jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin Šípka | Andreas Erlebach | Lukáš Grajciar
Source: arXiv (Cornell University) (preprint, 2022-03-15)
Field: Materials Science — Materials Chemistry

Understanding chemical reactions via variational autoencoder and atomic representations On the time scales accessible to atomistic numerical modelling, chemical reactions are considered rare events. Atomistic simulations are typically biased along a low-dimensional representation of a chemical reaction in an atomic structure space, i.e., along the collective variable, to accelerate sampling of these improbable events. However, suitable collective variables are often complicated to guess due to the complexity of the transitions. Therefore, we present an automatic method of generating robust collective variables from atomic representation vectors, using either fixed Behler-Parrinello functions or representations extracted from pre-trained machine learning potentials. Variational autoencoder with these representations as inputs is trained while its latent space with arbitrary dimension gives us the set of collective variables. The resulting collective variables inherit all necessary invariances from the atomic representations and can be trained entirely unsupervised. The method's effectiveness is demonstrated using three different chemical reactions, one being the complex hydrolysis of a heterogeneous aluminosilicate catalyst. Lastly, we consider the method in the context of unseen atomic structure prediction, efficiently creating structures for different values of collective variables in a generative model fashion. Autoencoder ; Representation (politics) ; Dimension (graph theory) ; Context (archaeology) ; Space (punctuation) ; Computer science ; Variable (mathematics) ; Set (abstract data type) ; Chemical space ; Statistical physics ; Artificial intelligence ; Mathematics ; Chemistry ; Physics ; Artificial neural network ; Pure mathematics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Source: Focus on Catalysts (article, 2022-08-26)
Field: Chemistry — Inorganic Chemistry

Qingdao Jinneng New Material opens new Catofin PDH plant in Qingdao Crystallization ; Zeolite ; Transformation (genetics) ; Domain (mathematical analysis) ; Materials science ; Process engineering ; Computer science ; Chemical space ; Chemical engineering ; Nanotechnology ; Biochemical engineering ; Biological system ; Chemistry ; Engineering ; Mathematics ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; Polyoxometalates: Synthesis and Applications ; Mesoporous Materials and Catalysis
Authors: Jaeho Song | Haechan Jo | Dongwoo Lee
Source: arXiv (Cornell University) (preprint, 2022-10-12)
Field: Materials Science — Materials Chemistry

Experimental data management platform for data-driven investigation of combinatorial alloy thin films Experimental materials data are heterogeneous and include a variety of metadata for processing and characterization conditions, making the implementation of data-driven approaches for developing novel materials difficult. In this paper, we introduce the Thin-Film Alloy Database (TFADB), a materials data management platform, designed for combinatorially investigated thin-film alloys through various experimental tools. Using TFADB, researchers can readily upload, edit, and retrieve multidimensional experimental alloy data, such as composition, thickness, X-ray Diffraction, electrical resistivity, nanoindentation, and image data. Furthermore, composition-dependent properties from the database can easily be managed in a format adequate to be preprocessed for machine learning analyses. High flexibility of the software allows management of new types of materials data that can be potentially acquired from new combinatorial experiments. Metadata ; Computer science ; Data management ; Flexibility (engineering) ; Upload ; Alloy ; Variety (cybernetics) ; Thin film ; Software ; Experimental data ; Materials science ; Data type ; Computational science ; Database ; Nanotechnology ; Artificial intelligence ; World Wide Web ; Programming language Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Haidong Xie | Xueshuang Xiang | Yuanqing Chen
Source: Journal of Physics Condensed Matter (article, 2022-11-24)
Field: Materials Science — Materials Chemistry

Improving the deconvolution of spectra at finite temperatures by replacing spectrum with a neural network Abstract In condensed matter physics studies, spectral information plays an important role in understanding the composition of materials. However, it is difficult to obtain a material’s spectrum information directly through experiments or simulations. For example, the spectral information deconvoluted by scanning tunneling spectroscopy suffers from the temperature broadening effect, which is a known ill-posed problem and makes the deconvolution results unstable. Existing methods, such as the maximum entropy method, tend to select an appropriate regularization to suppress unstable oscillations. However, the choice of regularization is difficult, and oscillations are not completely eliminated. We believe that the possible improvement direction is to pay different attention to different intervals. Combining stochastic optimization and deep learning, in this paper, we introduce a neural network-based strategy to solve the deconvolution problem. Because the neural network can represent any nonuniform piecewise linear function, our method replaces the target spectrum with a neural network and can find a better approximation solution through an accurate and efficient optimization. Experiments on theoretical datasets using superconductors demonstrate that the superconducting gap is more accurately estimated and oscillates less. Plug in real experimental data, our approach obtains clearer results for material analysis. Deconvolution ; Artificial neural network ; Regularization (linguistics) ; Discretization ; Computer science ; Algorithm ; Blind deconvolution ; Inverse problem ; Mathematical optimization ; Applied mathematics ; Statistical physics ; Mathematics ; Artificial intelligence ; Physics ; Mathematical analysis Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Advanced Condensed Matter Physics
Authors: Jesper Byggmästar | K. Nordlund | Flyura Djurabekova
Source: arXiv (Cornell University) (preprint, 2022-03-16)
Field: Materials Science — Materials Chemistry

Simple machine-learned interatomic potentials for complex alloys Developing data-driven machine-learning interatomic potentials for materials containing many elements becomes increasingly challenging due to the vast configuration space that must be sampled by the training data. We study the learning rates and achievable accuracy of machine-learning interatomic potentials for many-element alloys with different combinations of descriptors for the local atomic environments. We show that for a five-element alloy system, potentials using simple low-dimensional descriptors can reach meV/atom-accuracy with modestly sized training datasets, significantly outperforming the high-dimensional SOAP descriptor in data efficiency, accuracy, and speed. In particular, we develop a computationally fast machine-learned and tabulated Gaussian approximation potential (tabGAP) for Mo-Nb-Ta-V-W alloys with a combination of two-body, three-body, and a new simple scalar many-body density descriptor based on the embedded atom method. Simple (philosophy) ; Interatomic potential ; Atom (system on chip) ; Gaussian ; Computer science ; Training set ; Scalar (mathematics) ; Algorithm ; Artificial intelligence ; Machine learning ; Element (criminal law) ; Physics ; Molecular dynamics ; Mathematics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Quantum many-body systems
Authors: Lihao Chen | Shuopu Wang | Chen Zou | Jianchun Xu | Ben Xu | Ke Bi
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Influence of Molecular Layered Structure on Thermoelectric Properties of Materials Thermoelectric effect ; Thermoelectric materials ; Seebeck coefficient ; Materials science ; Material properties ; Condensed matter physics ; Thermal conductivity ; Composite material ; Thermodynamics ; Physics Advanced Thermoelectric Materials and Devices ; nanoparticles nucleation surface interactions
Authors: Yahaya Ibrahim
Source: UMYU Scientifica (article, 2022-09-30)
Field: Materials Science — Materials Chemistry

Sampling of thermodynamic systems with neural network surrogates Traditional sampling methods such as the Monte Carlo method are computationally expensive and not feasible for studying large and complex systems. These methods are essential for developing new materials, optimizing chemical reactions, and understanding biological processes. However, simulating thermodynamic systems for physically relevant system sizes is computationally challenging. This is partly due to the exponential growth of the configuration space with the system size. With the current Monte Carlo methods, studying the same system for different investigation of its properties means repeating the expensive computation multiple times. In this article, I showed that thermodynamic systems can be sampled using a surrogate neural network model thereby avoiding the computationally expensive proposal Monte Carlo methods for subsequent investigations. To demonstrate the method, I trained a feed-forward neural network surrogate for the Boltzmann distribution of the Ising model. This approach would potentially help accelerate Monte Carlo simulations towards understanding the physics of novel materials and some biological processes. Monte Carlo method ; Computer science ; Monte Carlo molecular modeling ; Artificial neural network ; Monte Carlo method in statistical physics ; Computation ; Statistical physics ; Sampling (signal processing) ; Hybrid Monte Carlo ; Boltzmann machine ; Artificial intelligence ; Algorithm ; Markov chain Monte Carlo ; Mathematics ; Physics ; Statistics Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Protein Structure and Dynamics
Authors: Tae-Gwan Kim | Eun‐Gyeong Kim | M. Shaheer Akhtar | O–Bong Yang
Source: 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC) (article, 2022-06-05)
Field: Materials Science — Materials Chemistry

Prediction of Novel Phosphors using Machine Learning for Efficiency Enhancement of Silicon Solar Cells In order to achieve the theoretical efficiency over 30%, the mismatch between solar spectrum and spectral response of Si solar cells is needed to improve. In general, the thermalization of charge carriers, that usually generated by the absorption of photons having higher energy greater than a bandgap of semiconductor is the most common loss mechanism in Si solar cells. To control the thermalization of charge carriers, the introduction of sensitizer ions (such as Eu2+) into phosphor fluorescence materials can enhance the absorption of photons with lower energy. In this work, BaMgAI10O17:Eu+2, SrMgAI10O17:Eu+2nano-phosphors were synthesized by solution combustion method. The synthesized phosphors were extensively analyzed in terms of morphology, crystal structure, compositional, photoluminescence, and electrical properties by various analytical tools like X-ray Diffraction (XRD), Field Emission Scanning Electron Microscope-Energy Dispersive X-ray Spectrometer (FESEM-EDX), PL Spectroscopy, and quantum efficiency analyzer, etc. The database were extracted from raw data to enable the application of algorithms for machine learning training. From optimized regression model like Random Forest Regressor, Gradient Boosting, we can predict appropriate crystal structure and desirable wavelength of phosphors. The establishment of predicted models is efficient to work, so researchers can consider to find the potential materials such as ZnMgAI10O17 and CaMgAI10O17 in advance and save the huge demand for energy resources. It is hope that this work can facilitate the development of desirable synthesis methods under various conditions for phosphors with minimal light scattering, and good luminous efficiency which may helpful for improving the performance of Si solar cells. Phosphor ; Materials science ; Photoluminescence ; Optoelectronics ; Solar cell efficiency ; Quantum efficiency ; Thermalisation ; Absorption (acoustics) ; Band gap ; Solar cell ; Optics ; Computer science ; Physics Luminescence Properties of Advanced Materials ; Solid-state spectroscopy and crystallography ; Machine Learning in Materials Science
Authors: Hazem Daoud | Dhruv Sirohi | Endri Mjeku | John J. Feng | Saeed Oghbaey | R. J. Dwayne Miller
Source: arXiv (Cornell University) (preprint, 2022-10-13)
Field: Materials Science — Materials Chemistry

Novel applications of Generative Adversarial Networks (GANs) in the analysis of ultrafast electron diffraction (UED) images Inferring transient molecular structural dynamics from diffraction data is an ambiguous task that often requires different approximation methods. In this paper we present an attempt to tackle this problem using machine learning. While most recent applications of machine learning for the analysis of diffraction images apply only a single neural network to an experimental dataset and train it on the task of prediction, our approach utilizes an additional generator network trained on both synthetic data and experimental data. Our network converts experimental data into idealized diffraction patterns from which information is extracted via a convolutional neural network (CNN) trained on synthetic data only. We validate this approach on ultrafast electron diffraction (UED) data of bismuth samples undergoing thermalization upon excitation via 800 nm laser pulses. The network was able to predict transient temperatures with a deviation of less than 6% from analytically estimated values. Notably, this performance was achieved on a dataset of 408 images only. We believe employing this network in experimental settings where high volumes of visual data are collected, such as beam lines, could provide insights into the structural dynamics of different samples. Computer science ; Diffraction ; Ultrashort pulse ; Convolutional neural network ; Ultrafast electron diffraction ; Artificial neural network ; Artificial intelligence ; Pattern recognition (psychology) ; Optics ; Electron diffraction ; Laser ; Physics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Alexandr Fonari | Farshad Fallah | Michael Rauch
Source: Proceedings of the Python in Science Conferences (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Utilizing SciPy and other open source packages to provide a powerful API for materials manipulation in the Schrödinger Materials Suite The use of several open source scientific packages in the Schrdinger Materials Science Suite will be discussed. A typical workflow for materials discovery will be described, discussing how open source packages have been incorporated at every stage. Some recent implementations of machine learning for materials discovery will be discussed, as well as how open source packages were leveraged to achieve results faster and more efficiently. Suite ; Open source ; Workflow ; Computer science ; Implementation ; Software engineering ; Operating system ; Software ; Database Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Lei Lan | Chia‐Hao Liu | Qiang Du | Simon J. L. Billinge
Source: arXiv (Cornell University) (preprint, 2022-02-24)
Field: Materials Science — Materials Chemistry

Robustness test of the spacegroupMining model for determining space groups from atomic pair distribution function data Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated using a fixed set of parameters that reflect specific experimental conditions, and the accuracy of the model when given PDFs generated with different choices of these parameters is unknown. In this paper, we report that the results of the top-1 accuracy and top-6 accuracy are robust when applied to PDFs of different choices of experimental parameters $r_\text{max}$, $Q_\text{max}$, $Q_\text{damp}$ and atomic displacement parameters. Robustness (evolution) ; Convolutional neural network ; Algorithm ; Function (biology) ; Distribution (mathematics) ; Test data ; Data set ; Computer science ; Mathematics ; Statistical physics ; Artificial intelligence ; Physics ; Mathematical analysis ; Chemistry X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity
Authors: Taufik Aditiyawarman | Johny Wahyuadi Soedarsono | Agus Paul Setiawan Kaban | Rini Riastuti | Haryo Rahmadani | Mohammad Pribadi | Rizal Tresna Ramdhani | Sidhi Aribowo | Suryadi Suryadi
Source: Eastern-European Journal of Enterprise Technologies (article, 2022-06-30)
Field: Materials Science — Metals and Alloys

Risk analysis of Ex-spool 16” mol: an insight of machine learning and experimental result The paper reports the development of a Risk-Based Inspection (RBI)-Machine Learning perspective. The Optical Emission Spectrometry (OES), Tensile and Hardness Test, Scanning Electron Microscope (SEM), Energy Dispersive X-Ray Spectroscopy (EDS), Sulfate Reducing Bacteria Check, and X-Ray Diffraction (XRD) was used to analyze the root cause of the pipeline’s failure. Corrosion attack shows at the cross-section microstructure based on SEM results. Carbon, Manganese, Phosphorous, and sulfur’s chemical composition is dramatically lower than the standard API 5L Grade X42. Siderite and hematite dominate the composition of the corroded area as a result of CO2 dissolving in water. In contrast, hematite is generated due to the pipe and outdoor atmosphere reaction. Severe local wall thinning of the sand abrasion causes the degradation of the material’s mechanical properties and increases the corrosion rate. This result amplifies by the development of Machine Learning (ML) of Pearson Multicollinear Matrix and Supervised ML (Random Forest, Support Vector Machine, and Linear Regression) to estimate the corrosion degradation of the material. The source of datasets provided by ILI inspection includes the calculated PoF Remaining Useful Life (RuL) as input data, while Probability of Failure (PoF) prediction serves as output data. The Random Forest shows superior predictions of 92.18 %, with the lowest validation loss of 0.0316. The modeling result confirms the experimental outcome. This work demonstrates the implementation strategy to reduce the analysis time, minimize human bias, and serve as a reliable reference tool and guideline to maintain the integrity of the subsea pipelines. Scanning electron microscope ; Abrasion (mechanical) ; Materials science ; Corrosion ; Least squares support vector machine ; Support vector machine ; Environmental science ; Computer science ; Metallurgy ; Composite material ; Machine learning Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Structural Integrity and Reliability Analysis
Authors: Oriel Kiss | Francesco Tacchino | S. Vallecorsa | Ivano Tavernelli
Source: Machine Learning Science and Technology (article, 2022-07-15)
Field: Materials Science — Materials Chemistry

Quantum neural networks force fields generation Abstract Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales. In the last decade, machine learning (ML) methods have demonstrated impressive performances in predicting accurate values for energy and forces when trained on finite size ensembles generated with ab initio techniques. At the same time, quantum computers have recently started to offer new viable computational paradigms to tackle such problems. On the one hand, quantum algorithms may notably be used to extend the reach of electronic structure calculations. On the other hand, quantum ML is also emerging as an alternative and promising path to quantum advantage. Here we follow this second route and establish a direct connection between classical and quantum solutions for learning neural network (NN) potentials. To this end, we design a quantum NN architecture and apply it successfully to different molecules of growing complexity. The quantum models exhibit larger effective dimension with respect to classical counterparts and can reach competitive performances, thus pointing towards potential quantum advantages in natural science applications via quantum ML. Quantum ; Computer science ; Quantum machine learning ; Artificial neural network ; Quantum computer ; Artificial intelligence ; Statistical physics ; Theoretical computer science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Hongjun Xiang | Yang Zhong | Hongyu Yu | Xinggao Gong
Source: Research Square (preprint, 2022-08-03)
Field: Materials Science — Materials Chemistry

A general tensor prediction framework based on graph neural networks Abstract Graph neural networks (GNNs) have shown extremely high flexibility and accuracy in predicting the physical properties of molecules and crystals. However, traditional invariant GNNs are not compatible with directional properties, which currently limits their application only to the prediction of invariant scalar properties. To address this issue, here we propose a general framework, i.e., an edge-based tensor prediction graph neural network (ETGNN), where a tensor is expressed as the linear combination of the local spatial components projected on the edge directions of multiple-sized clusters. This tensor decomposition is rotationally equivariant and exactly satisfies the symmetry of the local structures, while the coefficients before each local spatial component are rotationally invariant scalars which can be easily predicted by traditional invariant GNNs. The accuracy and universality of our new framework are demonstrated by the successful prediction of various tensor properties from first-order to third-order. The framework proposed in this work will enable GNNs to step into the broad field of the prediction of directional properties. Computer science ; Tensor (intrinsic definition) ; Graph ; Artificial neural network ; Artificial intelligence ; Mathematics ; Theoretical computer science ; Pure mathematics Machine Learning in Materials Science
Authors: Hyun Park | Emad Tajkhorshid
Source: Biophysical Journal (article, 2022-02-01)
Field: Materials Science — Materials Chemistry

Novel deep learning energy-based model to predict protein dynamics Dynamics (music) ; Computer science ; Artificial intelligence ; Energy (signal processing) ; Deep learning ; Biological system ; Statistical physics ; Physics ; Mathematics ; Biology ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods