Results 211 to 220 of about 33,836 (264)

Development of machine learning predictive models for estimating pharmaceutical solubility in supercritical CO₂: case study on lornoxicam solubility. [PDF]

Front Chem
Chao L, Wang Y, Erta B, Guo W, Wang H, Zhao C, Yang Y.   +6 more
europepmc   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Forensic classification of gunpowder and fireworks powders by ATR-FT-IR spectroscopy and chemometric modelling. [PDF]

Front Chem
Aljanaahi A, Aljanaahi A, Ahli NA, Alblooshi R, Yasin A, Shah I.   +5 more
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Critical points and syzygies for Feynman integrals. [PDF]

J High Energy Phys
Page B, Song Q.
europepmc   +1 more source

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley   +1 more source

Intermediate strain rate constitutive modeling of gradient rolled vanadium-microalloyed pearlitic steel for bridge cables. [PDF]

iScience
Huang S, Li Z, Yang H, Zeng Z, Shi Y, Li C.   +5 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

genCRC32: collision-free CRC32-based hashing of DNA sequences. [PDF]

Bioinform Adv
Beran P, Rost M, Beranová K, Kváč M, Stehlíková D, Udoh OE, Jozová E, Čurn V.   +7 more
europepmc   +1 more source