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We present a differentiation framework for plane-wave density-functional theory (DFT) that combines the strengths of forward-mode algorithmic differentiation (AD) and density-functional perturbation theory (DFPT). In the resulting AD-DFPT…

Materials Science · Physics 2025-12-23 Niklas Frederik Schmitz , Bruno Ploumhans , Michael F. Herbst

We present algorithms for diffusion model sampling which obtain $\delta$-error in $\mathrm{polylog}(1/\delta)$ steps, given access to $\widetilde O(\delta)$-accurate score estimates in $L^2$. This is an exponential improvement over all…

Machine Learning · Computer Science 2026-04-28 Fan Chen , Sinho Chewi , Constantinos Daskalakis , Alexander Rakhlin

A comprehensive model of high-concentration phosphorus diffusion has been developed and simulation of phosphorus diffusion from a constant source (phosphosilicate glass) at a temperature of 890 Celsius degrees for 14.25 min. has been…

Materials Science · Physics 2019-05-28 O. I. Velichko

Impurity diffusion coefficients are entirely obtained from a low cost classical molecular statics technique (CMST). In particular, we show how CMST is appropriate in order to describe the impurity diffusion behavior mediated by a vacancy…

Materials Science · Physics 2014-06-10 Viviana P. Ramunni

The grain boundary (GB) microchemistry and precipitation behaviour in high-strength Al-Zn-Mg-Cu alloys has an important influence on their mechanical and electrochemical properties. Simulation of the GB segregation, precipitation, and…

Data fusion is an essential task in various domains, enabling the integration of multi-source information to enhance data quality and insights. One key application is in satellite remote sensing, where fusing multi-sensor observations can…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Niraj Chaudhari , Manmeet Singh , Naveen Sudharsan , Amit Kumar Srivastava , Harsh Kamath , Dushyant Mahajan , Ayan Paul

Large-scale density functional theory (DFT) calculations provide a powerful tool to investigate the atomic and electronic structure of materials with complex structures. This article reviews a large-scale DFT calculation method, the…

Materials Science · Physics 2022-08-31 Ayako Nakata , David R. Bowler , Tsuyoshi Miyazaki

Designing alloys for additive manufacturing (AM) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via AM) are challenging to…

We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible…

Density Functional Theory (DFT) calculations of electrode material properties in high energy density storage devices like lithium batteries have been standard practice for decades. In contrast, DFT modelling of explicit interfaces in…

Materials Science · Physics 2020-06-24 Kevin Leung

We present an efficient ab initio dynamical mean-field theory (DMFT) implementation for quantitative simulations in solids. Our DMFT scheme employs ab initio Hamiltonians defined for impurities comprising the full unit cell or a supercell…

Strongly Correlated Electrons · Physics 2020-03-05 Tianyu Zhu , Zhi-Hao Cui , Garnet Kin-Lic Chan

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered,…

Materials Science · Physics 2020-07-08 Rickard Armiento

Accelerated design of novel hard coating materials requires state-of-the-art computational tools, which include data-driven techniques, building databases, and training machine learning models against the databases. In this work, we present…

Materials Science · Physics 2021-11-24 H. Levämäki , F. Tasnadi , D. G. Sangiovanni , L. J. S. Johnson , R. Armiento , I. A. Abrikosov

The present work proposes an approach to obtain a basis-set correction based on density-functional theory (DFT) for the computation of molecular properties in wave-function theory (WFT). This approach allows one to accelerate the basis-set…

Chemical Physics · Physics 2022-05-18 Diata Traore , Julien Toulouse , Emmanuel Giner

The high cost and accessibility problem associated with large datasets hinder the development of large-scale visual recognition systems. Dataset Distillation addresses these problems by synthesizing compact surrogate datasets for efficient…

Computer Vision and Pattern Recognition · Computer Science 2026-03-31 Tongfei Liu , Yufan Liu , Bing Li , Weiming Hu

Open material databases storing hundreds of thousands of material structures and their corresponding properties have become the cornerstone of modern computational materials science. Yet, the raw outputs of the simulations, such as the…

A reliable prediction of interatomic force constants in disordered alloys is an outstanding problem. This is due to the need for a proper treatment of multisite (atleast pair) correlation within a random environment. The situation becomes…

Materials Science · Physics 2014-04-14 Rajiv K. Chouhan , Aftab Alam , Subhradip Ghosh , Abhijit Mookerjee

DFT calculations have become widespread in both chemistry and materials, because they usually provide useful accuracy at much lower computational cost than wavefunction-based methods. All practical DFT calculations require an approximation…

Chemical Physics · Physics 2022-03-15 Eunji Sim , Suhwan Song , Stefan Vuckovic , Kieron Burke

DFT is a widely used method to compute properties of materials, which are often collected in databases and serve as valuable starting points for further studies. In this article, we present the Materials Cloud Three-Dimensional Structure…

In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants,…

Materials Science · Physics 2022-10-17 Ivan Novikov , Olga Kovalyova , Alexander Shapeev , Max Hodapp