update for hdslee retreat talk

bib/bibliography.bib

@@ -449,7 +449,7 @@
 }
 @article{andraeHypothesesPrimaryEnergy2020,
-  title = {Hypotheses for {{Primary Energy Use}}, {{Electricity Use}} and {{CΟ2 Emissions}} of {{Global Computing}} and {{Its Shares}} of the {{Total Between}} 2020 and 2030},
+  title = {Hypotheses for {{Primary Energy Use}}, {{Electricity Use}} and {{CO2 Emissions}} of {{Global Computing}} and {{Its Shares}} of the {{Total Between}} 2020 and 2030},
   author = {Andrae, Anders S. G.},
   date = {2020},
   journaltitle = {WSEAS Transactions on Power Systems},
@@ -749,7 +749,7 @@ Subject\_term\_id: cheminformatics;computational-models;computational-science},
   url = {https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00094f},
   urldate = {2024-06-05},
   langid = {english},
-  keywords = {/unread,FZJ,ML,PGI,PGI-1/IAS-1,QML,QSVM,quantum computing,quantum machine learning,quantum transport,random forest,rec-by-ghosh,spin dynamics,spintronics,Spintronics,SVM,tight binding,transport properties},
+  keywords = {FZJ,ML,PGI,PGI-1/IAS-1,QML,QSVM,quantum computing,quantum machine learning,quantum transport,random forest,rec-by-ghosh,spin dynamics,spintronics,Spintronics,SVM,tight binding,transport properties},
   file = {/Users/wasmer/Nextcloud/Zotero/B. Ghosh_Ghosh_2023_Classical and quantum machine learning applications in spintronics.pdf}
 }
@@ -3588,7 +3588,7 @@ Subject\_term\_id: computational-methods;electronic-structure;theory-and-computa
   urldate = {2023-03-06},
   abstract = {Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.},
   langid = {english},
-  keywords = {/unread,computing,Deep learning,for introductions,Neuromorphic,NN,review,Spintronics,unconventional computing},
+  keywords = {computing,Deep learning,for introductions,Neuromorphic,NN,review,Spintronics,unconventional computing},
   file = {/Users/wasmer/Nextcloud/Zotero/Christensen et al_2022_2022 roadmap on neuromorphic computing and engineering.pdf}
 }
@@ -5769,7 +5769,7 @@ Junqi Yin\\
   urldate = {2024-07-17},
   abstract = {In the ‘Beyond Moore’s Law’ era, with increasing edge intelligence, domain-specific computing embracing unconventional approaches will become increasingly prevalent. At the same time, adopting a variety of nanotechnologies will offer benefits in energy cost, computational speed, reduced footprint, cyber resilience, and processing power. The time is ripe for a roadmap for unconventional computing with nanotechnologies to guide future research, and this collection aims to fill that need. The authors provide a comprehensive roadmap for neuromorphic computing using electron spins, memristive devices, two-dimensional nanomaterials, nanomagnets, and various dynamical systems. They also address other paradigms such as Ising machines, Bayesian inference engines, probabilistic computing with p-bits, processing in memory, quantum memories and algorithms, computing with skyrmions and spin waves, and brain-inspired computing for incremental learning and problem-solving in severely resource-constrained environments. These approaches have advantages over traditional Boolean computing based on von Neumann architecture. As the computational requirements for artificial intelligence grow 50 times faster than Moore’s Law for electronics, more unconventional approaches to computing and signal processing will appear on the horizon, and this roadmap will help identify future needs and challenges. In a very fertile field, experts in the field aim to present some of the dominant and most promising technologies for unconventional computing that will be around for some time to come. Within a holistic approach, the goal is to provide pathways for solidifying the field and guiding future impactful discoveries.},
   langid = {english},
-  keywords = {/unread,nanomaterials,Neuromorphic,physics,quantum computing,rec-by-sanvito,review,review-of-spintronics,skyrmions,spintronics,unconventional computing},
+  keywords = {nanomaterials,Neuromorphic,physics,quantum computing,rec-by-sanvito,review,review-of-spintronics,skyrmions,spintronics,unconventional computing},
   file = {/Users/wasmer/Nextcloud/Zotero/Finocchio et al_2024_Roadmap for unconventional computing with nanotechnology.pdf}
 }
@@ -8188,6 +8188,26 @@ Subject\_term\_id: condensed-matter-physics;theory-and-computation},
   file = {/Users/wasmer/Nextcloud/Zotero/Hodapp_Shapeev_2023_Equivariant Tensor Networks.pdf;/Users/wasmer/Zotero/storage/J2HAIBJ3/2304.html}
 }
+@article{hoffmannAntiskyrmionsStabilizedInterfaces2017,
+  title = {Antiskyrmions Stabilized at Interfaces by Anisotropic {{Dzyaloshinskii-Moriya}} Interactions},
+  author = {Hoffmann, Markus and Zimmermann, Bernd and Müller, Gideon P. and Schürhoff, Daniel and Kiselev, Nikolai S. and Melcher, Christof and Blügel, Stefan},
+  date = {2017-08-21},
+  journaltitle = {Nature Communications},
+  shortjournal = {Nat Commun},
+  volume = {8},
+  number = {1},
+  pages = {308},
+  publisher = {Nature Publishing Group},
+  issn = {2041-1723},
+  doi = {10.1038/s41467-017-00313-0},
+  url = {https://www.nature.com/articles/s41467-017-00313-0},
+  urldate = {2024-09-09},
+  abstract = {Chiral magnets are an emerging class of topological matter harboring localized and topologically protected vortex-like magnetic textures called skyrmions, which are currently under intense scrutiny as an entity for information storage and processing. Here, on the level of micromagnetics we rigorously show that chiral magnets can not only host skyrmions but also antiskyrmions as least energy configurations over all non-trivial homotopy classes. We derive practical criteria for their occurrence and coexistence with skyrmions that can be fulfilled by (110)-oriented interfaces depending on the electronic structure. Relating the electronic structure to an atomistic spin-lattice model by means of density functional calculations and minimizing the energy on a mesoscopic scale by applying spin-relaxation methods, we propose a double layer of Fe grown on a W(110) substrate as a practical example. We conjecture that ultra-thin magnetic films grown on semiconductor or heavy metal substrates with C2vsymmetry are prototype classes of materials hosting magnetic antiskyrmions.},
+  langid = {english},
+  keywords = {/unread,interfaces and thin films,Magnetic properties and materials,Spintronics,Surfaces},
+  file = {/Users/wasmer/Nextcloud/Zotero/Hoffmann et al. - 2017 - Antiskyrmions stabilized at interfaces by anisotropic Dzyaloshinskii-Moriya interactions.pdf}
+}
+
 @software{hoffmannMapleScriptsCalculation2019,
   title = {Maple Scripts for the Calculation of {{Hubbard}} Matrices and Their Subsequent Downfolding by {{Loewdin}}'s Partitioning},
   author = {Hoffmann, Markus and Ohs, Nicholas and Blügel, Stefan},
@@ -8771,6 +8791,25 @@ Subject\_term\_id: computational-methods;research-management},
   file = {/Users/wasmer/Nextcloud/Zotero/Inizan et al_2023_Scalable hybrid deep neural networks-polarizable potentials biomolecular.pdf;/Users/wasmer/Zotero/storage/ZQFN36VU/Inizan et al. - 2023 - Scalable hybrid deep neural networkspolarizable p.pdf}
 }
+@article{inmanCarbonForever2008,
+  title = {Carbon Is Forever},
+  author = {Inman, Mason},
+  date = {2008-12-01},
+  journaltitle = {Nature Climate Change},
+  volume = {1},
+  number = {812},
+  pages = {156--158},
+  publisher = {Nature Publishing Group},
+  issn = {1758-6798},
+  doi = {10.1038/climate.2008.122},
+  url = {https://www.nature.com/articles/climate.2008.122},
+  urldate = {2024-09-08},
+  abstract = {Carbon dioxide emissions and their associated warming could linger for millennia, according to some climate scientists. Mason Inman looks at why the fallout from burning fossil fuels could last far longer than expected.},
+  langid = {english},
+  keywords = {Climate Change,Climate Change/Climate Change Impacts,Environment,Environmental Law/Policy/Ecojustice,general},
+  file = {/Users/wasmer/Nextcloud/Zotero/Inman - 2008 - Carbon is forever.pdf;/Users/wasmer/Zotero/storage/PQLEIE29/climate.2008.html}
+}
+
 @book{internationalenergyagencyWorldEnergyOutlook2022,
   title = {World {{Energy Outlook}} 2022},
   author = {{International Energy Agency}},
@@ -15127,6 +15166,19 @@ Subject\_term\_id: magnetic-properties-and-materials},
   file = {/Users/wasmer/Nextcloud/Zotero/Ramsundar et al_2021_Differentiable Physics.pdf;/Users/wasmer/Zotero/storage/RGUHZPWB/2109.html}
 }
+@article{reinhardtGettingMaterialsOut2024,
+  entrysubtype = {magazine},
+  title = {Getting Materials out of the Lab - {{Works}} in {{Progress}}},
+  author = {Reinhardt, Benjamin},
+  date = {2024-05-17},
+  journaltitle = {Works in Progress},
+  url = {https://worksinprogress.co/issue/getting-materials-out-of-the-lab/},
+  urldate = {2024-09-08},
+  abstract = {Inventing new materials is only the first step. Getting them into mass production and use is just as hard.},
+  langid = {american},
+  file = {/Users/wasmer/Zotero/storage/JDI83HNC/getting-materials-out-of-the-lab.html}
+}
+
 @article{reiserGraphNeuralNetworks2021,
   title = {Graph Neural Networks in {{TensorFlow-Keras}} with {{RaggedTensor}} Representation (Kgcnn)},
   author = {Reiser, Patrick and Eberhard, André and Friederich, Pascal},
@@ -15270,6 +15322,19 @@ Subject\_term\_id: condensed-matter-physics;history;quantum-physics},
   file = {/Users/wasmer/Nextcloud/Zotero/2016_The rise of quantum materials.pdf;/Users/wasmer/Zotero/storage/YG3UAYEY/nphys3668.html}
 }
+@article{ritchieEnergyMix2024,
+  title = {Energy {{Mix}}},
+  author = {Ritchie, Hannah and Rosado, Pablo and Roser, Max},
+  date = {2024-03-25},
+  journaltitle = {Our World in Data},
+  shortjournal = {Our World in Data},
+  url = {https://ourworldindata.org/energy-mix},
+  urldate = {2024-09-09},
+  abstract = {Explore global data on where our energy comes from, and how this is changing.},
+  keywords = {/unread},
+  file = {/Users/wasmer/Zotero/storage/NNNJIXUM/energy-mix.html}
+}
+
 @online{robertsTensorNetworkLibraryPhysics2019,
   title = {{{TensorNetwork}}: {{A Library}} for {{Physics}} and {{Machine Learning}}},
   shorttitle = {{{TensorNetwork}}},
@@ -20149,7 +20214,7 @@ Subject\_term\_id: computational-methods;corrosion;mathematics-and-computing;the
   url = {https://doi.org/10.1021/acs.jctc.0c00347},
   urldate = {2024-06-15},
   abstract = {Machine learning techniques allow a direct mapping of atomic positions and nuclear charges to the potential energy surface with almost ab initio accuracy and the computational efficiency of empirical potentials. In this work, we propose a machine learning method for constructing high-dimensional potential energy surfaces based on feed-forward neural networks. As input to the neural network, we propose an extendable invariant local molecular descriptor constructed from geometric moments. Their formulation via pairwise distance vectors and tensor contractions allows a very efficient implementation on graphical processing units (GPUs). The atomic species is encoded in the molecular descriptor, which allows the restriction to one neural network for the training of all atomic species in the data set. We demonstrate that the accuracy of the developed approach in representing both chemical and configurational spaces is comparable to the one of several established machine learning models. Due to its high accuracy and efficiency, the proposed machine-learned potentials can be used for any further tasks, for example, the optimization of molecular geometries, the calculation of rate constants, or molecular dynamics.},
-  keywords = {/unread,AML,descriptors,GM-NN,GTO basis,library,MD17,ML,MLP,original publication,PhysNet,SchNet,with-code},
+  keywords = {AML,descriptors,GM-NN,GTO basis,library,MD17,ML,MLP,original publication,PhysNet,SchNet,with-code},
   file = {/Users/wasmer/Nextcloud/Zotero/Zaverkin_Kästner_2020_Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and.pdf}
 }