Results 201 to 210 of about 24,937 (297)

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

An Optimal Algorithm for Strongly Convex Min-Min Optimization

We consider the problem of minimizing a function f(x, y), where f is a smooth and strongly convex function with respect to both variables, being mu(x)-strongly convex in x and mu(y)-strongly convex in y.
Kovalev, Dmitry, Malinovsky, Grigory, Gasnikov, Alexander   +2 more
core  

The new rank-based concentration index: Further analysis and properties. [PDF]

PLoS One
Kvålseth TO.
europepmc   +1 more source

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +1 more source

Geometric linearisation for optimal transport with strongly p-convex cost

We prove a geometric linearisation result for minimisers of optimal transport problems where the cost-function is strongly p-convex and of p-growth. Initial and target measures are allowed to be rough, but are assumed to be close to Lebesgue measure ...
Koch, Lukas
core  

Emergence Angle and Emergence Profile in Implant-Supported Restorations: A Scoping Review. [PDF]

Dent J (Basel)
Prpic V, Gojsovic S, Kosec P, Skec S, Catic A.   +4 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Gauges and Accelerated Optimization over Smooth and/or Strongly Convex Sets

We consider feasibility and constrained optimization problems defined over smooth and/or strongly convex sets. These notions mirror their popular function counterparts but are much less explored in the first-order optimization literature.
Grimmer, Benjamin, Liu, Ning
core  

General Perturbation Resilient Dynamic String-Averaging for Inconsistent Problems with Superiorization. [PDF]

J Optim Theory Appl
Barshad K, Censor Y.
europepmc   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source