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Disorder and homogeneity are two concepts that refer to spatial variation of the system potential. In condensed-matter systems disorder is typically divided into two types; those with local parameters varying from site to site (diagonal…

Disordered Systems and Neural Networks · Physics 2022-11-14 Z. Ovadyahu

We suggest and implement an approach for the bottom-up description of systems undergoing large-scale structural changes and chemical transformations from dynamic atomically resolved imaging data, where only partial or uncertain data on…

Materials Science · Physics 2021-04-23 Sergei V. Kalinin , Ondrej Dyck , Stephen Jesse , Maxim Ziatdinov

Generative models such as AlphaFold and MatterGen can directly generate novel material structures with desired properties, accelerating the new materials discovery and revolutionizing the material design paradigm from traditional…

Bulk amorphous materials have been studied extensively and are widely used, yet their atomic arrangement remains an open issue. Although they are generally believed to be Zachariasen continuous random networks, recent experimental evidence…

String theory models generically predict the existence of multiple axion-like particle (ALP) fields, yet the majority of both theoretical and experimental works have assumed only one ALP. In this paper, we discuss the phenomenology of…

High Energy Physics - Phenomenology · Physics 2024-10-03 Francesca Chadha-Day , James Maxwell , Jessica Turner

Predictive modelling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new materials, processes and structures. In the current study,…

Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches…

The ability to generate 3D multiphase microstructures on-demand with targeted attributes can greatly accelerate the design of advanced materials. Here, we present a conditional latent diffusion model (LDM) framework that rapidly synthesizes…

Designing topological materials with specific topological indices is a complex inverse problem, traditionally tackled through manual, intuition-driven methods that are neither scalable nor efficient for exploring the vast space of possible…

Mesoscale and Nanoscale Physics · Physics 2025-10-28 Samuel Bird , Chiara Devescovi , Pascal Engeler , Agnes Valenti , Doruk Efe Gökmen , Robin Worreby , Valerio Peri , Sebastian D. Huber

Cellular solids and micro-lattices are a class of lightweight architected materials that have been established for their unique mechanical, thermal, and acoustic properties. It has been shown that by tuning material architecture, a…

Materials Science · Physics 2024-03-12 Shengzhi Luan , Enze Chen , Joel John , Stavros Gaitanaros

Compressed sensing has become a widely accepted paradigm to construct high dimensional cluster expansion models used for statistical mechanical studies of atomic configuration in complex multicomponent crystalline materials. However, strict…

Materials Science · Physics 2022-01-05 Luis Barroso-Luque , Julia H. Yang , Gerbrand Ceder

Traditional design cycles for new materials and assemblies have two fundamental drawbacks. The underlying physical relationships are often too complex to be precisely calculated and described. Aside from that, many unknown uncertainties,…

Soft materials (e.g., enveloped viruses, liposomes, membranes and supercooled liquids) simultaneously deform or display collective behaviors, while undergoing atomic scale vibrations and collisions. While the multiple space-time character…

Soft Condensed Matter · Physics 2014-01-03 Peter Ortoleva , Abhishek Singharoy , Stephen Pankavich

The design of porous infill structures presents significant challenges due to their complex geometric configurations, such as the accurate representation of geometric boundaries and the control of localized maximum stress. In current…

Optimization and Control · Mathematics 2025-10-14 Shuzhi Xu , Hiroki Kawabe , Kentaro Yaji

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…

A new graph-based order parameter is introduced for the characterization of atomistic structures. The order parameter is universal to any material/chemical system, and is transferable to all structural geometries. Three sets of data are…

Materials Science · Physics 2022-03-22 James Chapman , Nir Goldman , Brandon Wood

The shear-modulus and yield-stress of amorphous solids are important material parameters, with the former determining the rate of increase of stress under external strain and the latter being the stress value at which the material flows in…

Materials Science · Physics 2015-05-13 Edan Lerner , Itamar Procaccia , Emily S. C. Ching , H. G. E Hentschel

We present the first public release of plasmonX, a novel open-source code for simulating the plasmonic response of complex nanostructures. The code supports both fully atomistic and implicit descriptions of nanomaterials. In particular, it…

Mesoscale and Nanoscale Physics · Physics 2026-01-07 Tommaso Giovannini , Pablo Grobas Illobre , Piero Lafiosca , Luca Nicoli , Luca Bonatti , Stefano Corni , Chiara Cappelli

Numerous aqueous systems host elements in multiple redox states, with wide ranging implications such as their influence on the formation/dissolution of minerals, water toxicity, and nutrient cycling. To uncover governing mechanisms and…

Materials Science · Physics 2025-01-22 Shishir Mundra , Mohit Pundir , Barbara Lothenbach , David S. Kammer , Ueli M. Angst

Mechanical deformation of amorphous solids can be described as consisting of an elastic part in which the stress increases linearly with strain, up to a yield point at which the solid either fractures or starts deforming plastically. It is…

Disordered Systems and Neural Networks · Physics 2020-03-25 Baoshuang Shang , Pengfei Guan , Jean-Louis Barrat