Related papers: LLM-driven discovery for carbon allotropes with bo…
The growing need for structural materials with strength, mechanical stability, and durability in extreme environments is driving the development of high entropy alloys. These are materials with near equiatomic mixing of five or more…
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional mechanical properties and the vast compositional space for new HEAs. However, understanding their novel physical mechanisms and then using these…
Four sp3 carbon allotropes with six, eight, and 16 atoms per primitive cell have been derived using a combination of metadynamics simulations and topological scan. A chiral orthorhombic phase oC16 (C2221) was found to be harder than…
A machine learning-accelerated high-throughput (HTP) workflow for the discovery of magnetic materials is presented. As a test case, we screened quaternary and all-$d$ Heusler compounds for stable compounds with large magnetocrystalline…
Large language models (LLMs) leverage chain-of-thought (CoT) techniques to tackle complex problems, representing a transformative breakthrough in artificial intelligence (AI). However, their reasoning capabilities have primarily been…
Crystal structure prediction (CSP), which aims to predict the three-dimensional atomic arrangement of a crystal from its composition, is central to materials discovery and mechanistic understanding. However, given the composition in a unit…
Search for new high-temperature superconductors and insight into their superconducting mechanism are of fundamental importance in condensed matter physics. The discovery of near-room temperature superconductivity at more than a million…
Controlled fusion energy is deemed pivotal for the advancement of human civilization. In this study, we introduce $\textbf{LPI-LLM}$, a novel integration of Large Language Models (LLMs) with classical reservoir computing paradigms tailored…
A thorough in situ characterization of materials at extreme conditions is challenging, and computational tools such as crystal structural search methods in combination with ab initio calculations are widely used to guide experiments by…
Original carbon allotropes C12 and C16 called superglitter and squarodiamond from relationships with literature glitter and squaroglitter respectively are shown through DFT-based geometry to be cohesive with energy dependent properties as…
Amorphous and amorphous porous palladium are key materials for catalysis, hydrogen storage, and functional applications, but their complex structures present computational challenges. This study employs a deep neural network trained on…
A new kind of three-dimensional carbon allotropes, termed carbon honeycomb (CHC), has recently been synthesized [PRL 116, 055501 (2016)]. Based on the experimental results, a family of graphene networks are constructed, and their electronic…
Predicting solid-solid phase transitions remains a long-standing challenge in materials science. Solid-solid transformations underpin a wide range of functional properties critical to energy conversion, information storage, and thermal…
High-entropy materials shift the traditional materials discovery paradigm to one that leverages disorder, enabling access to unique chemistries unreachable through enthalpy alone. We present a self-consistent approach integrating…
Thermal transport in complex solids is governed by local structure, defects, and anisotropy, yet most continuum models still rely on oversimplified, homogenized conductivities. Here, we bridge atomistic and continuum descriptions by…
Two mechanisms of changes from 2D to 3D (D = dimensionality) involving 2D C(sp2) trigonal paving to C(sp3) tetrahedral stacking are proposed through puckering of the 2D layers on one hand and interlayer insertion of extra C on the other…
The theoretical framework, which is built from the first-principles results, is successfully developed for investigating emergent two-dimensional (2D) materials, as it is clearly illustrated by carbon substitution in silicene. Computer…
Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge. While Large Language Models…
An original mechanism is proposed for a pressure-induced transformation of orthorhombic C12 from ground state normal pressure (NP) sp2/sp3 allotrope to ultra-dense and ultra-hard HP sp3 form. Upon volume decrease, the trigonal C=C parallel…
We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating…