Copertina

Algorithmic differentiation for plane-wave DFT: materials design, error control and learning model parameters

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Schmitz, Niklas Frederik, Ploumhans, Bruno, Herbst, Michael F.
Natura:	Preprint
Pubblicazione:	2025
Soggetti:	Materials Science Computational Physics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

What can machine learning help with microstructure-informed materials modeling and design?
di: Peng, Xiang-Long, et al.
Pubblicazione: (2024)

Generative deep learning for the inverse design of materials
di: Long, Teng, et al.
Pubblicazione: (2024)

Pseudopotentials, an overlooked source and remedy of DFT errors
di: Ye, Kuiyu, et al.
Pubblicazione: (2025)

Multi-property directed generative design of inorganic materials through Wyckoff-augmented transfer learning
di: Yamazaki, Shuya, et al.
Pubblicazione: (2025)

Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
di: Shuang, Fei, et al.
Pubblicazione: (2025)

BMach: a Bayesian machine for optimizing Hubbard U parameters in DFT+U with machine learning
di: Das, Ritwik
Pubblicazione: (2024)

Universal materials model of deep-learning density functional theory Hamiltonian
di: Wang, Yuxiang, et al.
Pubblicazione: (2024)

Exploring parameter dependence of atomic minima with implicit differentiation
di: Maliyov, Ivan, et al.
Pubblicazione: (2024)

Composition design of refractory compositionally complex alloys using machine learning models
di: Liang, Tao, et al.
Pubblicazione: (2026)

A way to identify whether a DFT gap is from right reasons or error cancellations: The case of copper chalcogenides
di: Shen, Jiale, et al.
Pubblicazione: (2024)

Optimized effective potential forces with the plane-wave and pseudopotential method
di: Contant, Damian, et al.
Pubblicazione: (2024)

Can machines learn density functionals? Past, present, and future of ML in DFT
di: Akashi, Ryosuke, et al.
Pubblicazione: (2025)

Coarse-grained crystal graph neural networks for reticular materials design
di: Korolev, Vadim, et al.
Pubblicazione: (2023)

Insight into the effect of force error on the thermal conductivity from machine-learned potentials
di: Zhou, Wenjiang, et al.
Pubblicazione: (2024)

Deep-learning design of graphene metasurfaces for quantum control and Dirac electron holography
di: Han, Chen-Di, et al.
Pubblicazione: (2024)

Electrostatic interactions in atomistic and machine-learned potentials for polar materials
di: Monacelli, Lorenzo, et al.
Pubblicazione: (2024)

On the self-consistency of DFT-1/2
di: Cui, Hanli, et al.
Pubblicazione: (2022)

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost
di: Baghishov, Ilgar, et al.
Pubblicazione: (2025)

Large Language Models for Material Property Predictions: elastic constant tensor prediction and materials design
di: Liu, Siyu, et al.
Pubblicazione: (2024)

Machine-learned atomistic simulations reveal the basis of hydrogen-induced crack-plane transition in alpha-Fe
di: Xu, Jiaqin, et al.
Pubblicazione: (2026)

Screening of material defects using universal machine-learning interatomic potentials
di: Berger, Ethan, et al.
Pubblicazione: (2025)

The occupation dependent DFT-1/2 method
di: Yang, Shengxin, et al.
Pubblicazione: (2025)

Polymorphic crystallites model for monolayer amorphous materials
di: Zhu, Le-Ye, et al.
Pubblicazione: (2026)

Dynamically training machine-learning-based force fields for strongly anharmonic materials
di: Callsen, Martin, et al.
Pubblicazione: (2026)

Discovery of sustainable energy materials via the machine-learned material space
di: Grunert, Malte, et al.
Pubblicazione: (2025)

Advances in modeling complex materials: The rise of neuroevolution potentials
di: Ying, Penghua, et al.
Pubblicazione: (2025)

Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package
di: CampBell, Audrey, et al.
Pubblicazione: (2024)

TPDH-Graphene as a New Anodic Material for Lithium Ion Battery: DFT-Based Investigations
di: Quispe, Juan Gomez, et al.
Pubblicazione: (2024)

Machine learning-driven elasticity prediction in advanced inorganic materials via convolutional neural networks
di: Liu, Yujie, et al.
Pubblicazione: (2025)

douka: A universal platform of data assimilation for materials modeling
di: Watanabe, Aoi, et al.
Pubblicazione: (2025)

Thermodynamical study of N$_2$ clathrate hydrate from DFT calculations
di: Martin-Gondre, L., et al.
Pubblicazione: (2025)

Navigating chemical design spaces for metal-ion batteries via machine-learning-guided phase-field simulations
di: Taghavian, Hamed, et al.
Pubblicazione: (2025)

Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures
di: Harwani, Mohnish, et al.
Pubblicazione: (2024)

DFT calculations of magnetocrystalline anisotropy energy with fixed spin moment
di: Snarski-Adamski, Justyn, et al.
Pubblicazione: (2026)

Ultrafast controlling net magnetization in g-wave altermagnets via laser fields
di: Zhou, Zhaobo, et al.
Pubblicazione: (2025)

Accelerated discovery and design of Fe-Co-Zr magnets with tunable magnetic anisotropy through machine learning and parallel computing
di: Xia, Weiyi, et al.
Pubblicazione: (2025)

Predicting structure-dependent Hubbard U parameters for assessing hybrid functional-level exchange via machine learning
di: Cao, Zhendong, et al.
Pubblicazione: (2023)

A physics-aware deep learning model for shear band formation around collapsing pores in shocked reactive materials
di: Cheng, Xinlun, et al.
Pubblicazione: (2025)

Limited Diffusion of Silicon in GaN: A DFT Study Supported by Experimental Evidence
di: Kawka, Karol, et al.
Pubblicazione: (2025)

Efficient Krylov methods for linear response in plane-wave electronic structure calculations
di: Herbst, Michael F., et al.
Pubblicazione: (2025)