Results 161 to 170 of about 8,521,646 (245)

Transfer Learning from Homogeneous to Heterogeneous: Fine-Tuning a Pretrained Interatomic Potential for Multicomponent Mo Alloys with Localized Substitutional Alloying. [PDF]

Materials (Basel)
Fang L, Qin L, Zhang L, Zhou H, He X, Ren Z, Zhang T, Liu Y.   +7 more
europepmc   +1 more source

Molecular and lattice dynamics study of the MgO-SiO2 system using a transferable interatomic potential

, 1996
We have developed a transferable interatomic potential to describe interatomic interaction in minerals of MgO-SiO2 composition. The potential was obtained in such a way that except for the data on thermoelastic properties of MgO (periclase) and SiO2 ...
Belonoshko, AB,, Dubrovinsky, LS,
core  

Mode‐Resolved Phonon Dynamics Under Chemical Pressure in SnTe Thermoelectrics

Advanced Science, EarlyView.
Chemical pressure reshapes low‐energy optical phonons in SnTe, altering their dispersion and scattering with heat‐carrying acoustic modes. Positive and negative pressure states modify phonon frequencies and lifetimes in distinct ways, revealing how mode‐selective lattice perturbations regulate acoustic–optical interactions and suppress lattice thermal ...
Zhihao Li, Jianhong Hu, Panpan Peng, Peng Cao, Hui Zhang, Yujie Zong, Yanhui Wu, Hongchao Wang   +7 more
wiley   +1 more source

Leveraging active learning-enhanced machine-learned interatomic potential for efficient infrared spectra prediction. [PDF]

NPJ Comput Mater
Bhatia N, Rinke P, Krejčí O.
europepmc   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

A Machine Learning Interatomic Potential Data Set and Model for Catalysis with Local Fine-Tuning to Chemical Accuracy. [PDF]

JACS Au
Wu Z, Zhou L, Hou P, Liu Y, Wang R, Guo T, Liu JC.   +6 more
europepmc   +1 more source

Determining the Relationship Between Composition, Structure, and Device Properties of GexSe1‐x‐Based Selector‐Only Memory

Advanced Electronic Materials, EarlyView.
Composition‐dependent structural evolution in GeXSe1‐X selector‐only memory (SOM) is correlated with device switching behavior. Increasing Ge strengthens network rigidity, suppresses atomic motion, and stabilizes threshold switching, while narrowing the memory window. The revealed structure–property relationship provides a guideline for compositionally
Tien Anh Nguyen, Changdeok Han, Dipannita Ghosh, Suyong Chae, Moongyu Choi, Sanghyun Ban, Myoungsub Kim, Sangmin Yoo, Seung Hwan Lee, Sieun Chae   +9 more
wiley   +1 more source

TrIP2: Expanding the Transformer Interatomic Potential Demonstrates Architectural Scalability for Organic Compounds. [PDF]

J Phys Chem A
Ebbert J, Hedelius B, Joy J, Ess DH, Corte DD.   +4 more
europepmc   +1 more source

Atomic‐Scale Mechanisms of Anisotropic Thermal Decomposition in GeSn Alloys With Stepwise Pinning

Advanced Electronic Materials, EarlyView.
Combining in situ TEM with DFT calculations, this work elucidates atomic‐scale anisotropic thermal decomposition in GeSn films, identifying laminar receding in defect‐free regions and stepwise pinning at stacking faults. Driven by crystallographic anisotropy and defect‐mediated energetic penalties, these findings establish a physical framework for ...
YiXin Wang, Qi Liu, XiYan Chang, Chi Xu, Tao Wang, XinZheng Li, ChunLai Xue   +6 more
wiley   +1 more source

Transferability of Data Sets between Machine-Learned Interatomic Potential Algorithms. [PDF]

J Chem Theory Comput
Niblett SP, Kourtis P, Magdău IB, Grey CP, Csányi G.   +4 more
europepmc   +1 more source