic filler on the properties of poly(DVB)HIPE porous foam was experimentally investigated. Incorporating AC-BTN into the continuous phase of the high internal phase emulsion would improve thermal and mechanical properties and also increase the surface area of the resulting materials when compared to the unfilled poly(DVB)HIPE foam. Various amounts of AC-BTN, i.e., 0, 1, 3, 5, 7, and 10 wt.% of AC-BTN, were incorporated into the continuous phase to enhance the properties of poly(DVB)HIPE foam. The surface area and the degradation temperatures (T d ) for the series of poly(DVB)HIPE foam filled with AC-BTN increased with increasing filler content from 0 to 10 wt.%. The maximum improvement of mechanical properties was found with the addition of 5 wt.% of AC-BTN into the continuous phase of poly(DVB)HIPE foam. Moreover, the adsorption of CO 2 gas by poly(DVB)HIPE foam filled with AC-BTN was found to increase as well. It has been demonstrated in this study that the adsorption of CO 2 by poly(DVB)HIPE foam filled with AC-BTN increased by 127% (from 0.00295 to 0.00670 mol/g) compared with neat poly(DVB)HIPE foam. Additionally, the machine learning (ML) method with a linear regression algorithm was employed for the characterization of poly(DVB)HIPE foam and the prediction of properties according to composite composition. Surface area, pore volume, T d , compressive stress, and Young’s modulus were evaluated. The accuracy of prediction using a machine learning application with a linear regression model for properties of poly(DVB)HIPE foam filled with AC-BTN was also reported. Materials science ; Porosity ; Adsorption ; Emulsion ; Composite material ; Chemical engineering ; Polymerization ; Polymer ; Organic chemistry Pickering emulsions and particle stabilization ; Advanced Polymer Synthesis and Characterization ; Mesoporous Materials and Catalysis
Authors: Yong Zhao | Edirisuriya M. Dilanga Siriwardane | Zhenyao Wu | Nihang Fu | Mohammed Al‐Fahdi | Ming Hu | Jianjun Hu
Source: npj Computational Materials (article, 2023-06-17)
Field: Materials Science — Materials Chemistry

Author Correction: Physics guided deep learning for generative design of crystal materials with symmetry constraints Symmetry (geometry) ; Generative grammar ; Theoretical physics ; Physics ; Crystal (programming language) ; Computer science ; Artificial intelligence ; Geometry ; Mathematics Machine Learning in Materials Science
Authors: Heren Chellam G.
Source: Tuijin Jishu/Journal of Propulsion Technology (article, 2023-11-15)
Field: Materials Science — Materials Chemistry

A Novel Crystal Structure Prediction Using Hybrid Method Chemical compositions are used to predict the crystal structure in solid state of new materials. To finding the crystalline arrangements of materials for major unsolved problems in materials science for their chemical compositions. Crystal structure prediction is one of the foremost methods for discovering new materials. In this paper, we propose a deep and machine learning model for approach to classification of the crystal structure. The more than 5000 dataset were used to predict the crystal structure. In previous work, various machine learning models were used for predicting the crystal structure. In this work, we fuse the machine learning and deep neural network algorithm. The dataset were trained and tested to this crystal structure prediction. In this model we evaluate the dataset to classification the models with high accuracy. Our approach, ANB-NET(AlextNet with Naive Bayes) classifier get the best accuracy to predict the crystal structure and time complexity is less than other model. Crystal structure prediction ; Artificial intelligence ; Machine learning ; Naive Bayes classifier ; Artificial neural network ; Computer science ; Crystal structure ; Crystal (programming language) ; Classifier (UML) ; Algorithm ; Pattern recognition (psychology) ; Support vector machine ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Antoine Lancien | Guillaume Darlot | Jonathan Bassut | Aurélie Jouenne | Tao Jiang | Rénato Froidevaux | Sébastien Paul | Anne Zaparucha | Egon Heuson
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2023-11-10)
Field: Materials Science — Materials Chemistry

Hybrid catalysis, assisted by machine learning, for bio-sourced molecules functionalization Séminaire invité Surface modification ; Computer science ; Molecule ; Catalysis ; Combinatorial chemistry ; Nanotechnology ; Chemistry ; Materials science ; Chemical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Asymmetric Hydrogenation and Catalysis
Authors: Malte Esders | Gimmy Alex Fernandez Ramirez | Michael Gastegger | Satya Swarup Samal
Source: arXiv (Cornell University) (preprint, 2023-07-25)
Field: Materials Science — Materials Chemistry

Scaling up machine learning-based chemical plant simulation: A method for fine-tuning a model to induce stable fixed points Idealized first-principles models of chemical plants can be inaccurate. An alternative is to fit a Machine Learning (ML) model directly to plant sensor data. We use a structured approach: Each unit within the plant gets represented by one ML model. After fitting the models to the data, the models are connected into a flowsheet-like directed graph. We find that for smaller plants, this approach works well, but for larger plants, the complex dynamics arising from large and nested cycles in the flowsheet lead to instabilities in the solver during model initialization. We show that a high accuracy of the single-unit models is not enough: The gradient can point in unexpected directions, which prevents the solver from converging to the correct stationary state. To address this problem, we present a way to fine-tune ML models such that initialization, even with very simple solvers, becomes robust. Initialization ; Solver ; Computer science ; Scaling ; Chemical plant ; Simple (philosophy) ; Algorithm ; Graph ; Mathematics ; Theoretical computer science ; Engineering Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Control Systems Optimization
Authors: Keita Kobayashi | Hiroki Nakamura | Mitsuhiro Itakura | Masahiko Machida | Masahiko Okumura
Source: Materia Japan (article, 2023-02-28)
Field: Materials Science — Materials Chemistry

Machine Learning Molecular Dynamics Simulations for Evaluation of High-Temperature Properties of Nuclear Fuel Materials Molecular dynamics ; Dynamics (music) ; Nuclear engineering ; Nuclear fuel ; Materials science ; Computer science ; Chemistry ; Physics ; Engineering ; Computational chemistry Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Michael Minotakis | Hugo Rossignol | Matteo Cobelli | Stefano Sanvito
Source: arXiv (Cornell University) (preprint, 2023-03-29)
Field: Materials Science — Materials Chemistry

Machine-Learning Surrogate Model for Accelerating the Search of Stable Ternary Alloys The prediction of phase diagrams in the search for new phases is a complex and computationally intensive task. Density functional theory provides, in many situations, the desired accuracy, but its throughput becomes prohibitively limited as the number of species involved grows, even when used with local and semi-local functionals. Here, we explore the possibility of integrating machine-learning models in the workflow for the construction of ternary convex hull diagrams. In particular, we train a set of spectral neighbour-analysis potentials (SNAPs) over readily available binary phases and we establish whether this is good enough to predict the energies of novel ternaries. Such a strategy does not require any new calculations specific for the construction of the model, but just avails of data stored in binary-phase-diagram repositories. We find that a so-constructed SNAP is capable of accurate total-energy estimates for ternary phases close to the equilibrium geometry but, in general, is not able to perform atomic relaxation. This is because during a typical relaxation path a given phase traverses regions in the parameter space poorly represented by the training set. Different metrics are then investigated to assess how an unknown structure is well described by a given SNAP model, and we find that the standard deviation of an ensemble of SNAPs provides a fast and non-specie-specific metric. Ternary operation ; Metric (unit) ; Computer science ; Relaxation (psychology) ; Convex hull ; Set (abstract data type) ; Binary number ; Statistical physics ; Algorithm ; Task (project management) ; Mathematical optimization ; Regular polygon ; Mathematics ; Physics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Zachary Gariepy | Zhiwen Chen | Isaac Tamblyn | Chandra Veer Singh | Conrard Giresse Tetsassi Feugmo
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Automatic graph representation algorithm for heterogeneous catalysis One of the most appealing aspects of machine learning for material design is its high throughput exploration of chemical spaces, but to reach the ceiling of ML-aided exploration, more than current model architectures and processing algorithms are required. New architectures such as Graph Neural Networks (GNNs) have seen significant research investments recently. For heterogeneous catalysis, defining substrate intramolecular bonds and adsorbate/substrate intermolecular bonds is a time-consuming and challenging process. Before applying a model, dataset pre-processing, node/bond descriptor design, and specific model constraints have to be considered. In this work, a framework designed to solve these issues is presented in the form of an automatic graph representation algorithm (AGRA) tool to extract the local chemical environment of metallic surface adsorption sites is presented. This tool is able to gather multiple adsorption geometry datasets composed of different systems and combine them into a single model. To show AGRA's excellent transferability and reduced computational cost compared to other graph representation methods, it was applied to 5 different catalytic reaction datasets and benchmarked against the Open Catalyst Projects (OCP) graph representation method. The two ORR datasets with O/OH adsorbates obtained 0.053 eV RMSD when combined together, whereas the three CO2RR datasets with CHO/CO/COOH obtained an average performance of 0.088 eV RMSD. To further display the algorithm's versatility and extrapolation ability, a model was trained on a subset combination of all 5 datasets with an RMSD of 0.105 eV. This universal model was then used to predict a wide range of adsorption energies and an entirely new ORR catalyst system and then verified through Density Functional Theory calculations Computer science ; Extrapolation ; Representation (politics) ; Graph ; Algorithm ; Transferability ; Theoretical computer science ; Machine learning ; Mathematics Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Mitsuru Ohno | Yoshihiro Hayashi | Qi Zhang | Yu Kaneko | Ryo Yoshida
Source: ChemRxiv (preprint, 2023-03-01)
Field: Materials Science — Materials Chemistry

SMiPoly: Generation of Synthesizable Polymer Virtual Library using Rule-based Polymerization Reactions Recent advances in machine learning have led to the rapid adoption of various computational methods for de novo molecular design in polymer research, including high-throughput virtual screening and inverse molecular design. In such workflows, molecular generators play an essential role in the creation or sequential modification of candidate polymer structures. Machine learning-assisted molecular design has made great technical progress over the past few years. However, its practical deployment has not progressed as much as expected. One reason for this is the difficulty in determining the synthetic routes to such designed polymers. To address this technical limitation, we present SMiPoly, a Python library for virtual polymer generation that implements 22 chemical rules for commonly applied polymerization reactions. For given small organic molecules to form a candidate monomer set, the SMiPoly generator conducts possible polymerization reactions to generate an exhaustive list of potentially synthesizable polymers. In this study, using 1,083 readily available monomers, we generated 169,347 unique polymers forming seven different molecular types: polyolefin, polyester, polyether, polyamide, polyimide, polyurethane, and polyoxazolidone. By comparing the distribution of the virtually created polymers with approximately 16,000 real polymers synthesized so far, it was found that the coverage and novelty of the SMiPoly-generated polymers can reach 48% and 53%, respectively. Polymer ; Polymerization ; Monomer ; Computer science ; Materials science ; Polymer science Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Shivank S. Shukla | Christopher Kuenneth | Rampi Ramprasad
Source: arXiv (Cornell University) (preprint, 2023-03-22)
Field: Materials Science — Materials Chemistry

Polymer Informatics Beyond Homopolymers Polymers are diverse and versatile materials that have met a wide range of material application demands. They come in several flavors and architectures, e.g., homopolymers, copolymers, polymer blends, and polymers with additives. Searching this enormous space for suitable materials with a specific set of property/performance targets is thus non-trivial, painstaking, and expensive. Such a search process can be made effective by the creation of rapid and accurate property predictors. In this work, we present a machine-learning framework to predict the thermal properties of homopolymers, copolymers, and polymer blends. A universal fingerprinting scheme capable of handling this entire polymer chemical class has been developed and a multi-task deep learning algorithm is trained simultaneously on a large dataset of glass transition, melting, and degradation temperatures. The developed models are accurate, fast, flexible, and scalable to other properties when suitable data become available. Polymer ; Copolymer ; Materials science ; Computer science ; Materials informatics ; Process (computing) ; Glass transition ; Scalability ; Property (philosophy) ; Set (abstract data type) ; Nanotechnology ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: HaoPeng Ai | Huihao Zhou | Qiong Gu | Jun Xu
Source:  (preprint, 2023-04-28)
Field: Materials Science — Materials Chemistry

Predicting 3D molecular structures based on the first-principles and crystal data with deep learning approaches Deep learning ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Kazuki Ishizaki | Toru Asahi | Takuya Taniguchi
Source: Acta Crystallographica Section A Foundations and Advances (article, 2023-08-22)
Field: Materials Science — General Materials Science

Machine learning-based material design and condition optimization of generating higher force of photo-actuation crystals Photo-actuation crystals are attracting attention as a new actuator material because they can be remotely controlled by light.For actuator materials, not only the mode of deformation (type of deformation, deformation degree, and response speed, etc.) but also the generating force are important.Generating force depends on experimental conditions such as crystal size and light intensity [1].The generating force also depends on the mechanical properties of the crystal (Young's modulus), it is necessary to search a wide range of conditions to find the optimum conditions to increase generating force.However, scanning through the vast experimental space of the generating force of photo-actuation crystals is time-consuming.Therefore, in this study, photo-actuation crystals with various Young's modulus were fabricated based on material design guidelines obtained by LASSO regression, and then conditions achieving larger generating force were explored by Bayesian optimization.To design photo-actuation crystals with various Young's moduli, we performed LASSO regression, which is one of the linear regression methods for variable reduction.Dataset included molecular structures and Young's modulus was constructed by collecting data from papers.LASSO regression was conducted to identify substituents correlated with Young's modulus (Fig. 1a).Eleven types of salicylideneamine crystals were prepared based on the finding of the LASSO regression.The conditions corresponding to a larger generating force were searched using Bayesian optimization.Bayesian optimization suggests the next experimental conditions based on the value of the acquisition function calculated by the predictions and the uncertainty of the predictions.Generating force F was measured under the condition of the Young's modulus E, crystal size S, and light intensity I presented by Bayesian optimization (Fig. 1b).Because it is easier to change the light intensity (I) than to change the crystal samples (E, S), the optimization of the light intensity (I) was performed without changing the crystal samples (E, S).In this research, a quadratic function was optimized to find the suitable intensity which affords maximum force of a specific crystal.After optimizing the irradiation intensity (I) for a particular crystal sample (E, S), we performed the same operation for the other crystal samples (E, S) presented by Bayesian optimization (Fig. 1b). Materials science ; Mechanical engineering ; Computer science ; Composite material ; Engineering Material Properties and Applications
Authors: Vahe Gharakhanyan | Max Aalto | Aminah Alsoulah | Nongnuch Artrith | Alexander Urban
Source: arXiv (Cornell University) (preprint, 2023-10-09)
Field: Materials Science — Materials Chemistry

Constructing and Compressing Global Moment Descriptors from Local Atomic Environments Local atomic environment descriptors (LAEDs) are used in the materials science and chemistry communities, for example, for the development of machine learning interatomic potentials. Despite the fact that LAEDs have been extensively studied and benchmarked for various applications, global structure descriptors (GSDs), i.e., descriptors for entire molecules or crystal structures, have been mostly developed independently based on other approaches. Here, we propose a systematically improvable methodology for constructing a space of representations of GSDs from LAEDs by incorporating statistical information and information about chemical elements. We apply the method to construct GSDs of varying complexity for lithium thiophosphate structures that are of interest as solid electrolytes and use an information-theoretic approach to obtain an optimally compressed GSD. Finally, we report the performance of the compressed GSD for energy prediction tasks. Construct (python library) ; Computer science ; Thiophosphate ; Space (punctuation) ; Theoretical computer science ; Moment (physics) ; Artificial intelligence ; Algorithm ; Statistical physics ; Chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions