jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Isaiah A. Moses | Chengyin Wu | Wesley F. Reinhart
Source: arXiv (Cornell University) (preprint, 2023-12-21)
Field: Materials Science — Materials Chemistry

Crystal Growth Characterization of WSe$_2$ Thin Film Using Machine Learning Materials characterization remains a labor-intensive process, with a large amount of expert time required to post-process and analyze micrographs. As a result, machine learning has become an essential tool in materials science, including for materials characterization. In this study, we perform an in-depth analysis of the prediction of crystal coverage in WSe$_2$ thin film atomic force microscopy (AFM) height maps with supervised regression and segmentation models. Regression models were trained from scratch and through transfer learning from a ResNet pretrained on ImageNet and MicroNet to predict monolayer crystal coverage. Models trained from scratch outperformed those using features extracted from pretrained models, but fine-tuning yielded the best performance, with an impressive 0.99 $R^2$ value on a diverse set of held-out test micrographs. Notably, features extracted from MicroNet showed significantly better performance than those from ImageNet, but fine-tuning on ImageNet demonstrated the reverse. As the problem is natively a segmentation task, the segmentation models excelled in determining crystal coverage on image patches. However, when applied to full images rather than patches, the performance of segmentation models degraded considerably, while the regressors did not, suggesting that regression models may be more robust to scale and dimension changes compared to segmentation models. Our results demonstrate the efficacy of computer vision models for automating sample characterization in 2D materials while providing important practical considerations for their use in the development of chalcogenide thin films. Artificial intelligence ; Segmentation ; Scratch ; Computer science ; Characterization (materials science) ; Machine learning ; Process (computing) ; Pattern recognition (psychology) ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: Eisuke F. Sato | Gaku Tachiwaki | Mayu Fujii | Seiji Suga
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Machine Learning-Assisted Exploration of Optimal Conditions for Electrochemical Synthesis Using Microflow Reactor Machine learning-assisted organic synthesis has been receiving increasing attention in the past decade. In particular, we have focused on a Gaussian process regression, a kernel-based statistical learning algorithm, to predict optimal conditions of chemical reactions performed by an electrochemical microflow reactor. In this presentation, we will report that a catalytic amount of electricity enables efficient chemical transformations by using an electrochemical microflow reactor. Furthermore, applying a Gaussian process regression could construct the prediction models which suggest optimal reaction conditions with rational explanations. Figure 1 Kernel (algebra) ; Gaussian process ; Computer science ; Kriging ; Process (computing) ; Process engineering ; Support vector machine ; Chemical reactor ; Machine learning ; Gaussian ; Artificial intelligence ; Chemistry ; Engineering ; Chemical engineering ; Mathematics Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Su‐Mi Hur | Jihun Ahn | Gabriella Pasya Irianti | Yeojin Choe
Source: Research Square (preprint, 2023-09-19)
Field: Materials Science — Materials Chemistry

Enhancing Deep Learning Predictive Models with HAPPY (Hierarchically Abstracted rePeat unit of PolYmers) Representation <title>Abstract</title> We introduce HAPPY (Hierarchically Abstracted rePeat unit of PolYmers), a new string representation for polymers, designed to efficiently encapsulate essential polymer structure features for property prediction. HAPPY assigns single constituent elements for groups of sub-structures and employs grammatically complete and independent connectors between chemical linkages. Using a limited number of datasets, we trained neural networks represented by both HAPPY and conventional SMILES encoding of repeated unit structures and compared their performance in predicting five polymer properties: dielectric constant, glass transition temperature, thermal conductivity, solubility, and density. The results showed that the HAPPY-based network achieved higher prediction accuracy and two-fold faster training times. We further tested the robustness and versatility of HAPPY-based network with an augmented training dataset. Additionally, we present topo-HAPPY (Topological HAPPY), an extension that incorporates topological details of the constituent connectivity, leading to improved solubility and glass transition temperature prediction accuracy. Polymer ; Glass transition ; Robustness (evolution) ; Computer science ; Representation (politics) ; Dielectric ; Network structure ; Artificial intelligence ; Solubility ; Artificial neural network ; Topology (electrical circuits) ; Materials science ; Machine learning ; Mathematics ; Chemistry ; Composite material ; Optoelectronics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: R. Sudo | Mikihiko Oogane
Source:  (article, 2023-05-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Prediction of Magnetocrystalline Anisotropy Constant in FeCoNi Alloys Using Machine Learning Using Machine Learning, the possibility of predicting small K<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> materials and exhaustive search, which is thought difficult with existing theoretical calculations, was verified for FeCoNi alloy systems. At the K<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ≠ 0 point for FeCoNi alloys of about 10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3~5</sup> (erg/cc), the results show that K<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> can be predicted with an error of 0.5 order of magnitude for virtually unknown compositions and temperatures, indicating the possibility of predicting small K<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> materials using machine learning. The results also show that it is possible to successfully predict how the K<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> = 0 line changes by structure-ordering (annealing samples), and exhaustively search for points where the sign of K<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> changes. Artificial intelligence ; Computer science ; Algorithm Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Magnetic properties of thin films
Authors: Sichao Li | Amanda S. Barnard
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Multi-Target Neural Network Predictions of Mxenes as High-Capacity Energy Storage Materials in a Rashomon Set MXenes ; Set (abstract data type) ; Artificial neural network ; Computer science ; Energy storage ; Energy (signal processing) ; Artificial intelligence ; Materials science ; Physics ; Mathematics ; Nanotechnology ; Statistics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Amina Diop | L. Ilsedore Cleeves | D. E. Anderson | Jamila Pegues | Adele Plunkett
Source: arXiv (Cornell University) (preprint, 2023-12-08)
Field: Chemistry — Spectroscopy

Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine Learning Techniques Molecular abundances in protoplanetary disks are highly sensitive to the local physical conditions, including gas temperature, gas density, radiation field, and dust properties. Often multiple factors are intertwined, impacting the abundances of both simple and complex species. We present a new approach to understanding these chemical and physical interdependencies using machine learning. Specifically we explore the case of CO modeled under the conditions of a generic disk and build an explanatory regression model to study the dependence of CO spatial density on the gas density, gas temperature, cosmic ray ionization rate, X-ray ionization rate, and UV flux. Our findings indicate that combinations of parameters play a surprisingly powerful role in regulating CO compared to any singular physical parameter. Moreover, in general, we find the conditions in the disk are destructive toward CO. CO depletion is further enhanced in an increased cosmic ray environment and in disks with higher initial C/O ratios. These dependencies uncovered by our new approach are consistent with previous studies, which are more modeling intensive and computationally expensive. Our work thus shows that machine learning can be a powerful tool not only for creating efficient predictive models, but also for enabling a deeper understanding of complex chemical processes. Cosmic ray ; Ionization ; COSMIC cancer database ; Astrophysics ; Interdependence ; Physics ; Work (physics) ; Computational physics ; Statistical physics ; Computer science ; Ion ; Thermodynamics Molecular Spectroscopy and Structure ; Atmospheric Ozone and Climate ; SAS software applications and methods
Authors: Jose Hobson Junchen Xiao
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-04-16)
Field: Materials Science — Polymers and Plastics

Flame retardant properties of metal hydroxide-based polymer composites: A machine learning approach With increasing the practical applications of polymeric materials, surging demands for the highperformance flame-retardant polymer composites are obvious. Accurately predicting the performance of flame-retardant materials is critical for expediting technology development. Here, we develop machine learning models based on Random Forest to create standard paradigms based on a selfgenerated comprehensive dataset consisting of 219 experimental data from the literature. integrated by plenty of randomly combined decision trees, the Random Forest algorithm is well known for its accurate predictability and easy interpretability. We predict the important flame-retardancy-evaluating parameters including time to ignition (TTI), peak heat release rate (pHRR), total heat release (THR) and flame retardancy index (FRI) of different polymer composites containing metal hydroxides as the main fire-retardant additive. Our optimized models achieve high accuracy with R-squared value of 0.81 for regression and over 0.85 for classification; mean absolute error is below 0.3 for all 4 models. The analysis of important features reveals that mass fraction of MH takes always the first place, and the influence of feature groups on target properties is usually multi-dimensional and complicated. Fire retardant ; Composite material ; Materials science ; Metal hydroxide ; Metal ; Polymer ; Hydroxide ; Chemical engineering ; Metallurgy ; Engineering Flame retardant materials and properties ; Environmental and Industrial Safety ; Thermal and Kinetic Analysis
Authors: Yuki Ide | Hayato Shirakura | Taichi Sano | Muthuchamy Murugavel | Yuya Inaba | Sheng Hu | Ichigaku Takigawa | Yasuhide Inokuma
Source: ChemRxiv (preprint, 2023-05-26)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Analysis of Molar and Enantiomeric Ratios and Reaction Yields Using Images of Solid Mixtures Visual observations are frequently used as a preliminary evaluation of the chemical contents of mixtures, but their accuracy largely depends on the observer’s experience and intuition, which are difficult to share. Here, we report component ratio pre-diction using image-based machine learning (ML), which is applicable to analysis of various solid mixtures, such as mix-tures of organics and inorganics, polymorphous crystals, and enantiomers. The trained model with 300 images could predict the sugar/dietary salt weight ratio from an image within 4% error. The ML prediction pipeline was shown to be broadly ap-plicable to polymorphic glycine, D/L-tartaric acid, and four-component systems. As an application demonstration, we also used our ML system to analyze yield of a solid-state decarboxylation reaction. These results demonstrated that accumulation of researchers’ experience derived from visual information can be shared as trained ML models and used as a quantitative analysis method. Artificial intelligence ; Chemistry ; Enantiomer ; Molar ratio ; Component (thermodynamics) ; Tartaric acid ; Biological system ; Computer science ; Organic chemistry ; Thermodynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Qiang Zou | Basu Dev Oli | Huimin Zhang | Joseph Benigno | Xin Li | Lian Li
Source: arXiv (Cornell University) (preprint, 2023-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deciphering alloy composition in superconducting single-layer FeSe1-xSx on SrTiO3(001) substrates by machine learning of STM/S data Scanning tunneling microscopy (STM) is a powerful technique for imaging atomic structure and inferring information on local elemental composition, chemical bonding, and electronic excitations. However, traditional methods of visual inspection can be challenging for such determination in multi-component alloys, particularly beyond the dilute limit due to chemical disorder and electronic inhomogeneity. One viable solution is to use machine learning to analyze STM data and identify patterns and correlations that may not be immediately apparent through visual inspection alone. Here, we apply this approach to determine the Se/S concentration in superconducting single-layer FeSe1-xSx alloy epitaxially grown on SrTiO3(100) substrate by molecular beam epitaxy. First, defect-related dI/dV tunneling spectra are identified by the K-means clustering method, followed by singular value decomposition to distinguish between those from S and Se. Such analysis provides an efficient and reliable determination of local elemental composition, and further reveals correlations of nanoscale chemical inhomogeneity to superconductivity in single-layer iron chalcogenide films. Superconductivity ; Scanning tunneling microscope ; Epitaxy ; Alloy ; Chalcogenide ; Substrate (aquarium) ; Materials science ; Layer (electronics) ; Condensed matter physics ; Molecular beam epitaxy ; Electronic structure ; Chemical physics ; Analytical Chemistry (journal) ; Chemistry ; Nanotechnology ; Optoelectronics ; Physics ; Metallurgy Iron-based superconductors research ; Surface and Thin Film Phenomena ; Physics of Superconductivity and Magnetism
Authors: Christian Venturella | Christopher Hillenbrand | Jiachen Li | Tianyu Zhu
Source: arXiv (Cornell University) (preprint, 2023-10-15)
Field: Materials Science — Materials Chemistry

Machine Learning Many-Body Green's Functions for Molecular Excitation Spectra We present a machine learning (ML) framework for predicting Green's functions of molecular systems, from which photoemission spectra and quasiparticle energies at quantum many-body level can be obtained. Kernel ridge regression is adopted to predict self-energy matrix elements on compact imaginary frequency grids from static and dynamical mean-field electronic features, which gives direct access to real-frequency many-body Green's functions through analytic continuation and Dyson's equation. Feature and self-energy matrices are represented in a symmetry-adapted intrinsic atomic orbital plus projected atomic orbital basis to enforce rotational invariance. We demonstrate good transferability and high data efficiency of proposed ML method across molecular sizes and chemical species by showing accurate predictions of density of states (DOS) and quasiparticle energies at the level of many-body perturbation theory (GW) or full configuration interaction. For the ML model trained on 48 out of 1995 molecules randomly sampled from the QM7 and QM9 datasets, we report the mean absolute errors of ML-predicted HOMO and LUMO energies to be 0.13 eV and 0.10 eV compared to GW@PBE0. We further showcase the capability of this method by applying the same ML model to predict DOS for significantly larger organic molecules with up to 44 heavy atoms. Quasiparticle ; Physics ; Perturbation theory (quantum mechanics) ; Excitation ; Quantum ; Density functional theory ; HOMO/LUMO ; Quantum mechanics ; Atomic physics ; Molecule Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Catalysis and Oxidation Reactions
Authors: V. A. Sotskov | Alexander V. Shapeev | Evgeny V. Podryabinkin
Source: arXiv (Cornell University) (preprint, 2023-06-06)
Field: Materials Science — Materials Chemistry

A machine learning potential-based generative algorithm for on-lattice crystal structure prediction We propose a method for crystal structure prediction based on a new structure generation algorithm and on-lattice machine learning interatomic potentials. Our algorithm generates the atomic configurations assigning atomic species to sites of the given lattice, and uses cluster expansion or low-rank potential to evaluate their energy. We demonstrate two benefits of such approach. First, our structure generation algorithm offers a ``smart'' configurational space sampling, targeting low-energy structures which significantly reduces computational costs. Second, the application of machine learning interatomic potentials significantly reduces the number of DFT calculations. We discuss how our algorithm resembles the latent diffusion models for image generation. We demonstrate the efficiency of our method by constructing the convex hull of Nb-Mo-Ta-W system, including binary and ternary Nb-W and Mo-Ta-W subsystems. We found new binary, ternary, and quaternary stable structures that are not reported in the AFLOW database which we choose as our baseline. Due to the computational efficiency of our method we anticipate that it can pave the way towards efficient high-throughput discovery of multicomponent materials. Crystal structure prediction ; Convex hull ; Algorithm ; Crystal structure ; Ternary operation ; Binary number ; Lattice (music) ; Computer science ; Interatomic potential ; Artificial intelligence ; Machine learning ; Computational science ; Regular polygon ; Mathematics ; Chemistry ; Crystallography ; Physics ; Molecular dynamics ; Computational chemistry ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Gaetano Settembre | Nicola Corriero | Nicoletta Del Buono | Flavia Esposito | Rosanna Rizzi
Source: Lecture notes in computer science (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Predicting Crystal Systems: A Brief Review and a Case Study Computer science ; Bottleneck ; Workflow ; Crystal (programming language) ; Machine learning ; Artificial intelligence ; Crystal system ; Crystal structure ; Algorithm ; Data mining ; Crystallography ; Database ; Chemistry ; Programming language X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Crystallography and molecular interactions
Authors: Yoona Yang | Michael Eldred | Judit Zádor | Habib N. Najm
Source: ChemRxiv (preprint, 2023-03-13)
Field: Materials Science — Materials Chemistry

Multifidelity neural network formulations for prediction of reactive molecular potential energy surfaces This paper focuses on the development of multifidelity modeling approaches using neural network surrogates, where training data arising from multiple model forms and resolutions are integrated to predict high-fidelity response quantities of interest at lower cost. We focus on the context of quantum chemistry and the integration of information from multiple levels of theory. Important foundations include the use of symmetry function-based atomic energy vector constructions as feature vectors for representing structures across families of molecules, and single-fidelity neural network training capabilities that learn the relationships needed to map feature vectors to potential energy predictions. These foundations are embedded within several multifidelity topologies that decompose the high-fidelity mapping into model-based components, including sequential formulations that admit a general nonlinear mapping across fidelities and discrepancy-based formulations that presume an additive decomposition. Methodologies are first explored and demonstrated on a pair of simple analytical test problems, and then deployed for potential energy prediction for C5H5 using B2PLYPD3 6-311++G(d,p) for high fidelity simulation data and Hartree Fock 6-31G for low fidelity data. For the common case of limited access to high-fidelity data, our computational results demonstrate that multifidelity neural network potential energy surface constructions achieve roughly an order of magnitude improvement, either in terms of test error reduction for equivalent total simulation cost or reduction in total cost for equivalent error. Artificial neural network ; Context (archaeology) ; Fidelity ; Reduction (mathematics) ; Computer science ; Feature (linguistics) ; Algorithm ; Artificial intelligence ; Topology (electrical circuits) ; Theoretical computer science ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Haoran Li | Shukai Wang | Minjie Chen
Source:  (article, 2023-10-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Compact Neural-Network Digital-Twin Models and Material Comparison for Power Magnetics Accurate modeling of magnetic behaviors, such as the hysteresis loop and core loss, is critical in the design and optimization of power magnetic components. However, these behaviors can be significantly influenced by various factors, such as excitation waveforms and operating conditions, which makes their precise modeling challenging. Neural-network-based power magnetics modeling has emerged as an effective approach for capturing complicated multi-variable non-linearity. This paper uses transformer-based encoder-projector-decoder neural network models to study the material characteristics under ideal excitations, which cannot be done with traditional methods. The neural network model functions as a digital-twin of the original material database with over 10000x smaller data size (e.g., from 3.8 GB to 204 kB) while accurately and rapidly reproducing the material characteristics under complex operating conditions. An online platform – MagNet-AI – is developed as an example to demonstrate the effectiveness of using neural networks as the digital-twins to study and compare power magnetics materials. Artificial neural network ; Computer science ; Transformer ; Power (physics) ; Waveform ; Electronic engineering ; Magnet ; Encoder ; Voltage ; Artificial intelligence ; Engineering ; Electrical engineering Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Magnetic Field Sensors Techniques