Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

Statistics for Spatial Data.

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability. Researchers in the United States have developed a versatile machine learning framework to aid the search for novel materials. Led by Christopher Wolverton and Logan Ward from Northwestern University, the researchers used machine learning techniques trained against known material data to generate models that predict the specific properties of new materials. The utility of the technique was demonstrated through searches for novel crystalline compounds for photovoltaic applications, and for metallic glass alloys based on the probability of glass formation for ternary alloys. New models can be created by optimizing the machine learning algorithm and partitioning input data to maximize the prediction accuracy for specific parameters. The technique has the potential to automate and accelerate the search for new functional materials using the large libraries of material data now available to researchers.

/pdf/a-general-purpose-machine-learning-framework-for-predicting-24w9t8kp4v.pdf

A general-purpose machine learning framework for predicting properties of inorganic materials

Our ability to collect “big data” has greatly surpassed our capability to analyze it, underscoring the emergence of the fourth paradigm of science, which is data-driven discovery. The need for data informatics is also emphasized by the Materials Genome Initiative (MGI), further boosting the emerging field of materials informatics. In this article, we look at how data-driven techniques are playing a big role in deciphering processing-structure-property-performance relationships in materials, with illustrative examples of both forward models (property prediction) and inverse models (materials discovery). Such analytics can significantly reduce time-to-insight and accelerate cost-effective materials discovery, which is the goal of MGI.

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

A large number of sentiment reviews, blogs and comments present online.These reviews must be classified to obtain a meaningful information.Four different supervised machine learning algorithm used for classification.Unigram, Bigram, Trigram models and their combinations used for classification.The classification is done on IMDb movie review dataset. With the ever increasing social networking and online marketing sites, the reviews and blogs obtained from those, act as an important source for further analysis and improved decision making. These reviews are mostly unstructured by nature and thus, need processing like classification or clustering to provide a meaningful information for future uses. These reviews and blogs may be classified into different polarity groups such as positive, negative, and neutral in order to extract information from the input dataset. Supervised machine learning methods help to classify these reviews. In this paper, four different machine learning algorithms such as Naive Bayes (NB), Maximum Entropy (ME), Stochastic Gradient Descent (SGD), and Support Vector Machine (SVM) have been considered for classification of human sentiments. The accuracy of different methods are critically examined in order to access their performance on the basis of parameters such as precision, recall, f-measure, and accuracy.

Classification of sentiment reviews using n-gram machine learning approach

A very active area of materials research is to devise methods that use machine learning to automatically extract predictive models from existing materials data. While prior examples have demonstrated successful models for some applications, many more applications exist where machine learning can make a strong impact. To enable faster development of machine-learning-based models for such applications, we have created a framework capable of being applied to a broad range of materials data. Our method works by using a chemically diverse list of attributes, which we demonstrate are suitable for describing a wide variety of properties, and a novel method for partitioning the data set into groups of similar materials in order to boost the predictive accuracy. In this manuscript, we demonstrate how this new method can be used to predict diverse properties of crystalline and amorphous materials, such as band gap energy and glass-forming ability.

/pdf/a-general-purpose-machine-learning-framework-for-predicting-37q9xnckua.pdf

Ankit Agrawal

Papers

A general-purpose machine learning framework for predicting properties of inorganic materials

Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

Deep Convolutional Neural Networks with transfer learning for computer vision-based data-driven pavement distress detection

Classification of sentiment reviews using n-gram machine learning approach

A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials