scholarly journals Materials science in the artificial intelligence age: high-throughput library generation, machine learning, and a pathway from correlations to the underpinning physics

2019 ◽  
Vol 9 (3) ◽  
pp. 821-838 ◽  
Author(s):  
Rama K. Vasudevan ◽  
Kamal Choudhary ◽  
Apurva Mehta ◽  
Ryan Smith ◽  
Gilad Kusne ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
High Throughput ◽  
Materials Science ◽  
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Abstract

scholarly journals A data ecosystem to support machine learning in materials science

2019 ◽  
Vol 9 (4) ◽  
pp. 1125-1133 ◽  
Author(s):  
Ben Blaiszik ◽  
Logan Ward ◽  
Marcus Schwarting ◽  
Jonathon Gaff ◽  
Ryan Chard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Data Ecosystem ◽  
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Abstract

scholarly journals Machine learning as the new approach in understanding biomarkers of suicidal behavior

2020 ◽  
Author(s):  
Alja Videtič Paska ◽  
Katarina Kouter
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
High Throughput ◽  
Suicidal Behavior ◽  
Technological Development ◽  
Machine Learning Algorithms ◽  
Classical Approach ◽  
New Approach

In psychiatry, compared to other medical fields, the identification of biological markers that would complement current clinical interview, and enable more objective and faster clinical diagnosis, implement accurate monitoring of treatment response and remission, is grave. Current technological development enables analyses of various biological marks in high throughput scale at reasonable costs, and therefore ‘omic’ studies are entering the psychiatry research. However, big data demands a whole new plethora of skills in data processing, before clinically useful information can be extracted. So far the classical approach to data analysis did not really contribute to identification of biomarkers in psychiatry, but the extensive amounts of data might get to a higher level, if artificial intelligence in the shape of machine learning algorithms would be applied. Not many studies on machine learning in psychiatry have been published, but we can already see from that handful of studies that the potential to build a screening portfolio of biomarkers for different psychopathologies, including suicide, exists.

Generalized machine learning technique for automatic phase attribution in time variant high-throughput experimental studies

2015 ◽  
Vol 30 (7) ◽  
pp. 879-889 ◽  
Author(s):  
Jonathan Kenneth Bunn ◽  
Shizhong Han ◽  
Yan Zhang ◽  
Yan Tong ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Experimental Studies ◽  
Machine Learning Technique ◽  
Automatic Phase ◽  
Learning Technique ◽  
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In silico high throughput screening of bimetallic and single atom alloys using machine learning and ab initio microkinetic modelling

2020 ◽  
Vol 8 (1) ◽  
pp. 107-123 ◽  
Author(s):  
Shivam Saxena ◽  
Tuhin Suvra Khan ◽  
Fatima Jalid ◽  
Manojkumar Ramteke ◽  
M. Ali Haider
Keyword(s):  
Machine Learning ◽  
Ab Initio ◽  
High Throughput ◽  
Material Properties ◽  
In Silico ◽  
High Throughput Screening ◽  
Chemical Engineering ◽  
Materials Science ◽  
Single Atom ◽  
Single Atom Alloys

The advent of machine learning (ML) techniques in solving problems related to materials science and chemical engineering is driving expectations to give faster predictions of material properties.

Nanoinformatics, and the big challenges for the science of small things

Nanoscale ◽  
2019 ◽  
Vol 11 (41) ◽  
pp. 19190-19201 ◽  
Author(s):  
A. S. Barnard ◽  
B. Motevalli ◽  
A. J. Parker ◽  
J. M. Fischer ◽  
C. A. Feigl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Chemistry ◽  
Functional Properties ◽  
Materials Science ◽  
Structural Features ◽  
Computational Materials Science ◽  
Computational Materials ◽  
Small Things

The combination of computational chemistry and computational materials science with machine learning and artificial intelligence provides a powerful way of relating structural features of nanomaterials with functional properties.

Artificial intelligence/machine learning in manufacturing and inspection: A GE perspective

MRS Bulletin ◽  
2019 ◽  
Vol 44 (7) ◽  
pp. 545-558 ◽  
Author(s):  
Kareem S. Aggour ◽  
Vipul K. Gupta ◽  
Daniel Ruscitto ◽  
Leonardo Ajdelsztajn ◽  
Xiao Bian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
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Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

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