Results 81 to 90 of about 8,521,646 (245)

Data-efficient machine-learning interatomic potential for studying radiation effects in germanium

Machine Learning: Science and Technology
Exposure to harsh radiation environments leads to displacement damage in semiconductor materials, ultimately degrading the device performance. Molecular dynamics (MD) is a powerful method for simulating the dynamic processes of radiation-induced defect ...
Ruoyan Jin, Ali Hamedani, Andrea E Sand
doaj   +1 more source

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

Broadening Hard‐Magnet Discovery Beyond Symmetry Constraints via Unified Effective Anisotropy

Advanced Science, EarlyView.
A unified effective‐anisotropy descriptor (Keff) extends hard‐magnet screening across all seven crystal systems, beyond the uniaxial restriction of conventional searches. Machine‐learning screening of 9320 known ferromagnets and diffusion‐model generation together yield 38 rare‐earth‐free or ‐lean candidates with DFT‐validated magnetic hardness (κ > 1),
Hojae Kim, Hyeondeok Shin, Kyungju Nam, Seunghyo Noh, Donghwi Kim, Yousung Jung   +5 more
wiley   +1 more source

CaTiO3 Second-Principles Interatomic Potential

Second-principles interatomic potential for the perovskite ...
Ghosez, Philippe, Schmitt, Michael Marcus, Bastogne, Louis, Zhang, Huazhang, He, Xu   +4 more
core   +1 more source

Understanding and Designing Phase Change Materials: Insights From Atom Probe Tomography

Advanced Science, EarlyView.
Atom probe tomography can distinguish metallic, covalent, and metavalent bonds based upon their bond rupture; in particular, the probability to form more than one ion (PME) in laser‐assisted field evaporation. These findings open new avenues in understanding and designing phase change materials (PCMs) since they allow quantification of bonds in solids ...
Jan Köttgen, Nils von den Driesch, Alexander Pawlis, Matthias Wuttig   +3 more
wiley   +1 more source

Machine learning interatomic potential can infer electrical response

npj Computational Materials
Modeling the response of material and chemical systems to electric fields remains a longstanding challenge. Machine learning interatomic potentials (MLIPs) offer an efficient and scalable alternative to quantum mechanical methods, but do not by ...
Peichen Zhong, Dongjin Kim, Daniel S. King, Bingqing Cheng   +3 more
doaj   +1 more source

Tunability of Martensitic Transformation with Cohesive Energies for Fe_80−xMn_xCo₁₀Cr₁₀ High-Entropy Alloys

Metals
Multi-element alloys (e.g., non-equiatomic FeMnCoCr alloys) have attracted extensive attention from researchers due to the breaking of the strengthen-ductility tradeoff relationship.
Yu Cao, Xiaoliang Zhang, Daoxuan Zhou, Peng Wang, Deng Pan, Hongtao Wang   +5 more
doaj   +1 more source

Embedded-atom method interatomic potential for boron nanostructures

, 2020
Текст статьи не публикуется в открытом доступе в соответствии с политикой журнала.Parameters of embedded-atom method interatomic potential for boron are presented in this paper.
Zalizniak, V. E., Zolotov, O. A.
core   +1 more source

Neuromorphic Near‐Sensor and In‐Sensor Computing Enabled by Next‐Generation Material‐Based Sensors

Advanced Science, EarlyView.
This Review presents a structural framework that classifies neuromorphic sensing into near‐sensor and in‐sensor architectures, clarifying physical coupling between sensing and computation. The framework connects neural and synaptic device functions with recent advances in optical, mechanical, and chemical sensing, compares energy consumption and ...
Su Yeon Jung, Gwang Ya Kim, Sejin Kim, Hyunjong Seo, Se Gi Lee, Sang Min Won, Dongjoon Rhee, Joohoon Kang   +7 more
wiley   +1 more source

Equivariant tensor network potentials

Machine Learning: Science and Technology
Machine-learning interatomic potentials (MLIPs) have made a significant contribution to the recent progress in the fields of computational materials and chemistry due to the MLIPs’ ability of accurately approximating energy landscapes of quantum ...
M Hodapp, A Shapeev
doaj   +1 more source