Results 171 to 180 of about 95,657 (238)

How Does Transcription-Associated Mutagenesis Shape tRNA Microevolution? [PDF]

Genome Biol Evol
Baños H, Wang L, Simonti C, Paaby AB, Heitsch C.   +4 more
europepmc   +1 more source

Evolution of Physical Intelligence Across Scales

Advanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu, Tianyun Huang, Aming Li, Pengyu Lv, Tingting Qin, Wenhui Chen, Hui Wei, Huiling Duan   +7 more
wiley   +1 more source

Transcriptome-wide prediction of heat-sensitive RNA structures in <i>Zea mays</i>. [PDF]

Front Plant Sci
Eisenhauer MW, Dapour AO, Rouse WB, Moss WN.   +3 more
europepmc   +1 more source

A Unifying Approach to Self‐Organizing Systems Interacting via Conservation Laws

Advanced Intelligent Discovery, EarlyView.
The article develops a unified way to model and analyze self‐organizing systems whose interactions are constrained by conservation laws. It represents physical/biological/engineered networks as graphs and builds projection operators (from incidence/cycle structure) that enforce those constraints and decompose network variables into constrained versus ...
F. Barrows, G. Zhang, S. Anand, Z. Chen, J. Lin, A. Desai, S. Martiniani, F. Caravelli   +7 more
wiley   +1 more source

Design of Fe-Co-Cr-Ni-Mn-Al-Ti Multi-Principal Element Alloys Based on Machine Learning. [PDF]

Materials (Basel)
Xu X, He Z, Zheng K, Che L, Zhao F, Hua D.   +5 more
europepmc   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

A Paired Database of Predicted and Experimental Protein Peptide Binding Information. [PDF]

Sci Data
Torres JA, Kieslich CA, Pantazes RJ.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Predicting and Validating the Performance of Zn-Al-Mg Coatings through an Integrated Thermodynamic, Molecular Dynamics, and Electrochemical Approach. [PDF]

ACS Omega
Chaouki A, Ben Ali M, Cifuentes SC, Bedmar J, Rams J, Tsipas SA, Sahir Z, Benabdallah I, El Fatimy A, Naamane S.   +9 more
europepmc   +1 more source