Results 211 to 220 of about 52,570 (259)

An integrated molecular-thermodynamic framework for analyzing nanobubbles in supersaturated liquids. [PDF]

RSC Adv
Ghamartale A, Shahini E, Jain A, Manica R, Berg P, Tang T.   +5 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

The Optoelectronic, Vibrational, and Thermodynamic Attributes of 2D TMD-MoWX₄ (X = S, Se) Alloys and Janus TMD-MoWS₂Se₂ Alloy: A DFT Approach. [PDF]

ACS Omega
Fernandes Gatinho AS, Navis CN, de Souza Barbosa L, Barboza CA, Azevedo DL, Moreira E.   +5 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

Mechanically robust and thermodynamically stable FeSc₂Z₄ (Z = S, Se) spinels for future spintronic architectures. [PDF]

RSC Adv
Anwar A, Noor NA, Mustafa GM, Atiq S, Ibrahim A, Laref A.   +5 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

Predictive optimization of curcumin nanocomposites using hybrid machine learning and physics informed modeling. [PDF]

Sci Rep
Rahdar A, Fathi-Karkan S, Shirzad M.
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

Interaction of Lysozyme with Sulfated β-Cyclodextrin: Dissecting Salt and Hydration Contributions. [PDF]

Molecules
Walkowiak JJ.
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