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The mitigation of material defects from additive manufacturing (AM) processes is critical to reliability in their fabricated parts and is enabled by modeling the complex relations between available build monitoring signals and final…

Applied Physics · Physics 2024-02-26 Qixiang Luo , John D. Shimanek , Timothy W. Simpson , Allison M. Beese

Calculations of elastic and mechanical characteristics of non-crystalline solids are challenging due to high computation cost of $ab$ $initio$ methods and low accuracy of empirical potentials. We propose a computational technique towards…

In recent years, the behavior of dislocations in random solid solutions has received renewed interest, and several models have been discussed where random alloys are treated as effective media containing random distributions of dilatation…

Materials Science · Physics 2025-03-20 Ronghai Wu , Michael Zaiser

The rational tailoring of transition metal complexes is necessary to address outstanding challenges in energy utilization and storage. Heterobimetallic transition metal complexes that exhibit metal-metal bonding in stacked "double decker"…

Materials Science · Physics 2021-08-02 Michael G. Taylor , Aditya Nandy , Connie C. Lu , Heather J. Kulik

ANN (Artificial Neural Networks) modeling methodology was adopted for predicting mechanical properties of aluminum cast composite materials. For this purpose aluminum alloy were developed using conventional foundry method. The composite…

Materials Science · Physics 2016-06-01 Muhammad Hayat Jokhio , Muhammad Ibrahim Panhwer , Mukhtiar Ali Unar

A multi-scale methodology is developed in conjunction with a probabilistic fatigue lifetime model for structures with pores whose exact distribution, i.e. geometries and locations, is unknown. The method takes into account uncertainty in…

Computational Engineering, Finance, and Science · Computer Science 2024-09-27 Abhishek Palchoudhary , Cristian Ovalle , Vincent Maurel , Pierre Kerfriden

Discrete dislocation dynamics (DDD) simulations offer valuable insights into the plastic deformation and work-hardening behavior of metals by explicitly modeling the evolution of dislocation lines under stress. However, the computational…

Materials Science · Physics 2023-08-22 Rasool Ahmad , Wei Cai

Material strength is a classical concept with renewed importance in fracture mechanics, particularly in crack nucleation in brittle solids. We formulate material strength in finite elasticity and examine its geometric, constitutive, and…

Materials Science · Physics 2026-05-05 Arash Yavari , Aditya Kumar

Accurate methane sorption prediction across heterogeneous coal ranks requires models that combine thermodynamic consistency, efficient knowledge transfer across data-scarce geological systems, and calibrated uncertainty estimates,…

Machine Learning · Computer Science 2026-04-16 Mohammad Nooraiepour , Zezhang Song , Wei Li , Sarah Perez

Hydrogen embrittlement (HE) poses a significant challenge to the durability of materials used in hydrogen production and utilization. Disentangling the competing nanoscale mechanisms driving HE often relies on simulations and…

It was experimentally confirmed that if steel cyclic stress reduces to less than tensile yield stress values, i.e. in case of high-cycle fatigue, the mechanism of fracture changes from dislocation to vacancy one. This conclusion was based…

Materials Science · Physics 2019-02-12 Leonid Kremnev , Vyacheslav Matyunin , Artem Marchenkov , Larisa Vinogradova

The development of accurate constitutive models for materials that undergo path-dependent processes continues to be a complex challenge in computational solid mechanics. Challenges arise both in considering the appropriate model assumptions…

Machine Learning · Computer Science 2023-02-22 Jan N. Fuhg , Craig M. Hamel , Kyle Johnson , Reese Jones , Nikolaos Bouklas

Accurately predicting when and how materials fail is critical to designing safe, reliable structures, mechanical systems, and engineered components that operate under stress. Yet, fracture behavior remains difficult to model across the…

The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either…

Materials Science · Physics 2018-04-10 Tian Xie , Jeffrey C. Grossman

Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic…

We reexamine two essential issues within the Peierls-Nabarro model which are critical in obtaining accurate values for the Peierls stress. The first issue is related to the sampling scheme of the misfit energy across the glide plane and the…

Materials Science · Physics 2007-05-23 Gang Lu , Nicholas Kioussis , Vasily V. Bulatov , Efthimios Kaxiras

The existence of a well defined yield stress, where a macroscopic piece of crystal begins to plastically flow, has been one of the basic observations of materials science. In contrast to macroscopic samples, in micro- and nanocrystals the…

Dislocations in ductile ceramics offer the potential for robust mechanical performance while unlocking versatile functional properties. Previous studies have been limited by small volumes with dislocations and/or low dislocation densities…

Materials Science · Physics 2024-11-20 Oliver Preuß , Enrico Bruder , Jiawen Zhang , Wenjun Lu , Jürgen Rödel , Xufei Fang

Parameterized tight-binding models fit to first principles calculations can provide an efficient and accurate quantum mechanical method for predicting properties of molecules and solids. However, well-tested parameter sets are generally…

Materials Science · Physics 2023-04-28 Kevin F. Garrity , Kamal Choudhary

The onset of nonlinear effects in metals, such as plasticity and damage, is strongly influenced by the heterogeneous stress distribution at the grain level. This work is devoted to studying the local stress distribution of shear stresses…

Materials Science · Physics 2022-07-26 Flavia Gehrig , Daniel Wicht , Maximilian Krause , Thomas Böhlke
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