Results 231 to 240 of about 83,573 (332)

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

Enhancing bioinformatics engineering by utilizing graph therapeutic properties for clinically approved antitoxin drugs in zoonotic diseases. [PDF]

Sci Rep
Imran M, Aqib M, Malik MA, Jutt S.
europepmc   +1 more source

Non-isomorphic abelian varieties with the same arithmetic. [PDF]

R Soc Open Sci
Bell J.
europepmc   +1 more source

Vassiliev invariants as polynomials [PDF]

Banach Center Publications, 1998
openaire   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

<i>P</i>-adic <i>L</i>-functions for GL ( 3 ). [PDF]

Math Ann
Loeffler D, Williams C.
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

Euclidean-Lorentzian Dichotomy and Algebraic Causality in Finite Ring Continuum. [PDF]

Entropy (Basel)
Akhtman Y.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Microstructure.jl: A Julia package for probabilistic microstructure model fitting with diffusion MRI. [PDF]

Imaging Neurosci (Camb)
Gong T, Yendiki A.
europepmc   +1 more source