@article{nokey,

title = {The Critical Role of Anharmonic Lattice Dynamics for Macroscopic Properties of the Visible Light Absorbing Nitride Semiconductor CuTaN2},

author = {F S Hegner and A Cohen and S S Rudel and S M Kronawitter and M Grumet and X Zhu and R Korobko and L Houben and C-M Jiang and W Schnick and G Kieslich and O Yaffe and I D Sharp and D A Egger},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aenm.202303059},

doi = {https://doi.org/10.1002/aenm.202303059},

issn = {1614-6832},

year  = {2024},

date = {2024-05-17},

journal = {Advanced Energy Materials},

volume = {14},

number = {19},

pages = {2303059},

abstract = {Abstract Ternary nitride semiconductors are rapidly emerging as a promising class of materials for energy conversion applications, offering an appealing combination of strong light absorption in the visible range, desirable charge transport characteristics, and good chemical stability. In this work, it is shown that finite-temperature lattice dynamics in CuTaN2 \textendash a prototypical ternary nitride displaying particularly strong visible light absorption \textendash exhibit a pronounced anharmonic character that plays an essential role in defining its macroscopic optoelectronic and thermal properties. Low-frequency vibrational modes that are Raman-inactive from symmetry considerations of the average crystal structure and unstable in harmonic phonon calculations are found to appear as intensive Raman features near room temperature. The atomic contributions to the anharmonic vibrations are characterized by combining Raman measurements with molecular dynamics and density functional theory calculations. This analysis reveals that anharmonic lattice dynamics have large ramifications on the fundamental properties of this compound, resulting in uniaxial negative thermal expansion and the opening of its bandgap to a near-optimal value for solar energy harvesting. The atomic-level understanding of anharmonic lattice dynamics, as well as the finding that they strongly influence key properties of this semiconductor at room temperature, have important implications for design of new functional materials, especially within the emerging class of ternary nitride semiconductors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Autonomous battery optimisation by deploying distributed experiments and simulations},

author = {M Vogler and S Steensen and F Ramirez and L Merker and J Busk and J M Carlsson and L Rieger and B Zhang and F Liot and G Pizzi and F Hanke and E Flores and H Hajiyani and S Fuchs and A Sanin and M Gaberscek and I Castelli and S Clark and T Vegge and A Bhowmik and H S Stein},

url = {https://chemrxiv.org/engage/chemrxiv/article-details/66448bfb21291e5d1d55f2a7},

doi = {10.26434/chemrxiv-2024-vfq1n},

year  = {2024},

date = {2024-05-16},

journal = {ChemRxiv},

abstract = {Non-trivial relationships link individual materials properties to device-level performance. Device optimisation therefore calls for new automation approaches beyond the laboratory bench with tight integration of different research methods. We demonstrate a Materials Acceleration Platform (MAP) in the field of battery research based on our problem-agnostic Fast Intentional Agnostic Learning Server (FINALES) framework, which integrates simulations and physical experiments without centrally controlling them. The connected capabilities entail the formulation and characterisation of electrolytes, cell assembly and testing, early lifetime prediction, and ontology-mapped data storage provided by institutions distributed across Europe. The infrastructure is used to optimise the ionic conductivity of electrolytes and the End Of Life (EOL) of lithium-ion coin cells by varying the electrolyte formulation. We rediscover trends in ionic conductivity and investigate the effect of the electrolyte formulation on the EOL. We further demonstrate the capability of our MAP to bridge diverse research modalities, scales, and institutions enabling system-level investigations under asynchronous conditions while handling concurrent workflows on the material- and system-level, demonstrating true intention-agnosticism.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Attention towards chemistry agnostic and explainable battery lifetime prediction},

author = {F Rahmanian and R M Lee and D Linzner and K Michel and L Merker and B B Berkes and L Nuss and H S Stein},

url = {https://doi.org/10.1038/s41524-024-01286-7},

doi = {10.1038/s41524-024-01286-7},

issn = {2057-3960},

year  = {2024},

date = {2024-05-10},

journal = {npj Computational Materials},

volume = {10},

number = {1},

pages = {100},

abstract = {Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed number of cycles, and are limited to a single discharge protocol. Here, an attention-based recurrent algorithm for neural analysis (ARCANA) architecture is developed and trained on an ultra-large, proprietary dataset from BASF and a large Li-ion dataset gathered from literature across the globe. ARCANA generalizes well across this diverse set of chemistries, electrolyte formulations, battery designs, and cycling protocols and thus allows for an extraction of data-driven knowledge of the degradation mechanisms. The model’s adaptability is further demonstrated through fine-tuning on Na-ion batteries. ARCANA advances the frontier of large-scale time series models in analytical chemistry beyond textual data and holds the potential to significantly accelerate discovery-oriented battery research endeavors.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}