Results 151 to 160 of about 93,556 (252)

Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

Advanced Energy Materials, Volume 15, Issue 11, March 18, 2025.
A closed‐loop, data‐driven approach facilitates the exploration of high‐performance Si─Ge─Sn alloys as promising fast‐charging battery anodes. Autonomous electrochemical experimentation using a scanning droplet cell is combined with real‐time optimization to efficiently navigate composition space.
Alexey Sanin, Jackson K. Flowers, Tobias H. Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein   +7 more
wiley   +1 more source

Minimizing unnecessary tax audits using multi-objective hyperparameter tuning of XGBoost with focal loss. [PDF]

Front Artif Intell
Malashin IP, Masich IS, Tynchenko VS, Gantimurov AP, Nelyub VA, Borodulin AS.   +5 more
europepmc   +1 more source

Prediction of Structural Stability of Layered Oxide Cathode Materials: Combination of Machine Learning and Ab Initio Thermodynamics

Advanced Energy Materials, EarlyView.
In this work, we developed a phase‐stability predictor by combining machine learning and ab initio thermodynamics approaches, and identified the key factors determining the favorable phase for a given composition. Specifically, a lower TM ionic potential, higher Na content, and higher mixing entropy favor the O3 phase.
Liang‐Ting Wu, Milica Zdravković, Dijana Milosavljević, Konstantin Köster, Olivier Guillon, Jyh‐Chiang Jiang, Payam Kaghazchi   +6 more
wiley   +1 more source

Optimizing Detection Reliability in Safety-Critical Computer Vision: Transfer Learning and Hyperparameter Tuning with Multi-Task Learning. [PDF]

Sensors (Basel)
Broderick W, McConnell S.
europepmc   +1 more source

Accelerated Screening of Halide Double Perovskites via Hybrid Density Functional Theory and Machine Learning for Thermoelectric Energy Conversion

Advanced Energy and Sustainability Research, EarlyView.
This study integrates hybrid density functional theory, Boltzmann transport theory, and machine learning to accelerate the discovery of lead‐free halide double perovskites for thermoelectric energy conversion. By screening 102 compounds, the authors identify high‐performing candidates such as Rb2GeI6 and Cs2SnBr6, offering a sustainable pathway toward ...
Souraya Goumri‐Said, Ghouti Abdellaoui, Mohammed Benali Kanoun   +2 more
wiley   +1 more source

Practical Bayesian optimisation for hyperparameter tuning

, 2020
Advances in machine learning have had, and continue to have, a profound effect on scientific research and industrial activities. We are able to uncover insights contained within large troves of data and develop models to solve problems that seemed infeasible until recently.
openaire   +2 more sources

Construction of a Feedback Comment Analysis Model for Evaluation of Endoscopic Surgical Skill

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Background Surgical education and skill assessments are important in improving surgical skills. However, instructors' comments tend to be complex and unorganized, with varying content and categories. This study aimed to develop a natural language processing (NLP) model to automatically classify feedback comments on surgical procedures and ...
Shusaku Iwai, Shin Takenaka, Daichi Kitaguchi, Hiroki Matsuzaki, Kei Nakajima, Yuya Takabe, Nobuyoshi Takeshita, Masaaki Ito   +7 more
wiley   +1 more source

Enhanced spectrum sensing for 5G and LTE signals using advanced deep learning models and hyperparameter tuning. [PDF]

Sci Rep
Elmorsy SMA, Osman SM, Gamel SA.
europepmc   +1 more source

A multiscale Bayesian optimization framework for process and material codesign

AIChE Journal, EarlyView.
Abstract The simultaneous design of processes and enabling materials such as solvents, catalysts, and adsorbents is challenging because molecular‐ and process‐level decisions are strongly interdependent. Sequential approaches often yield suboptimal results since improvements in material properties may not translate into superior process performance. We
Michael Baldea
wiley   +1 more source

Optimizing Depression Classification Using Combined Datasets and Hyperparameter Tuning with Optuna. [PDF]

Sensors (Basel)
Duță Ș, Sultana AE.
europepmc   +1 more source