Organic Materials of Tomorrow: Horizons of Artificial Intelligence
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena +3 more
wiley +1 more source
Phase Engineering of Atomically Precise Nanoclusters (APNCs) of Gold and Beyond
Engineering the structural phase of materials is of paramount importance for both fundamental research and practical applications. In this Review, we summarize the recent progress in controlling the phases of atomically precise nanoclusters (APNCs) of gold, silver and copper, as well as bimetallic systems. The phase‐enabled material properties of APNCs
Yitong Wang +4 more
wiley +1 more source
Building Capacity and Strengthening Collaboration: A PBRN Conference Update. [PDF]
Haddad L, Gross N.
europepmc +1 more source
Template effect and kinetic control enable crystal‐phase engineering of Ru nanocrystals, granting access to either metastable fcc‐Ru or stable hcp‐Ru with distinct surface structures, thermal stabilities, and catalytic behaviors. Moreover, the hcp‐Ru can further serve as an epitaxial template to direct Pd and Rh nanocrystals into the metastable hcp ...
Jianlong He +3 more
wiley +1 more source
Designing Nursing Policy Intelligence for 2040: Japan as a Leading Case. [PDF]
Kubota K.
europepmc +1 more source
Phase Engineering of Nanomaterials (PEN): Evolution, Current Challenges, and Future Opportunities
This review summarizes the synthesis, phase transition, advanced characterization spanning ex situ to in situ and operando techniques, and diverse applications of phase engineering of nanomaterials (PEN). It further outlines key challenges and future opportunities, such as phase stability, architecture control, and artificial intelligence (AI)‐driven ...
Ye Chen +7 more
wiley +1 more source
Data‐Driven Materials Science for Energy‐Sustainable Applications
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley +1 more source
Family planning self-care: From global frameworks to local meaning, perceptions, experiences and opportunities in Niger. [PDF]
Fotso JC +4 more
europepmc +1 more source
Closed‐Loop Solid‐State Synthesis Planning for Materials Discovery With Large Language Models
Leveraging literature data, we build a large‐language‐model‐driven workflow that extracts synthesis steps from 4407 papers, retrieves similar precedents, and generates candidate solid‐state synthesis recipes. The system benchmarks against ground‐truth and then operates in a closed loop with experiments to synthesize oxy‐selenide electrolyte materials ...
Dong Won Jeon +9 more
wiley +1 more source
Response to 'Upgrade procedures of pacemakers or implantable cardioverter-defibrillators require creative clinical decision making and careful planning that begins at the first implantation'. [PDF]
Keene D +4 more
europepmc +1 more source

