Results 121 to 130 of about 9,526 (252)
Machine‐learning potentials are increasingly taking on the exploratory tasks of homogeneous catalysis, enabling rapid conformer sampling and reaction‐space mapping. However, when selectivity depends on subtle electronic effects, electronic‐structure methods remain essential.
Maxime Ferrer +3 more
wiley +1 more source
Graphical representation of a data‐driven framework for Fischer‐Tropsch synthesis (FTS) modelling and optimization. Abstract This study presents a data‐driven approach for predicting the relationships between catalyst design, process conditions, and product selectivity in Fischer–Tropsch synthesis (FTS).
Doaa M. Hassan +2 more
wiley +1 more source
Quenching the Hubbard Model: Comparison of Nonequilibrium Green's Function Methods
ABSTRACT We benchmark nonequilibrium Green's function (NEGF) approaches for interaction quenches in the half‐filled Fermi–Hubbard model in one and two dimensions. We compare fully self‐consistent two‐time Kadanoff–Baym equations (KBE), the generalized Kadanoff–Baym ansatz (GKBA), and the recently developed NEGF‐based quantum fluctuations approach (NEGF‐
Jan‐Philip Joost +3 more
wiley +1 more source
Unveiling Spin Transition at Single-Particle Level in Levitating Spin Crossover Nanoparticles. [PDF]
Pinilla-Cienfuegos E +6 more
europepmc +1 more source
Sr‐LMO microspheres were synthesized by one‐pot spray pyrolysis, forming multi‐phase lithium manganese oxide with Sr‐doped LiMn2O4, Sr2Mn2O5, and Li2MnO3‐like domains. This structure refines particles, suppresses Jahn–Teller distortion, and adds Li+ pathways, delivering superior cycling stability and rate performance over undoped and commercial LMO ...
Jae Hyeon Choi +7 more
wiley +1 more source
Reverse micelle synthesis and downsizing effects in iron(iii) spin crossover materials. [PDF]
Lazaro SE +6 more
europepmc +1 more source
This article reviews the fundamental consequences of strong correlations on excitations and elementary steps of energy conversion leading to new opportunities to control energy conversion. Examples include friction at surfaces, thermal transport, and photovoltaic energy conversion.
Vasily Moshnyaga +14 more
wiley +1 more source
Machine Learning Prediction for Fe(II) Spin-Crossover Complex in the Same Spin State Using Geometrical and Topological Descriptors. [PDF]
Okawa N, Miyao T.
europepmc +1 more source
Spin Crossover by Encapsulation
Encapsulation by synthetic hosts can transform the spin states of square planar Ni(II)(acen) and Co(II)(tap) complexes. Upon encapsulation, the red Ni(II) diamagnetic state was converted into a green paramagnetic state, whereas the Co(II) low spin (S = 1/2) state was changed into a coupled (S = 1/2 and S = 3/2) state. The host cages are noninnocent and
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