Results 21 to 30 of about 522,455 (311)
Machine learning for interatomic potential models [PDF]
The Journal of Chemical Physics, 2020 The use of supervised machine learning to develop fast and accurate interatomic potential models is transforming molecular and materials research by greatly accelerating atomic-scale simulations with little loss of accuracy. Three years ago, Jörg Behler published a perspective in this journal providing an overview of some of the leading methods in this
Tim Mueller +2 more
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Metals, 2023 With the continuous improvement of machine learning methods, building the interatomic machine learning potential (MLP) based on the datasets from quantum mechanics calculations has become an effective technical approach to improving the accuracy of ...
Jiawei Jiang +3 more
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Revolutionizing healthcare with federated reinforcement learning: from machine learning to machine unlearning [PDF]
, 2023 The landscape of healthcare is undergoing a transformative shift with the emergence of artificial intelligence (AI) and machine learning (ML) technologies, particularly in remote patient monitoring systems. These systems offer real-time data on patients’
Shaik, Thanveer Basha
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An accurate and transferable machine learning potential for carbon [PDF]
The Journal of Chemical Physics, 2020 We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab ...
Rowe, P +4 more
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Applications and training sets of machine learning potentials
Science and Technology of Advanced Materials: Methods, 2023 Recently, machine learning potentials (MLPs) have been attracting interest as an alternative to the computationally expensive density-functional theory (DFT) calculations.
Changho Hong +6 more
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A Review of Causality for Learning Algorithms in Medical Image Analysis
, 2022 Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area.
Vlontzos, Athanasios +2 more
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A comparison of machine learning models for the mapping of groundwater spring potential [PDF]
, 2020 Groundwater resources are vitally important in arid and semi-arid areas meaning that spatial planning tools are required for their exploration and mapping.
Pourghasemi, H. R. +6 more
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Greedy structure learning from data that contain systematic missing values
, 2022 Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume ...
Liu, Y +5 more
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Earth, Planets and Space, 2023 Physics-based simulations are important for elucidating the fundamental mechanisms behind the time-varying complex ionospheric conditions, such as ionospheric potential, against unprecedented solar wind variations incident on the Earth’s magnetosphere ...
Ryuho Kataoka +2 more
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Evaluating the Role of Machine Learning in Economics: A Cutting-Edge Addition or Rhetorical Device?
Studies in Logic, Grammar and Rhetoric, 2023 This paper explores the integration of machine learning into economics and social sciences, assessing its potential impact and limitations. It introduces fundamental machine learning concepts and principles, highlighting the differences between the two ...
Czech Sławomir
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