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DETERMINED BY WEIGHTS AND BIAS
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Classroom Contents
Machine Learning in Condensed Matter and Materials Physics
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- 1 Intro
- 2 Atomic-scale modeling of real materials ⚫ Foundations for the predictive modeling of chemicals and materials Key challenge: accurate electronic properties + sampling of fluctuations/defec
- 3 Predicting properties beyond potentials • Symmetry-adapted ML for tensors: CCSD-quality molecular polarizabilities & d • Electron charge density for molecules (and condensed phases!) • Single-particl…
- 4 Structural and functional properties, combined • Predicting any property accessible to quantum calculations • Realistic time and size scales, with first-principles accuracy and mapping of stru functi…
- 5 TUNNELING DENSITY OF STATES IN 1962
- 6 X-ray diffraction in 1913
- 7 Projective Measurements in 1922
- 8 DETERMINED BY WEIGHTS AND BIAS
- 9 Hypothesis test
- 10 Learn the sorting criteria for emerger
- 11 Discoveries
- 12 Machine Learning Quantum Emergence From Quantum Matter Data
