MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**

Despite rapid progress in the field of metal–organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, ii) training and optimizing ML models by employing the MOF database, and iii) predicting the synthesis conditions for new MOF structures. The ML models, even at an initial stage, exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey. The automated synthesis prediction is available via a web‐tool on https://mof‐synthesis.aimat.science

Vorhersage der MOF‐Synthese durch automatisches Data‐Mining und maschinelles Lernen

Trotz großer Fortschritte auf dem Gebiet der metallorganischen Gerüststrukturen (MOF) ist das volle Potential des Maschinellen Lernens (ML) für die Vorhersage von MOF-Syntheseparametern bisher noch nicht erschlossen. In diesem Beitrag wird dargestellt, wie Methoden des ML für die Rationalisierung und Beschleunigung von MOF-Entwicklungsverfahren eingesetzt werden können, indem die Synthesebedingungen der MOFs direkt anhand ihrer Kristallstruktur vorhergesagt werden. Unser Ansatz stützt sich auf: i) die Erstellung der ersten MOF-Synthese-Datenbank durch automatische Extraktion der Syntheseparameter aus der Fachliteratur, ii) das Trainieren und die Optimierung von ML-Modellen mit Daten der MOF-Datenbank und iii) die ML basierte Vorhersage der Synthesebedingungen neuer MOF-Strukturen. Schon jetzt übertreffen die Ergebnisse der Vorhersagemodelle die Vorhersagen menschlicher ExpertInnen, welche in einer Befragung ermittelt wurden. Die automatisierte Synthesevorhersage ist über ein Web-Tool unter https://mof-synthesis.aimat.science verfügbar

Cocrystallization, high-pressure/low-temperature behaviour and vapochromism in a family of aurophilic copper–gold supramolecular networks

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Long-term whole blood DNA preservation by cost-efficient cryosilicification

This work was supported by the National Natural Science Foundation of China (21972047 to W.Z., 52003086 to Q.L.), Guangdong Provincial Pearl River Talents Program (2019QN01Y314 to Q.L.), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2019ZT08Y318 to W.Z.), Natural Science Foundation of Guangdong Province, China (2021A1515010724 to Q.L.), China Postdoctoral Science Foundation (2020M672625, 2021T140213 to Q.L.), Science and Technology Project of Guangzhou, China (202102020352 to W.Z., 202102020259 to Q.L.), the Fundamental Research Funds for the Central Universities of China. The authors thank the support from the Guangzhou Women and Children’s Medical Center and Laboratory Animal Research Center of the South China University of Technology. S.W. acknowledges funding from the Basque Government Industry Department under the ELKARTEK and HAZITEK programs.Deoxyribonucleic acid (DNA) is the blueprint of life, and cost-effective methods for its long-term storage could have many potential benefits to society. Here we present the method of in situ cryosilicification of whole blood cells, which allows long-term preservation of DNA. Importantly, our straightforward approach is inexpensive, reliable, and yields cryosilicified samples that fulfill the essential criteria for safe, long-term DNA preservation, namely robustness against external stressors, such as radical oxygen species or ultraviolet radiation, and long-term stability in humid conditions at elevated temperatures. Our approach could enable the room temperature storage of genomic information in book-size format for more than one thousand years (thermally equivalent), costing only 0.5 $/person. Additionally, our demonstration of 3D-printed DNA banking artefacts, could potentially allow 'artificial fossilization'.Publisher PDFPeer reviewe

Zwitterionic coordination compounds: synthesis and characterization of Co and Pd metallates

The nucleophilic addition of aminophosphanes to alkyl- and aryl- isothiocyanate leads to the formation of the zwitterionic thioamidyl-phosphonium (P+C(S)N−R) functional group. Within this family of its compounds, EtNHC(S)Ph2PNPPh2C(S)NEt (HEtSNS) can be prepared by reacting Ph2PNHPPh2 (dppa) in EtNCS as the reaction medium. In this thesis, we present a comparison between various metal complexes families and how this exceptionally flexible ligand adjust itself to best fit the metal requirement as a function of the metal centre and chemical conditions of the environment

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning

Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly predicting the synthesis conditions of a MOF based on its crystal structure. Our approach is based on: (i) establishing the first MOF synthesis database via automatic extraction of synthesis parameters from the literature, (ii) training and optimizing ML models by employing the MOF database, and (iii) predicting the synthesis conditions for new MOF structures. The ML models even at an initial stage exhibit a good prediction performance, outperforming human expert predictions, obtained through a synthesis survey

The Importance of Dean Flow in Microfluidic Nanoparticle Synthesis: A ZIF-8 Case Study

The Dean Flow, a physics phenomenon that accounts for the impact of channel curvature on fluid dynamics, has great potential to be used in microfluidic synthesis of nanoparticles. This study explores the impact of the Dean Flow on the synthesis of ZIF-8 particles. Several variables that influence the Dean Equation (the mathematical expression of Dean Flow) are tested to validate the applicability of this expression in microfluidic synthesis, including the flow rate, radius of curvature, channel cross sectional area, and reagent concentration. It is demonstrated that the current standard of reporting, providing only the flow rate and crucially not the radius of curvature, is an incomplete description that will invariably lead to irreproducible syntheses across different laboratories. An alternative standard of reporting is presented and it is demonstrated how the sleek and simple math of the Dean Equation can be used to precisely tune the final dimensions of high quality, monodisperse ZIF-8 nanoparticles between 40 and 700 nm