jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Enhu Diao | Xuhong Liu | Qiang Tong | Xiulei Liu
Source: Research Square (preprint, 2022-07-18)
Field: Materials Science — Materials Chemistry

First Principles Data Driven Potentials for Prediction of Iron Carbide Clusters Abstract Iron carbide clusters are studied extensively in many catalysis fields by quantum chemistry methods. The high cost of structural energy calculation has always been the bottleneck of its development. First principles based on density functional theory (DFT) have high calculation accuracy and sound portability. Still, its computational cost is so high that it is difficult to carry out large-scale, long-term, and high-throughput simulations. A data driven potential is crucial for further accelerating the screening or prediction process in the high through-put calculation. In this paper, we generate 177k clusters data and choose some state-of-the-art machine learning models in physical chemistry to train them. The generated potential gives a very high prediction accuracy on the order of the structure stability and achieves better adaptability/tolerance on poor structures of clusters. In addition, we use the machine learning potential to assist in high-throughput data collection and realize the prediction of the adsorption position of hydrogen atoms on the cluster surface. We can get a more stable adsorption position of the hydrogen atom in a shorter time, compared with traditional quantum chemical calculators. Software portability ; Bottleneck ; Throughput ; Computer science ; Cluster (spacecraft) ; Coupled cluster ; Density functional theory ; Stability (learning theory) ; Machine learning ; Chemistry ; Computational chemistry ; Molecule Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Surface Chemistry and Catalysis
Authors: Е. А. Муравьева | В.В. Биткулов | А.И. Николаева
Source: Промышленные АСУ и контроллеры. (article, 2022-09-28)
Field: Chemistry — Spectroscopy

Development of a neural network to control the polymerization of isoprene in an isopentane solution using a virtual analyzer Isopentane ; Isoprene ; Polymerization ; Spectrum analyzer ; Artificial neural network ; Computer science ; Polymer science ; Chemistry ; Process engineering ; Astrobiology ; Environmental science ; Biochemical engineering ; Engineering ; Organic chemistry ; Artificial intelligence ; Physics ; Polymer ; Catalysis ; Telecommunications Analytical Chemistry and Chromatography ; Various Chemistry Research Topics ; Advanced Control Systems Optimization
Authors: Bappaditya Dey | Sandip Halder | Argho Das | SAYANTAN DAS | STEWART WU | Germain Fenger
Source:  (article, 2022-09-26)
Field: Materials Science — Surfaces, Coatings and Films

Deep-learning denoiser-assisted framework for robust SEM contour extraction and analysis for advanced semiconductor node Computer science ; Node (physics) ; Extraction (chemistry) ; Semiconductor ; Artificial intelligence ; Materials science ; Electronic engineering ; Optoelectronics ; Engineering ; Acoustics ; Physics ; Chemistry Electron and X-Ray Spectroscopy Techniques ; Industrial Vision Systems and Defect Detection ; Advancements in Photolithography Techniques
Authors: Francesc Font-Clos | Marco Zanchi | Stefan Hiemer | Silvia Bonfanti | Roberto Guerra | Michael Zaiser | Stefano Zapperi
Source: Nature Communications (article, 2022-05-20)
Field: Materials Science — Materials Chemistry

Predicting the failure of two-dimensional silica glasses Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices and components. Thanks to recent advances in deep learning, accurate failure predictions are becoming possible even for strongly disordered solids, but the sheer number of parameters used in the process renders a physical interpretation of the results impossible. Here we address this issue and use machine learning methods to predict the failure of simulated two dimensional silica glasses from their initial undeformed structure. We then exploit Gradient-weighted Class Activation Mapping (Grad-CAM) to build attention maps associated with the predictions, and we demonstrate that these maps are amenable to physical interpretation in terms of topological defects and local potential energies. We show that our predictions can be transferred to samples with different shape or size than those used in training, as well as to experimental images. Our strategy illustrates how artificial neural networks trained with numerical simulation results can provide interpretable predictions of the behavior of experimentally measured structures. Interpretation (philosophy) ; Exploit ; Computer science ; Process (computing) ; Artificial neural network ; Artificial intelligence ; Relevance (law) ; Class (philosophy) ; Machine learning Machine Learning in Materials Science ; Composite Material Mechanics ; Data Visualization and Analytics
Authors: Nikita Fedik | R.I. Zubatyuk | Maksim Kulichenko | Nicholas Lubbers | Justin S. Smith | Benjamin Nebgen | Richard A. Messerly | Ying Wai Li | Alexander I. Boldyrev | Kipton Barros | Olexandr Isayev | Sergei Tretiak
Source: Nature Reviews Chemistry (review, 2022-11-16)
Field: Materials Science — Materials Chemistry

Publisher Correction: Extending machine learning beyond interatomic potentials for predicting molecular properties Computer science ; Artificial intelligence ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Fang Wu | Nicolas Courty | Jin, Shuting | Li, Stan Z.
Source: arXiv (Cornell University) (preprint, 2022-02-13)
Field: Materials Science — Materials Chemistry

Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport Training data are usually limited or heterogeneous in many chemical and biological applications. Existing machine learning models for chemistry and materials science fail to consider generalizing beyond training domains. In this article, we develop a novel optimal transport-based algorithm termed MROT to enhance their generalization capability for molecular regression problems. MROT learns a continuous label of the data by measuring a new metric of domain distances and a posterior variance regularization over the transport plan to bridge the chemical domain gap. Among downstream tasks, we consider basic chemical regression tasks in unsupervised and semi-supervised settings, including chemical property prediction and materials adsorption selection. Extensive experiments show that MROT significantly outperforms state-of-the-art models, showing promising potential in accelerating the discovery of new substances with desired properties. Computer science ; Machine learning ; Metric (unit) ; Generalization ; Artificial intelligence ; Regression ; Bridge (graph theory) ; Regularization (linguistics) ; Representation (politics) ; Variance (accounting) ; Kriging ; Mathematics ; Engineering ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Fuyuan Dong | Mingxu Zhang | Peng Fan | Jianglong Zhang
Source: Computational Intelligence and Neuroscience (article, 2022-09-26)
Field: Materials Science — Materials Chemistry

Preparation and Mechanical Properties of High Silicon Molybdenum Cast Iron Materials: Based on Deep Learning Model In the production and life of machines, wear is the main form of material failure. Many workpieces and equipment will produce friction and wear during use, causing energy and material consumption, resulting in huge economic losses. In the process of using engineering equipment, in order to improve the service life of the workpiece and improve the wear-resistant material, the research of the material itself must be closely linked with the wear mechanism. Among the antiwear materials, the application of high-chromium cast iron is the most common. The reason is that high-chromium cast iron has a series of advantages in performance, manufacturing technology, and price. However, high-chromium cast iron itself has high C content and high brittleness, resulting in defects in toughness and poor corrosion resistance. When working in harsh environments such as mechanical extrusion, high-strength wear, and chemical corrosion, cracking and deformation are prone to occur, corrosion and peeling, and another damage failure. High chromium cast iron must have a relatively stable structure, good strength and toughness, excellent wear resistance, and corrosion resistance. In this study, the static corrosion performance of high-chromium cast iron was tested to study the effects of Mo and V elements addition and content changes on corrosion resistance and vanadium, molybdenum, and subcritical treatment processes on the microstructure and properties of high-chromium cast iron, in order to obtain better performance. Lower cost high chromium cast iron castings. As a wear-resistant part, high-chromium cast iron castings are widely used in wear and can cope with harsh working environments, but their excellent hardness conditions bring certain defects in toughness and at the same time lack good corrosion resistance, in mechanical extrusion., high-strength wear, chemical corrosion, and other harsh environments are prone to damage and failure. Therefore, high-chromium cast iron should have dual properties, not only high hardness but also suitable for mud, sand, chemicals, and other media corrosion and wear resistance. Materials science ; Cast iron ; Metallurgy ; Chromium ; Corrosion ; Toughness ; Brittleness ; Microstructure ; Molybdenum Metal Alloys Wear and Properties ; Advanced materials and composites ; High Entropy Alloys Studies
Authors: Wenkai Fu | Steven R. Spurgeon | Chongmin Wang | Yuyan Shao | Wei Wang | Amra Peles
Source: arXiv (Cornell University) (preprint, 2022-05-23)
Field: Materials Science — Materials Chemistry

Deep-learning-based prediction of nanoparticle phase transitions during in situ transmission electron microscopy We develop the machine learning capability to predict a time sequence of in-situ transmission electron microscopy (TEM) video frames based on the combined long-short-term-memory (LSTM) algorithm and the features de-entanglement method. We train deep learning models to predict a sequence of future video frames based on the input of a sequence of previous frames. This unique capability provides insight into size dependent structural changes in Au nanoparticles under dynamic reaction condition using in-situ environmental TEM data, informing models of morphological evolution and catalytic properties. The model performance and achieved accuracy of predictions are desirable based on, for scientific data characteristic, based on limited size of training data sets. The model convergence and values for the loss function mean square error show dependence on the training strategy, and structural similarity measure between predicted structure images and ground truth reaches the value of about 0.7. This computed structural similarity is smaller than values obtained when the deep learning architecture is trained using much larger benchmark data sets, it is sufficient to show the structural transition of Au nanoparticles. While performance parameters of our model applied to scientific data fall short of those achieved for the non-scientific big data sets, we demonstrate model ability to predict the evolution, even including the particle structural phase transformation, of Au nano particles as catalyst for CO oxidation under the chemical reaction conditions. Using this approach, it may be possible to anticipate the next steps of a chemical reaction for emerging automated experimentation platforms. Similarity (geometry) ; Benchmark (surveying) ; Computer science ; Artificial intelligence ; Deep learning ; Transformation (genetics) ; Materials science ; Machine learning ; Image (mathematics) ; Chemistry ; Geology Machine Learning in Materials Science ; nanoparticles nucleation surface interactions
Authors: Mao Su | Ji‐Hui Yang | Hongjun Xiang | Xin-Gao Gong
Source: arXiv (Cornell University) (preprint, 2022-05-11)
Field: Materials Science — Materials Chemistry

Efficient determination of the Hamiltonian and electronic properties using graph neural network with complete local coordinates Despite the successes of machine learning methods in physical sciences, prediction of the Hamiltonian, and thus electronic properties, is still unsatisfactory. Here, based on graph neural network architecture, we present an extendable neural network model to determine the Hamiltonian from ab initio data, with only local atomic structures as inputs. Rotational equivariance of the Hamiltonian is achieved by our complete local coordinates. The local coordinates information, encoded using the convolutional neural network and designed to preserve Hermitian symmetry, is used to map hopping parameters onto local structures. We demonstrate the performance of our model using graphene and SiGe random alloys as examples. We show that our neural network model, although trained using small-size systems, can predict the Hamiltonian, as well as electronic properties such as band structures and densities of states (DOS) for large-size systems within the ab initio accuracy, justifying its extensibility. In combination with the high efficiency of our model, which takes only seconds to get the Hamiltonian of a 1728-atom system, present work provides a general framework to predict electronic properties efficiently and accurately, which provides new insights into computational physics and will accelerate the research for large-scale materials. Hamiltonian (control theory) ; Ab initio ; Artificial neural network ; Electronic structure ; Convolutional neural network ; Computer science ; Topology (electrical circuits) ; Statistical physics ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Mathematical optimization ; Combinatorics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Chengyue Gong | Lemeng Wu | Qiang Liu
Source: arXiv (Cornell University) (preprint, 2022-02-16)
Field: Materials Science — Materials Chemistry

How to Fill the Optimum Set? Population Gradient Descent with Harmless Diversity Although traditional optimization methods focus on finding a single optimal solution, most objective functions in modern machine learning problems, especially those in deep learning, often have multiple or infinite numbers of optima. Therefore, it is useful to consider the problem of finding a set of diverse points in the optimum set of an objective function. In this work, we frame this problem as a bi-level optimization problem of maximizing a diversity score inside the optimum set of the main loss function, and solve it with a simple population gradient descent framework that iteratively updates the points to maximize the diversity score in a fashion that does not hurt the optimization of the main loss. We demonstrate that our method can efficiently generate diverse solutions on a variety of applications, including text-to-image generation, text-to-mesh generation, molecular conformation generation and ensemble neural network training. Gradient descent ; Set (abstract data type) ; Computer science ; Mathematical optimization ; Local optimum ; Optimization problem ; Population ; Function (biology) ; Frame (networking) ; Focus (optics) ; Variety (cybernetics) ; Descent (aeronautics) ; Simple (philosophy) ; Artificial intelligence ; Artificial neural network ; Algorithm ; Mathematics ; Engineering Machine Learning in Materials Science ; Machine Learning and Data Classification
Authors: Kristof T. Schütt | Stefaan S. P. Hessmann | Niklas W. A. Gebauer | Jonas Lederer | Michael Gastegger
Source: arXiv (Cornell University) (preprint, 2022-12-11)
Field: Materials Science — Materials Chemistry

SchNetPack 2.0: A neural network toolbox for atomistic machine learning SchNetPack is a versatile neural networks toolbox that addresses both the requirements of method development and application of atomistic machine learning. Version 2.0 comes with an improved data pipeline, modules for equivariant neural networks as well as a PyTorch implementation of molecular dynamics. An optional integration with PyTorch Lightning and the Hydra configuration framework powers a flexible command-line interface. This makes SchNetPack 2.0 easily extendable with custom code and ready for complex training task such as generation of 3d molecular structures. Toolbox ; Computer science ; Artificial neural network ; Pipeline (software) ; Interface (matter) ; Artificial intelligence ; Task (project management) ; Deep learning ; Code generation ; Computer architecture ; Programming language ; Parallel computing ; Operating system ; Engineering ; Systems engineering Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics ; Mass Spectrometry Techniques and Applications
Authors: Lei Tao | John Byrnes | Vikas Varshney | Ying Li
Source: SSRN Electronic Journal (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning Strategies for the Structure-Property Relationship of Copolymers Copolymer ; Property (philosophy) ; Materials science ; Computer science ; Polymer ; Philosophy ; Epistemology ; Composite material Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Jules Schleinitz | Maxime Langevin | Yanis Smail | Benjamin Wehnert | Laurence Grimaud | Rodolphe Vuilleumier
Source: ChemRxiv (preprint, 2022-05-18)
Field: Materials Science — Materials Chemistry

Guidelines for Machine Learning Yield Prediction from Small-Size Literature Dataset Synthetic yield prediction using machine learning is intensively studied. Previous work focused on two categories of datasets: High-Throughput Experimentation data, as an ideal case study and datasets extracted from proprietary databases, which are known to have a strong reporting bias towards high yields. However, predicting yields using published reaction data remains elusive. To fill the gap, we built a dataset on nickel-catalyzed cross-couplings extracted from organic reaction publications, including scope and optimization information. We demonstrate the importance of including optimization data as a source of failed experiments and emphasize how publication constraints shape the exploration of the chemical space by the synthetic community. While machine learning models still fail to perform out-of-sample predictions, this work shows that adding chemical knowledge enables fair predictions in a low-data regime. Eventually, we hope that this unique public database will foster further improvements of machine learning methods for reaction yield prediction in a more realistic context. Machine learning ; Computer science ; Yield (engineering) ; Context (archaeology) ; Scope (computer science) ; Artificial intelligence ; Ideal (ethics) ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Ryan Jacobs | Priyam Patki | Matthew Lynch | Steven Chen | Dane Morgan | Kevin G. Field
Source: arXiv (Cornell University) (preprint, 2022-08-02)
Field: Materials Science — Materials Chemistry

Materials Swelling Revealed Through Automated Semantic Segmentation of Cavities in Electron Microscopy Images Accurately quantifying swelling of alloys that have undergone irradiation is essential for understanding alloy performance in a nuclear reactor and critical for the safe and reliable operation of reactor facilities. However, typical practice is for radiation-induced defects in electron microscopy images of alloys to be manually quantified by domain-expert researchers. Here, we employ an end-to-end deep learning approach using the Mask Regional Convolutional Neural Network (Mask R-CNN) model to detect and quantify nanoscale cavities in irradiated alloys. We have assembled the largest database of labeled cavity images to date, which includes 400 images, &gt;34k discrete cavities, and numerous alloy compositions and irradiation conditions. We have evaluated both statistical (precision, recall, and F1 scores) and materials property-centric (cavity size, density, and swelling) metrics of model performance, and performed in-depth analysis of materials swelling assessments. We find our model gives assessments of material swelling with an average (standard deviation) swelling mean absolute error based on random leave-out cross-validation of 0.30 (0.03) percent swelling. This result demonstrates our approach can accurately provide swelling metrics on a per-image and per-condition basis, which can provide helpful insight into material design (e.g., alloy refinement) and impact of service conditions (e.g., temperature, irradiation dose) on swelling. Finally, we find there are cases of test images with poor statistical metrics, but small errors in swelling, pointing to the need for moving beyond traditional classification-based metrics to evaluate object detection models in the context of materials domain applications. Swelling ; Materials science ; Convolutional neural network ; Context (archaeology) ; Computer science ; Irradiation ; Standard deviation ; Artificial intelligence ; Composite material ; Statistics ; Physics ; Mathematics ; Geology Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ion-surface interactions and analysis
Authors: Yaowen Yang | Jihan Zhou | Zipeng Zhao | Geng Sun | Saman Moniri | Colin Ophus | Yongsoo Yang | Ziyang Wei | Yakun Yuan | Cheng Zhu | Qiang Sun | Qingying Jia | Hendrik Heinz | Jim Ciston | Peter Ercius | Philippe Sautet | Yu Huang | Jianwei Miao
Source: arXiv (Cornell University) (preprint, 2022-02-18)
Field: Materials Science — Surfaces, Coatings and Films

Atomic-scale identification of the active sites of nanocatalysts Alloy nanocatalysts have found broad applications ranging from fuel cells to catalytic converters and hydrogenation reactions. Despite extensive studies, identifying the active sites of nanocatalysts remains a major challenge due to the heterogeneity of the local atomic environment. Here, we advance atomic electron tomography to determine the 3D local atomic structure, surface morphology and chemical composition of PtNi and Mo-doped PtNi nanocatalysts. Using machine learning trained by density functional theory calculations, we identify the catalytic active sites for the oxygen reduction reaction from experimental 3D atomic coordinates, which are corroborated by electrochemical measurements. By quantifying the structure-activity relationship, we discover a local environment descriptor to explain and predict the catalytic active sites at the atomic level. The ability to determine the 3D atomic structure and chemical species coupled with machine learning is expected to expand our fundamental understanding of a wide range of nanocatalysts. Nanomaterial-based catalyst ; Catalysis ; Atomic units ; Density functional theory ; Nanotechnology ; Active site ; Materials science ; Oxygen reduction reaction ; Chemical physics ; Chemistry ; Electrochemistry ; Physical chemistry ; Nanoparticle ; Computational chemistry ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Materials Characterization Techniques ; Machine Learning in Materials Science