Sitemap - 2020 - ML4Sci

ML4Sci #29: AI in Science; NeurIPS wrap-ups; Algorithmic Idiocy

ML4Sci #28: DeepMind "solves" protein folding

ML4Sci #27: Some theses about ML4Sci

ML4Sci #26: Fourier Neural Operator for PDE's; Multi-fidelity Graph Networks for Deep Learning the Experimental Properties of Ordered and Disordered Materials

ML4Sci #25: A special issue on Batteries x AI

ML4Sci #24: Massive Open Catalyst Project by Facebook and Carnegie Mellon; Trends from ICLR

ML4Sci #23: Synthesis planning with Literature-trained Neural Networks; Kohn-Sham Equations as regularizer;

ML4Sci #22: Why I love Random Forests; AI as Software; Podcasts

ML4Sci #21: NeurIPS Workshops; Google Flooding and Weather Forecasting; AI predicts locust breeding grounds

ML4Sci #20: Discovering Symbolic Models from Deep Learning with Inductive Biases; Inverse Design of Crystals using Generalized Invertible Crystallographic Representation

ML4Sci #19: Pushing the limit of MD with ab-initio accuracy to 100 million atoms with ML; Randomized Automatic Differentiation

ML4Sci #18: Unveiling the Predictive Power of Static Structure in Glassy Systems; Learning to Simulate and Design for Structural Engineering

ML4Sci #17: Predicting the long-term stability of compact multiplanet systems; Weakly-Supervised DL of Heat Transport via Physics Informed Loss

ML4Sci #16: Reflections on 6 months of ML4Sci; Photo Up-Sampling Algorithm Shows Racial Bias; US Bills on National AI Cloud and NSF Funding;

ML4Sci #15: News from the World of Open Science; Bayesian Experimental Autonomous Researcher for Mechanical Design; Learning Graph Models for Template-Free Retrosynthesis

ML4Sci #14: Uncertainty Quantification using NNs for Molecular Property Prediction; SunDown: model-driven per-panel solar anomaly detection

ML4Sci #13: An Interpretable Mortality Prediction Model for COVID-19; ML-revealed Statistics of Li-battery Cathode Failure; Accelerated Discovery of CO2 Electrocatalysts using Active ML

ML4Sci #12: Thoughts on COVID-19, Scientific Gatekeeping, and Substack Newsletters

ML4Sci #11: Embedding Physical Domain Knowledge in Bayesian Networks for PV Process Optimization; DL for inverse design of EM metastructures; Deep elastic strain engineering of bandgaps

ML4Sci #10: TLDRs for Science Papers; First Principles Database of Ferroelectrics; Bayesian Optimization for Stanford Linear Accelerator Center Laser

ML4Sci #9: AutoML discovers new normalization-activation layer; Ph.D Thesis on Inverse Design for Photonics

ML4Sci #8: Defining the new AI-powered SaaS: Science as a Service

ML4Sci #7: Exascale Deep Learning for Scientific Inverse Problems; Learning Fluid Mechanics; MetNet: Another weather model from Google

ML4Sci #6: Neural Message Passing for Quantum Chemistry; Language Modeling for Protein Sequences; AI for diagnosing COVID-19 from Chest CT Scans

ML4Sci #5: High Throughput-experimentation and AI for optical property prediction; Predicting Cardiovascular health from retinal scans; AI, COVID-19, and Society

ML4Sci #4: Speeding up Scientific Simulations; Hamiltonian NN's; Discovering new Antibiotics with AI; AI4Science Report released by US National Labs

ML4Sci #3: Google Tackles Protein Folding; What can scientists learn from Computer Vision and Robotics Research; Lessons learned from Airbnb using Deep Learning; +Cool Science Highlight

ML4Sci #2: Nowcasting rain forecasts from radar; graph similarities predict zeolite properties; cycle-consistent GANs for climate change visualizations;

ML4Sci #1: Discovering new materials from abstracts; Designing diffractive metagratings with GAN's; How ML can help fight climate change; +Industry Highlight

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