One handed mode: On large screen phones, pin keyboard to the left or the right of the screen by long pressing on Enter and selecting the thumb icon.Symbols hints: Show quick hints on your keys to access symbols with a long press (enable in Google Keyboard Settings → Preferences → Long press for symbols).Gesture delete: Slide left from the delete key to quickly delete multiple words.Gesture cursor control: Slide your finger across the space bar to move the cursor.Sync your learned words across devices to improve suggestions: To turn on keyboard syncing, go to Settings → Language & input → Google Keyboard → Dictionary → Sync learned words.Long press a suggestion to remove it from your “learned words” or the keyboard dictionary. Type a word once and you can gesture type it or find it in suggestions next time. Learns as you type: No need to manually add words to a personal dictionary.Emoji: Press and hold Enter to choose from hundreds of emoji in any app (Android 4.4+).Word completion suggestions, automatic corrections, and next-word prediction based on the words you have typed.Voice Typing: Touch the microphone to simply dictate your text.Completes a word before you’ve finished the gesture, especially useful when gesturing longer words.
When you can’t find the right words, express yourself with hundreds of perfect emoji. Compose text on-the-go with Voice Typing. Glide through letters with Gesture Typing to enter words - just lift your finger to finish a word and gesture again, no spacebar required. Google Keyboard makes typing fast and easy with gesture and voice. We select a random subset of 100,000 molecules from this library and perform high-throughput electronic structure theory calculations to obtain Polarizability using the Kohn-Sham density functional theory (DFT).This release does not have a Play Store Description, so we grabbed one from version 5.97460: The derived insights provide a foundation for rational and targeted design that goes beyond traditional trial-and-error searches.įor this study, we created a library of 1.5 million small organic molecules using an initial set of 15 building blocks (and constraints on molecular weight and number of ring-moieties per molecule) via ChemLG. In particular, we explore the utility of various strategies that introduce highly polarizable moieties into the PI backbone to increase its RI yield. Using the data mining and machine learning program package ChemML, we analyze the top candidates with respect to prevalent structural features and feature combinations that distinguish them from less promising ones. This rapid and efficient approach yields a number of highly promising leads compounds.
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We deploy the virtual screening software ChemHTPS to automate the assessment of this extensive pool of PI structures in order to determine the performance potential of each candidate. Our study utilizes an RI prediction protocol based on a combination of first-principles and data modeling developed in previous work, which we employ on a large-scale PI candidate library generated with the ChemLG code. We present a high-throughput computational study to identify novel polyimides (PIs) with exceptional refractive index (RI) values for use as optic or optoelectronic materials. Accelerated Discovery of High-Refractive-Index Molecules
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