Physical Review Materials

From our Collection on Machine Learning for Materials Discovery and Understanding, researchers from Jilin University develop a machine learning potential to study how intrinsic point defects suppress thermal conductivity in gallium oxide. doi.org/10.1103/fdrt-2…

From our Collection on Machine Learning for Materials Discovery and Understanding, researchers from @DutOfficial present a machine learning interatomic potential for palladium systems to study the transition from isolated clusters to nanocrystals. doi.org/10.1103/y1gy-9…

New #EdSugg: researchers from @NIST used neutron reflectometry & SIMS to map depth-dependent structure of layered nickelate films, identifying that better substrate selection and reduction conditions can mitigate amorphization and improve film uniformity. doi.org/10.1103/9ldc-s…

New #EdSugg: researchers from @lpensl study how nonlinear oscillatory shear affects the sol-gel transition of colloidal silica suspensions. They show that mechanical perturbations can be utilized to design softer and more ductile materials. doi.org/10.1103/ng2j-n…

From our Collection on New Insights into Functional Materials through Advanced Electron Microscopy, researchers from @UTokyo_News_en demonstrate a new technique to directly measure nanoscale exchange parameters with high accuracy and in real space. doi.org/10.1103/wt54-n…

From our Collection on Machine Learning for Materials Discovery and Understanding, researchers from @GeorgiaTech present an AI-driven pipeline that uses virtual forward synthesis to design poly(ethylene terephthalate) replacement copolymers. doi.org/10.1103/wjcq-j…

New #EdSugg: researchers from @DIPCehu investigate how the intercalation of magnetic atoms influences the ground state of 2D transition metal dichalcogenides. The results highlight the tunability of spin-lattice interactions in van der Waals materials. doi.org/10.1103/gyfq-2…

From our Collection on Machine Learning for Materials Discovery and Understanding, researchers from @NUSingapore introduce a scalable machine learning framework to learn the macroscopic dynamics of large stochastic systems using small-system simulations. doi.org/10.1103/mlh4-h…

New #EdSugg: researchers from @TIFRScience connect directional memory to the nature of yielding. By simulating 2D polydisperse glasses, they show that the type of memory is controlled by deformation history, strain rate, and parent temperature. doi.org/10.1103/stkn-v…

From our Collection on Machine Learning for Materials Discovery and Understanding, researchers from NTNU in Norway use machine-learned force fields for simulating finite-temperature ferroelectric phase transitions. doi.org/10.1103/nkc5-r…

New #EdSugg: researchers from @FVB_adlershof and @penn_state reveal two distinct structural phases in intercalated monolayer silver. The results highlight the crucial interplay between atomic structure and electronic properties at confined interfaces. doi.org/10.1103/pc8w-h…

Join APS TV Host Clara Nellist as she sits down with the editors of PRX Intelligence, a new @APSPhysics journal. Watch them introduce their vision for a publication dedicated to research at the intersection of physics, AI & machine learning. #APSSummit26 youtu.be/3UpwDhHLGRE

New #EdSugg: researchers from @UT_Space_Inst reveal that the valence electron concentration can be used to tune the mechanical properties of high-entropy diborides. They can achieve materials that resist shear deformation and reach extreme superhardness. doi.org/10.1103/hqlp-d…

From our Collection on Functional Materials Through Electrochemical Ion Insertion, researchers from @MIT use first-principles calculations to explore how hydrogen intercalation affects polaron mobility in transition metal oxides. doi.org/10.1103/hds1-y…

New #EdSugg: Researchers from @maxplanckpress explore the "forgotten" Néel's L-type ferrimagnets with anomalous temperature dependencies using first-principles calculations, making these alloys promising candidates for spintronic applications. doi.org/10.1103/l913-4…

New #EdSugg: Researchers from @HiroshimaUnivEn introduce new topological magnetic textures: skyrmionium metamatter. This new class of tunable, heterogeneous magnetic lattices opens the door to highly reconfigurable magnonic and spintronic functionalities. doi.org/10.1103/s8xh-m…

From our Collection on Machine Learning for Materials Discovery and Understanding, researchers from @PKU1898 develop a new machine learning potential for tin and bridge the gap between ab initio precision and large-scale dynamic simulations. doi.org/10.1103/6psb-3…

New #EdSugg: @CUBoulder researchers reveal that multi-component cubic halide perovskites can simultaneously achieve upward band gap bowing and negative mixing enthalpy, accelerating the design of functional semiconductor alloys across diverse materials. doi.org/10.1103/tp35-8…

Dive into the low-temperature magnetization dynamics of Weyl semimetals with a new #EdSugg, where researchers from @USouthFlorida explore the thermal evolution of Co2MnGa thin films. doi.org/10.1103/lbnv-g…

Why does density functional theory underestimate the stability of the VCoNi random solid solution? A new #EdSugg by researchers from @ruhrunibochum resolves this discrepancy by calculating accurate Gibbs energies. doi.org/10.1103/9vbd-x…