Results 61 to 70 of about 31,740 (265)

A mathematical model for entropy generation in a Powell-Eyring nanofluid flow in a porous channel

open access: yesHeliyon, 2019
The continuous generation of entropy leads to exergy destruction which reduces the performance of a physical system. Hence, entropy minimization becomes necessary.
Hammed Abiodun Ogunseye   +1 more
doaj   +1 more source

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

open access: yesAdvanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang   +6 more
wiley   +1 more source

Thermodynamics of computing with circuits

open access: yesNew Journal of Physics, 2020
Abstract Digital computers implement computations using circuits, as do many naturally occurring systems (e.g., gene regulatory networks). The topology of any such circuit restricts which variables may be physically coupled during the operation of the circuit.
David H Wolpert, Artemy Kolchinsky
openaire   +3 more sources

Investigating the Low‐Temperature Phase Stability of the Binary Ta–W System

open access: yesAdvanced Engineering Materials, EarlyView.
Atomistic simulations show that the binary Ta–W system forms ordered intermetallic phases, B2‐TaW and D03‐TaW3, as 0 K ground states. Configurational entropy, however, lowers the free energy of the disordered bcc solid solution, which becomes the stable phase above about 400 K.
Klemens Lechner   +7 more
wiley   +1 more source

Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs

open access: yesAdvanced Engineering Materials, EarlyView.
The connection of conceptual workflow design, portable execution, and ontology‐based semantics leading to provenance‐rich knowledge graphs are main contributors to interoperability in materials science and a prerequisite to AI‐assisted orchestration and for interoperable Materials Acceleration Platforms.
Jan Janssen   +14 more
wiley   +1 more source

Effects of Mg on Microstructure and Solidification of a Hypereutectic Zn–8 wt.%Al Alloy

open access: yesAdvanced Engineering Materials, EarlyView.
An appreciable set of results involving thermal data, microstructure, chemical composition, and microstructural growth laws is reported for ZnAlMg alloys. Such results demonstrate that ZnAlMg alloys have high potential for applications in automotive self‐lubricating components, batteries, and electrical systems.
Raí B. de Sousa   +6 more
wiley   +1 more source

Going Round in Circles: Landauer vs. Norton on the Thermodynamics of Computation

open access: yesEntropy, 2014
There seems to be a consensus among physicists that there is a connection between information processing and thermodynamics. In particular, Landauer’s Principle (LP) is widely assumed as part of the foundation of information theoretic/computational ...
James Ladyman, Katie Robertson
doaj   +1 more source

Supporting AI Readiness Through Digital Workflows in Materials Science

open access: yesAdvanced Engineering Materials, EarlyView.
Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns   +67 more
wiley   +1 more source

Error Mitigation for Thermodynamic Computing

open access: yesCoRR
17 pages, 8 ...
Maxwell Aifer   +5 more
openaire   +2 more sources

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

open access: yesAdvanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin   +14 more
wiley   +1 more source

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