Results 191 to 200 of about 52,686 (280)

From MAX to MXene: Unveiling Robust Magnetism and Half-Metallicity in Cr₂ZnC and Its Half-Metallic 2D Cr₂C Through Ab-Initio Investigation. [PDF]

Nanomaterials (Basel)
Lokbaichi A, Gueddouh A, Gueribiz D, Rougab M, Lagoun B, Elhamra F, Mahammedi A, Marfoua B.   +7 more
europepmc   +1 more source

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

Advanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob, Karsten Reuter, Johannes T. Margraf   +2 more
wiley   +1 more source

Targeting the TRIM28-EZH2 Protein-Protein Interface With Cysteine-Reactive Covalent Inhibitors: A Computational Blueprint for Cancer Therapy. [PDF]

Chem Biodivers
Kehinde IO, Mzozoyana V, Zamisa SJ, Faya M, Soliman MES.   +4 more
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Research on Temperature Control Method of Rice Noodles Extruder Based on APSO-MPC. [PDF]

Sensors (Basel)
Zhang M, Yang Y, Gao G, Li Z, He Y, Li P, Lv H.   +6 more
europepmc   +1 more source

Flexible Memory: Progress, Challenges, and Opportunities

Advanced Intelligent Discovery, EarlyView.
Flexible memory technology is crucial for flexible electronics integration. This review covers its historical evolution, evaluates rigid systems, proposes a flexible memory framework based on multiple mechanisms, stresses material design's role, presents a coupling model for performance optimization, and points out future directions.
Ruizhi Yuan, Sen Chen, Minghui Guo, Ziliang Cui, Yujie Du, Jing Liu   +5 more
wiley   +1 more source

Application of the Richards function to serum antibody titration. [PDF]

Front Immunol
Kovács Á, Papp K, Prechl J, Pfeil T.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

Constructing (101)-Oriented Anatase TiO₂ Seed Layers on Amorphous Microchannel Plate Glass: Surface Energetics and Template-Assisted Oriented Growth. [PDF]

Nanomaterials (Basel)
Li X, Cai H, Wang W, Zhao X, Guo XY, Ma MN, Zhu YY, Li KM, Liu H.   +8 more
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

Advanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source