Results 61 to 70 of about 522,455 (311)
Performance and Cost Assessment of Machine Learning Interatomic Potentials [PDF]
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom ...
Zuo, Yunxing +10 more
openaire +7 more sources
Machine learning coarse-grained potentials of protein thermodynamics
AbstractA generalized understanding of protein dynamics is an unsolved scientific problem, the solution of which is critical to the interpretation of the structure-function relationships that govern essential biological processes. Here, we approach this problem by constructing coarse-grained molecular potentials based on artificial neural networks and ...
Majewski +19 more
openaire +6 more sources
On Interactive Machine Learning and the Potential of Cognitive Feedback
14 pages, 2 figures, submitted and accepted to the 2nd Workshop on Deep Models and Artificial Intelligence for Defense Applications: Potentials, Theories, Practices, Tools and Risks sponsored by the Association for the Advancement of Artificial Intelligence in cooperation with the Stanford University Computer Science ...
Chris J. Michael 0001 +2 more
openaire +2 more sources
Early Prediction Model of Macrosomia Using Machine Learning for Clinical Decision Support
The condition of fetal overgrowth, also known as macrosomia, can cause serious health complications for both the mother and the infant. It is crucial to identify high-risk macrosomia-relevant pregnancies and intervene appropriately.
Md. Shamshuzzoha, Md. Motaharul Islam
core +1 more source
A Review of Machine Learning Potentials in the Study of Materials Properties
With the rapid advancement of artificial intelligence (AI) technologies and hardware capabilities, AI has gradually become a revolutionary tool driving transformative changes across multiple scientific research domains. In the field of materials science,
Jinlong LI, Hao WANG, Huayun GENG
doaj +1 more source
Learning in continuous action space for developing high dimensional potential energy models
Reinforcement learning algorithms are emerging as powerful machine learning approaches. This paper introduces a novel machine-learning approach for learning in continuous action space and applies this strategy to the generation of high dimensional ...
Sukriti Manna +12 more
doaj +1 more source
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska +13 more
wiley +1 more source
reservedThis case study focuses on using machine learning to segment hospitals into potential clusters based on their number of patients treated for a certain disease.
DI FRANCESCO, REBECCA
core
Machine Learning for PAC1D and SESE [PDF]
This document outlines various machine learning approaches that were taken in an effort to surrogate numerical models Python Ablation Code 1-Dimension (PAC1D) and Scalable Effects Simulation Environment (SESE), with the ultimate objective of discovering ...
Seman, Matthew G. +4 more
core
Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review
The domain of Machine learning has experienced Substantial advancement and development. Recently, showcasing a Broad spectrum of uses like Computational linguistics, image identification, and autonomous systems. With the increasing demand for intelligent
Abrar Yaqoob +2 more
doaj +1 more source

