Virtual Issue on Machine-Learning Discoveries in Materials Science

Oliynyk, Anton O.; Buriak, Jillian

doi:10.1021/acs.chemmater.9b03854

Cited by 25 publications

(16 citation statements)

References 64 publications

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“…Other equations are also important (notably that of Euler) [6] If the Navier-Stokes equation models the behavior of fluids, its solution is not the simplest. This comes from the fact that it is nonlinear, depending on the observation environment, turbulence (example: passage of an airplane during a calculation of the trajectory of an air current) make calculations almost impossible [7] To overcome this difficulty, scientists have chosen to take approximations of the resolutions of this equation. There are several, depending on the constraints posed in terms of reliability or computing time.…”

Section: Related Workmentioning

confidence: 99%

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Amroun¹,

Ammi

Hafid³

2021

IEEE Access

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In this paper, we present a new approach to use machine learning (ML) for the calibration of a physical model allowing the reproduction of the vibratory behavior of an overhead line conductor. This physical model known as Strip Theory (ST) has the advantage of being very precise but very complicated and cumbersome in its software operations and manipulations. A second model known as the Wake Oscillator (WO) has been implemented in order to meet the limitations of the ST model. In order to be able to use the WO model instead of the ST model, very heavy manual adjustments are required, which makes its use complicated.Precisely, the WO must be able to generate a time series similar to a time series generated by the ST model. In order to respond to this limitation, a machine learning model known as ENS has been proposed. The machine learning model will therefore take as input the data from the WO model and output the data from the ST model. A series of Machine learning models have been implemented and tested. The ENS algorithm was retained with a best Pearson's linear coefficient of determination (R2 Score) of almost 0.7 and a Root mean square deviation (RMSE) of 7.57e-09. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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Section: Related Workmentioning

confidence: 99%

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Amroun¹,

Ammi

Hafid³

2021

IEEE Access

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“…Anton Oliynyk's research: The Oliynyk group studies intermetallic compounds and combines machine learning methods with experimental research. [ 490–495 ] They study the inorganic chemistry of intermetallic materials with focus on energy‐converting materials and mechanical properties such as hardness and wear resistance. [ 493 ] The materials are synthesized directly from elements by high‐temperature methods, including arc‐melting and sintering.…”

Section: Overview Of Mercury Faculty Research Effortsmentioning

confidence: 99%

“…[ 493 ] The materials are synthesized directly from elements by high‐temperature methods, including arc‐melting and sintering. A cutting‐edge machine learning‐driven approach [ 492 ] is utilized to guide the discoveries of novel materials and screen chemical space for potential compounds, and computational methods are used for structure prediction from first principles.…”

Section: Overview Of Mercury Faculty Research Effortsmentioning

confidence: 99%

Twenty years of exceptional success: The molecular education and research consortium in undergraduate computational chemistry (MERCURY)

Shields

2020

Int J of Quantum Chemistry

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The molecular education and research consortium in undergraduate computational chemistry (MERCURY) consortium, established in 2000, has contributed greatly to the scientific development of faculty and undergraduates. The MERCURY faculty peer-reviewed publication rate from 2001 to 2019 of 1.7 papers/faculty/year is 3.4 times the rate of the physical science faculty at primarily undergraduate institutions. We have worked with over 1000 students on research projects since 2001, and 75% of our undergraduate research students have been under-represented in chemistry, either female or students of color. Approximately half of our alumni attend graduate school for the purpose of obtaining advanced degrees in STEM fields, and two-thirds are female and/or students of color. We have had more than 1600 attendees at 18 MERCURY conferences, including 111 invited speakers, 61 of whom have been female and/or faculty of color. In this paper, the research accomplishments, transformational outcomes, and scientific productivity of the MERCURY faculty are highlighted.

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“…Machine learning (ML) has been succesfully applied in screening and processing of large databases with materials data with the aim to detect cases with potential for use in various applications. [62][63][64][65][66][67][68][69][70][71][72][73][74] There are relatively few published works in utilizing machine learning targeting magnetic materials compared to other disciplines but there is increasing interest in the field, for the reasons we have already mentioned.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning in Magnetic Materials

Katsikas

Sarafidis

Kioseoglou

2021

Physica Status Solidi (b)

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The technological advancements of every era of human civilization owe themselves to the materials available at the time. Despite the substantial interest in the discovery of novel materials, materials research remains a very delicate and time‐exhaustive procedure. Over the last 30 years, ab initio computational methods based on density functional theory (DFT) have allowed researchers to explore materials with ease and expect above‐experiment measurement precision. However, DFT‐based detailed investigation of novel materials is generally computationally intensive and greatly time‐consuming. This review presents machine learning methods applied to DFT simulation data to uncover connections between material structure, chemical composition, and magnetization, a target property chosen for its great industrial demand. Models are developed that can partially circumvent the need for simulation, guiding researchers in the design of magnetic materials. Specifically, the Materials Project database is examined and it is concluded that Eu, Gd, Pu, Fe, Np, Mn, U, Cr, Co, and Ce are amongst the most common elements found in magnetic materials, and that materials of the same composition may have different magnetization depending on their space group. A neural network capable of predicting magnetization with a standard error of 8.3 × 10−3 μ B Å−3 is created.

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Virtual Issue on Machine-Learning Discoveries in Materials Science

Cited by 25 publications

References 64 publications

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Twenty years of exceptional success: The molecular education and research consortium in undergraduate computational chemistry (MERCURY)

Machine Learning in Magnetic Materials

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