jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sherif Abdulkader Tawfik | Olexandr Isayev | Catherine Stampfl | Joseph G. Shapter | David A. Winkler | Michael J. Ford
Source: ChemRxiv (preprint, 2018-05-17)
Field: Materials Science — Materials Chemistry

Efficient Prediction of Structural and Electronic Properties of Hybrid 2D Materials Using DFT and Machine Learning Materials constructed from different van der Waals two-dimensional (2D) heterostructures offer a wide range of benefits, but these systems have been little studied because of their experimental and computational complextiy, and because of the very large number of possible combinations of 2D building blocks. The simulation of the interface between two different 2D materials is computationally challenging due to the lattice mismatch problem, which sometimes necessitates the creation of very large simulation cells for performing density-functional theory (DFT) calculations. Here we use a combination of DFT, linear regression and machine learning techniques in order to rapidly determine the interlayer distance between two different 2D heterostructures that are stacked in a bilayer heterostructure, as well as the band gap of the bilayer. Our work provides an excellent proof of concept by quickly and accurately predicting a structural property (the interlayer distance) and an electronic property (the band gap) for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties. van der Waals force ; Heterojunction ; Density functional theory ; Band gap ; Computer science ; Lattice (music) ; Bilayer ; Computation ; Materials science ; Work (physics) ; Statistical physics ; Machine learning ; Algorithm ; Physics ; Quantum mechanics ; Chemistry ; Optoelectronics ; Molecule 2D Materials and Applications ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Ivan S. Novikov | Yury V. Suleimanov | Alexander V. Shapeev
Source: arXiv (Cornell University) (article, 2018-05-30)
Field: Materials Science — Materials Chemistry

Automated Calculation of Thermal Rate Coefficients using Ring Polymer Molecular Dynamics and Machine-Learning Interatomic Potentials with Active Learning: Application to Thermally Activated Gas Phase Chemical Reactions. Thermal ; Gas phase ; Ring (chemistry) ; Polymer ; Molecular dynamics ; Chemical physics ; Phase (matter) ; Thermodynamics ; Materials science ; Chemistry ; Physical chemistry ; Computational chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; Various Chemistry Research Topics ; Process Optimization and Integration
Authors: Sherif Abdulkader Tawfik | Olexandr Isayev | Catherine Stampfl | Joseph G. Shapter | David W. Winkler | Michael J. Ford
Source: ChemRxiv (preprint, 2018-05-17)
Field: Materials Science — Materials Chemistry

Efficient prediction of structural and electronic properties of hybrid 2D materials using complementary DFT and machine learning approaches &lt;p&gt;There are now, in principle, a limitless number of hybrid van der Waals heterostructures that can be built from the rapidly growing number of two-dimensional layers. The key question is how to explore this vast parameter space in a practical way. Computational methods can guide experimental work however, even the most efficient electronic structure methods such as density functional theory, are too time consuming to explore more than a tiny fraction of all possible hybrid 2D materials. Here we demonstrate that a combination of DFT and machine learning techniques provide a practical method for exploring this parameter space much more efficiently than by DFT or experiment. As a proof of concept we applied this methodology to predict the interlayer distance and band gap of bilayer heterostructures. Our methods quickly and accurately predicted these important properties for a large number of hybrid 2D materials. This work paves the way for rapid computational screening of the vast parameter space of van der Waals heterostructures to identify new hybrid materials with useful and interesting properties.&lt;/p&gt; van der Waals force ; Density functional theory ; Computer science ; Key (lock) ; Heterojunction ; Space (punctuation) ; Parameter space ; Work (physics) ; Artificial intelligence ; Machine learning ; Statistical physics ; Computational science ; Mathematics ; Physics ; Quantum mechanics ; Molecule ; Geometry Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Steen Lysgaard | Paul C. Jennings | Jens S. Hummelshøj | Thomas Bligaard | Tejs Vegge
Source: ChemRxiv (preprint, 2018-12-10)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Genetic Algorithms for Computational Materials Search A machine learning model is used as a surrogate fitness evaluator in a genetic algorithm (GA) optimization of the atomic distribution of Pt-Au nanoparticles. The machine learning accelerated genetic algorithm (MLaGA) yields a 50-fold reduction of required energy calculations compared to a traditional GA. Genetic algorithm ; Computer science ; Algorithm ; Artificial intelligence ; Machine learning ; Reduction (mathematics) ; Surrogate model ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shaoping Xiao | Amaury Lendasse | Renjie Hu
Source:  (article, 2018-12-01)
Field: Materials Science — Materials Chemistry

Data-Enabled Computational Multiscale Method in Materials Science and Engineering In the community of computational materials science, one of the challenges in hierarchical multiscale modeling is information-passing from one scale to another, especially from the molecular model to the continuum model. A machine-learning-enhanced approach, proposed in this paper, provides an alternative solution. In the developed hierarchical multiscale method, molecular dynamics simulations in the molecular model are conducted first to generate datasets, which represents physical phenomena at the nanoscale. The datasets are then used to train neural networks for failure classification and stress regressions. Finally, the well-trained learning machines are implemented in the continuum model to study the mechanical behaviors of materials at the macroscale. Randomized neural networks are employed due to their computational efficiency. Multiscale modeling ; Computer science ; Artificial neural network ; Artificial intelligence ; Scale (ratio) ; Computational model ; Molecular dynamics ; Computational Science and Engineering ; Machine learning ; Bioinformatics Machine Learning in Materials Science ; Microstructure and mechanical properties ; Composite Material Mechanics
Authors: Masato Sumita | Xiufeng Yang | Shinsuke Ishihara | Ryo Tamura | Koji Tsuda
Source: ChemRxiv (preprint, 2018-04-04)
Field: Materials Science — Materials Chemistry

Hunting for Organic Molecules with Artificial Intelligence: Molecules Optimized for Desired Excitation Energies This work presents a proof-of-concept study in artificial-intelligence-assisted (AI-assisted) chemistry where a machine-learning-based molecule generator is coupled with density functional theory (DFT) calculations, synthesis, and measurement. Although deep-learning-based molecule generators have shown promise, it is unclear to what extent they can be useful in real-world materials development. To assess the reliability of AI-assisted chemistry, we prepared a platform using the ChemTS molecule generator and a DFT simulator, and attempted to generate novel photo-functional molecules whose lowest excited states lie at desired energetic levels. A ten-day run on 12 cores discovered 86potential photo-functional molecules around target lowest excitation levels, designated as 200, 300, 400, 500, and 600 nm. Among the molecules discovered, six were synthesized and five were confirmed to reproduce DFT predictions in ultraviolet visible absorption measurements. This result shows the potential of AI-assisted chemistry to discover ready-to-synthesize novel molecules with modest computational resources. Density functional theory ; Molecule ; Excitation ; Excited state ; Generator (circuit theory) ; Computational chemistry ; Ultraviolet ; Organic molecules ; Nanotechnology ; Chemistry ; Computer science ; Materials science ; Physics ; Atomic physics ; Quantum mechanics ; Organic chemistry ; Optoelectronics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Mohammed AlQuraishi
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-08-06)
Field: Materials Science — Materials Chemistry

pNeRF: Parallelized Conversion from Internal to Cartesian Coordinates ABSTRACT The conversion of polymer parameterization from internal coordinates (bond lengths, angles, and torsions) to Cartesian coordinates is a fundamental task in molecular modeling, often performed using the Natural Extension Reference Frame (NeRF) algorithm. NeRF can be parallelized to process multiple polymers simultaneously, but is not parallelizable along the length of a single polymer. A mathematically equivalent algorithm, pNeRF, has been derived that is parallelizable along a polymer’s length. Empirical analysis demonstrates an order-of-magnitude speed up using modern GPUs and CPUs. In machine learning-based workflows, in which partial derivatives are backpropagated through NeRF equations and neural network primitives, switching to pNeRF can reduce the fractional computational cost of coordinate conversion from over two-thirds to around 10%. An optimized TensorFlow-based implementation of pNeRF is available on GitHub. Parallelizable manifold ; Cartesian coordinate system ; Computer science ; Geographic coordinate conversion ; Process (computing) ; Workflow ; Reference frame ; Algorithm ; Computational science ; Computation ; Artificial neural network ; Frame (networking) ; Coordinate system ; Parallel computing ; Geometry ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: George Rajna
Source: viXra (preprint, 2018-03-01)
Field: Materials Science — Materials Chemistry

Brain-Like Computers U.S. Army Research Laboratory scientists have discovered a way to leverage emerging brain-like computer architectures for an age-old number-theoretic problem known as integer factorization. [18] have come up with a novel machine learning method that enables scientists to derive insights from systems of previously intractable complexity in record time. [17] Quantum computers can be made to utilize effects such as quantum coherence and entanglement to accelerate machine learning. [16] Neural networks learn how to carry out certain tasks by analyzing large amounts of data displayed to them. [15] Who is the better experimentalist, a human or a robot? When it comes to exploring synthetic and crystallization conditions for inorganic gigantic molecules, actively learning machines are clearly ahead, as demonstrated by British Scientists in an experiment with polyoxometalates published in the journal Angewandte Chemie. [14] Machine learning algorithms are designed to improve as they encounter more data, making them a versatile technology for understanding large sets of photos such as those accessible from Google Images. Elizabeth Holm, professor of materials science and engineering at Carnegie Mellon University, is leveraging this technology to better understand the enormous number of research images accumulated in the field of materials science. [13] With the help of artificial intelligence, chemists from the University of Basel in Switzerland have computed the characteristics of about two million crystals made up of four chemical elements. The researchers were able to identify 90 previously unknown thermodynamically stable crystals that can be regarded as new materials. [12] The artificial intelligence system's ability to set itself up quickly every morning and compensate for any overnight fluctuations would make this fragile technology much more useful for field measurements, said co-lead researcher Dr Michael Hush from UNSW ADFA. [11] Quantum physicist Mario Krenn and his colleagues in the group of Anton Zeilinger from the Faculty of Physics at the University of Vienna and the Austrian Academy of Sciences have developed an algorithm which designs new useful quantum experiments. As the computer does not rely on human intuition, it finds novel unfamiliar solutions. [10] Computer science ; Artificial intelligence ; Leverage (statistics) ; Field (mathematics) ; Quantum machine learning ; Deep learning ; Data science ; Quantum ; Cognitive science ; Quantum computer ; Mathematics Machine Learning in Materials Science
Authors: Eman Sarwat
Source: INTERNATIONAL JOURNAL OF COMPUTER APPLICATION (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Nonlinear Autoregression Neural Network for Meteorological Parameters Forecasting to Evaluate the Behavior of Radioactive Material Release from Nuclear Power Plants In this paper a Nonlinear Autoregression Neural Network with exogenous input (NARX) is used to forecast temperature and wind speed. Six cases are studied to find the best condition with minimum mean absolute percentage error (MAPR) for prediction. In the first three cases the input data to NARX are four meteorological parameters (time, dew point, pressure and humidity) and the outputs are two parameters (temperature (Temp.) and wind speed (W.S.)). The first case is tested for Temp. and W.S. together as outputs. The second and the third cases are tested for Temp. and W.S. respectively. The results show that the first case gives MAPE for forecasting the two parameters together less than considering each one separately. As a result the first case is taken as a basic approach to the following three cases. In the fourth, fifth and sixth cases the input to NARX is taken as three parameters only without (dew point, pressure and humidity) respectively in each case. The NARX outputs are two parameters (Temp. and W.S.). In the last three cases the MAPE in the output parameters when compared with practical measurement of meteorological parameters do not exceed 20%. These results confirm that NARX model used in the current paper have the potential for successful application to meteorological parameters forecasting. NARX based weather forecast shows improvement over guidance forecasts from numerical models and official local weather. Nuclear power ; Nonlinear system ; Artificial neural network ; Environmental science ; Nuclear power plant ; Econometrics ; Computer science ; Artificial intelligence ; Economics ; Nuclear physics ; Physics Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Nuclear reactor physics and engineering
Authors: Rohit Pandey
Source: Türk bilgisayar ve matematik eğitimi dergisi (article, 2018-12-30)
Field: Materials Science — Materials Chemistry

Analysis the Need of Machine Learning in Predicting the Mechanical Properties of Nanomaterials Model Nanomaterials have gained significant attention in various fields due to their unique properties and potential applications. However, accurately predicting the mechanical properties of nanomaterials remains a challenging task due to their complex structures and size-dependent behaviour. In recent years, machine learning techniques have emerged as powerful tools for predicting material properties, including the mechanical behaviour of nanomaterials. This paper presents an analysis of the need for machine learning in predicting the mechanical properties of nanomaterials. The limitations of traditional approaches, such as empirical models and atomistic simulations, are discussed, highlighting their inability to capture the intricate interactions at the nanoscale. The inherent uncertainty and complexity associated with nanomaterials necessitate more efficient and accurate prediction methods.&#x0D; Machine learning algorithms offer a promising solution by leveraging large datasets and learning patterns from the available data. Through the use of feature extraction and selection techniques, machine learning models can identify the key parameters influencing the mechanical properties of nanomaterials. Additionally, these models can handle high-dimensional datasets and nonlinear relationships, which are commonly encountered in nanomaterials research. The paper further explores the application of various machine learning techniques, including regression models, support vector machines, random forests, and neural networks, in predicting the mechanical properties of nanomaterials. Examples of recent studies are presented to illustrate the successful implementation of these techniques in different nanomaterial systems, such as carbon nanotubes, graphene, and metal nanoparticles.&#x0D; The analysis considers the challenges and limitations associated with machine learning-based predictions, including the need for large and diverse datasets, potential overfitting, and the interpretability of models. Strategies to address these challenges, such as data augmentation, model regularization, and uncertainty quantification, are discussed. In conclusion, machine learning techniques provide a valuable approach for predicting the mechanical properties of nanomaterials. They offer improved accuracy, efficiency, and scalability compared to traditional methods, enabling researchers to explore and design novel nanomaterials with desired mechanical properties. The further research is needed to address the existing challenges and enhance the interpretability and robustness of machine learning models in this domain. Machine learning ; Overfitting ; Computer science ; Artificial intelligence ; Support vector machine ; Random forest ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Joakim Nyman
Source: KTH Publication Database DiVA (KTH Royal Institute of Technology) (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Pixel classification of hyperspectral images For sugar producers, it is a major problem to detect contamination of sugar. Doing it manually would not be feasible because of the high demand and would require toomuch labor. This report evaluates if the problem can be solved by using a hyperspectral camera operating in a wavelength range of 400-1000 nm and a spectralresolution of 224. Using the machine learning algorithms Artificial Neural Networkand Support Vector Machine, models were trained on pixels labeled as sugar or different materials of contamination. An autonomous system could be developed to analyze the sugar in real time and remove the contaminated sugar. This paper presents the results from using both Artificial Neural Networks as well as SupportVector Machine. It also addresses the impact of the pre-processing techniques filtering and maximum normalization when applying machine learning algorithms. The results showed that the accuracy can be significantly increased by using a hyperspectral camera instead of a normal camera, especially for plastic materials where using anormal camera gave a precision and recall score of 0.0 while using the hyperspectral camera gave above 0.9. Support Vector Machine performed slightly better than using Artificial Neural Network, especially for plastic material. The filtering and themaximum normalization did not increase the accuracy and could therefore be omitted in favor for performance. Hyperspectral imaging ; Pixel ; Artificial intelligence ; Sugar ; Computer science ; Computer vision ; Remote sensing ; Pattern recognition (psychology) ; Geography ; Biology Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Remote Sensing in Agriculture
Authors: Felipe Oviedo | Zekun Ren | Shijing Sun | Charlie Settens | Zhe Liu | Noor Titan Putri Hartono | Savitha Ramasamy | Brian DeCost | Siyu Tian | Giuseppe Romano | A. Gilad Kusne | Tonio Buonassisi
Source: arXiv (Cornell University) (preprint, 2018-11-20)
Field: Materials Science — Materials Chemistry

Fast and interpretable classification of small X-ray diffraction\n datasets using data augmentation and deep neural networks X-ray diffraction (XRD) data acquisition and analysis is among the most\ntime-consuming steps in the development cycle of novel thin-film materials. We\npropose a machine-learning-enabled approach to predict crystallographic\ndimensionality and space group from a limited number of thin-film XRD patterns.\nWe overcome the scarce-data problem intrinsic to novel materials development by\ncoupling a supervised machine learning approach with a model agnostic,\nphysics-informed data augmentation strategy using simulated data from the\nInorganic Crystal Structure Database (ICSD) and experimental data. As a test\ncase, 115 thin-film metal halides spanning 3 dimensionalities and 7\nspace-groups are synthesized and classified. After testing various algorithms,\nwe develop and implement an all convolutional neural network, with cross\nvalidated accuracies for dimensionality and space-group classification of 93%\nand 89%, respectively. We propose average class activation maps, computed from\na global average pooling layer, to allow high model interpretability by human\nexperimentalists, elucidating the root causes of misclassification. Finally, we\nsystematically evaluate the maximum XRD pattern step size (data acquisition\nrate) before loss of predictive accuracy occurs, and determine it to be\n0.16{\\deg}, which enables an XRD pattern to be obtained and classified in 5.5\nminutes or less.\n Interpretability ; Curse of dimensionality ; Artificial intelligence ; Pooling ; Computer science ; Artificial neural network ; Convolutional neural network ; Machine learning ; Pattern recognition (psychology) ; Thin film ; Materials science ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Felipe Oviedo | Zekun Ren | Shijing Sun | Charlie Settens | Zhe Liu | Noor Titan Putri Hartono | Savitha Ramasamy | Brian DeCost | Siyu Tian | Giuseppe Romano | A. Gilad Kusne | Tonio Buonassisi
Source: arXiv (Cornell University) (preprint, 2018-11-20)
Field: Materials Science — Materials Chemistry

Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks X-ray diffraction (XRD) data acquisition and analysis is among the most time-consuming steps in the development cycle of novel thin-film materials. We propose a machine-learning-enabled approach to predict crystallographic dimensionality and space group from a limited number of thin-film XRD patterns. We overcome the scarce-data problem intrinsic to novel materials development by coupling a supervised machine learning approach with a model agnostic, physics-informed data augmentation strategy using simulated data from the Inorganic Crystal Structure Database (ICSD) and experimental data. As a test case, 115 thin-film metal halides spanning 3 dimensionalities and 7 space-groups are synthesized and classified. After testing various algorithms, we develop and implement an all convolutional neural network, with cross validated accuracies for dimensionality and space-group classification of 93% and 89%, respectively. We propose average class activation maps, computed from a global average pooling layer, to allow high model interpretability by human experimentalists, elucidating the root causes of misclassification. Finally, we systematically evaluate the maximum XRD pattern step size (data acquisition rate) before loss of predictive accuracy occurs, and determine it to be 0.16°, which enables an XRD pattern to be obtained and classified in 5.5 minutes or less. Interpretability ; Curse of dimensionality ; Pooling ; Artificial intelligence ; Computer science ; Convolutional neural network ; Artificial neural network ; Machine learning ; Thin film ; Pattern recognition (psychology) ; Materials science ; Nanotechnology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Condensed Matter Physics