Results 141 to 150 of about 15,495,898 (198)

Learning potential energy surfaces of hydrogen atom transfer reactions in peptides.

Digit Discov
Neubert M, Reiser P, Gräter F, Friederich P.   +3 more
europepmc   +1 more source

Accelerating the Structure Exploration of Diverse Bi-Pt Nanoclusters via Physics-Informed Machine Learning Potential and Particle Swarm Optimization. [PDF]

Chemphyschem
Vangheluwe R, Clavaguéra C, Truong MT, Domin D, Pham HC, Marinica MC, Van-Oanh NT.   +6 more
europepmc   +1 more source

BranchIP: Learning Adaptive Equivariant Computation for Interatomic Potentials [PDF]

Laura Zichi, Gil Harari, Chuin Wei Tan, Marc L. Descoteaux, Albert Zhu, Menghang Wang, Yoel Zimmermann, H. T. Kung, Boris Kozinsky   +8 more
openalex   +1 more source

Biologically explainable multi-omics feature demonstrates greater learning potential by identifying tissue of origin, stages, and subtypes for pan-cancer classification. [PDF]

Sci Rep
Munquad S, Dash BK, Sengupta S, Singh V, Ushakiran Y, Das AB.   +5 more
europepmc   +1 more source

Machine Learning‐Assisted Analytical–Numerical Framework for Bound‐State Spectra of Screened and Perturbed Potentials [PDF]

Souraya Goumri‐Said, Mohammed Benali Kanoun   +1 more
openalex   +1 more source

Machine Learning for Screening Bioactive Compounds in Traditional Chinese Medicine: Computational Prediction, Multi-Omics Integration, and Clinical Translation Potential

Zi-Meng Qi, Jing-Zhen Wu, Pan Li, Tian-Yang Xu, Peng Yu   +4 more
openalex   +1 more source

Machine Learning Potential Analysis of Structural Transition in Cu and Ag Nanoparticles: From Icosahedral to Face-Centered Cubic. [PDF]

J Chem Theory Comput
Yang Y, Han J, Viñes F, Illas F.
europepmc   +1 more source

Midwifery students’ and teachers’ perceptions and needs regarding Extended Reality (XR) in educational training and its potential role in anxiety-related learning contexts [PDF]

Clara Pérez de los Cobos Cintas, Nicolas Vuillerme, Lionel Di Marco, Guillaume Thomann   +3 more
openalex   +1 more source

Unraveling disorder-to-order transitions and chemical ordering in PtCoM ternary alloys using machine learning potential. [PDF]

Chem Sci
Niu X, Zhen S, Zhang R, Li J, Zhang L.
europepmc   +1 more source

Efficient and Transferable Machine Learning Potentials for Zn-Coordinated Zeolitic Imidazolate Frameworks Through Structural Diversity Sampling and Transfer Learning [PDF]

Shang‐Wei Lin, Yen-Yung Wu, Li‐Chiang Lin   +2 more
openalex   +1 more source