Results 181 to 190 of about 2,637,115 (207)

Maximum Stability Point Tracking Stabilizes Wide‐Bandgap Mixed‐Halide Perovskite Solar Cells

Advanced Energy Materials, EarlyView.
A maximum stability point tracking (MSPT) strategy suppresses charge trapping‐induced halide segregation, enabling wide‐bandgap perovskite solar cells to achieve significantly enhanced operational stability compared to conventional maximum power point tracking (MPPT).
Seongheon Kim, Geon Kim, Taeeon Kim, Kwang Seop Shin, Jongchan Kim, Mansoo Choi, Yun Seog Lee, Namyoung Ahn   +7 more
wiley   +1 more source

Competing Charge Separation Pathways Govern Charge Generation in Organic Photovoltaic Blends: Insights From Transient Electron Spin Resonance

Advanced Energy Materials, EarlyView.
Transient electron spin resonance distinguishes interfacial charge‐transfer states from separated charges, revealing competing charge separation pathways in organic photovoltaic blends. The relative contributions of fast charge separation and a pathway mediated by spin‐polarized charge‐transfer states vary across different blends, with high‐efficiency ...
Jack M. S. Palmer, Claudia E. Tait
wiley   +1 more source

Hybrid Two‐Step Inkjet Printing and Dip‐Coating Enable Scalable 30%‐Efficient Perovskite/Silicon Tandem Solar Cells

Advanced Energy Materials, EarlyView.
Adopting a scalable hybrid two‐step deposition process route, textured perovskite/silicon tandem solar cells achieve over 30% power‐conversion efficiency. The process route replaces all non‐scalable spin‐coating steps, including organic precursor deposition, self‐assembled monolayer formation, and passivation layer deposition, with scalable techniques ...
Raphael Pesch, Lena P. Rothbauer, Johannes Schenk, Lingyi Fang, Theresa Kuechle, Julian Petry, Uma K. Dangudubiyyam, Richard Thelen, David B. Ritzer, Ting Pan, Daniel O. Baumann, Mohammad Gholipoor, Tonghan Zhao, Nils W. Rosemann, Jinzhao Li, Ulrich Lemmer, Ulrich W. Paetzold   +16 more
wiley   +1 more source

A computational study of Ising‐based solvers for discrete landscape exploration in process optimization

AIChE Journal, EarlyView.
ABSTRACT Conceptual process design combines discrete configuration choices with continuous operating decisions, often yielding difficult mixed‐integer nonlinear or simulation‐based optimization problems. This work presents an exploratory computational assessment of Ising‐based solvers, simulated annealing, quantum annealing, and entropy computing, as ...
Yirang Park, David E. Bernal Neira
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +1 more source

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

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Noisy intermediate-scale quantum algorithms

Reviews of Modern Physics, 2022
Sukin Sim, Leong-Chuan Kwek, Wai-Keong Mok   +2 more
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