Results 171 to 180 of about 12,284 (247)

Oxygen‐Driven Structural Reorganization by Trace Water Enabling Fast Li‐Ion Transport in a Pliable Solid Electrolyte

Advanced Energy Materials, EarlyView.
Trace water is acting as a constructive mediator in 2LiCl–GaF3, markedly increasing ionic conductivity while reorganizing local coordination. Hydration creates localized Li+ solvation environments and partially dissociates ion pairs, enhancing Li‐ion mobility.
Youngkyung Kim, Jae‐Seung Kim, Kwang Ho Shin, Jingyu Choi, Beom Jin Park, Jinyeong Choe, Jiwon Seo, Chanhyun Park, Jeongjae Lee, Dong‐Hwa Seo, Sung‐Kyun Jung   +10 more
wiley   +1 more source

Density functional study of PuC and PuC_0.75O_0.25. [PDF]

RSC Adv
Yang R, Zhang Z, Tang B.
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

First-Principles insights into group-V impurities and their impact on germanium detector performance. [PDF]

Sci Rep
Aryal S, Batista ER, Wang G.
europepmc   +1 more source

Limitations of Foundation Models in Energy Materials Simulations: A Case Study in Polyanion Sodium Cathode Materials

Advanced Intelligent Discovery, EarlyView.
Several simulation techniques are used to explore static and dynamic behavior in polyanion sodium cathode materials. The study reveals that universal machine learning interatomic potentials (MLIPs) struggle with system‐specific chemistry, emphasizing the need for tailored datasets.
Martin Hoffmann Petersen, Chiku Parida, Jonas Busk, Jin Hyun Chang, Arghya Bhowmik, Juan Maria Garcia Lastra   +5 more
wiley   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Hidden Charge-Order in the Mixed-Valent K_0.75Li₂Cr₆O₁₂ High-Pressure Oxide. [PDF]

Adv Sci (Weinh)
Arévalo-López AM, Ritter C, Huvé M, Mentré O.   +3 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

First principles study of flat silicon rich 2D alloys Si_xBe_y. [PDF]

Discov Nano
Takahashi M.
europepmc   +1 more source

Discovery of Novel Materials with Giant Dielectric Constants via First‐Principles Phonon Calculations and Machine Learning

Advanced Intelligent Discovery, EarlyView.
We discovered novel materials with giant dielectric constants by combining first‐principles phonon calculations and machine learning. Screening 525 perovskites identified six candidates. RbNbO3 was synthesized under pressure and showed ε ≈ 800–1000. This validates our framework as a powerful tool for high‐performance dielectric materials discovery.
Hiroki Moriwake, Kazuki Shitara, Rie Yokoi, Takeharu Kato, Isao Tanaka, Ayako Yamamoto, Kimitoshi Murase, Yosuke Hamasaiki, Yoshitaka Ehara, Mitsuru Itoh   +9 more
wiley   +1 more source