Lecture
Improving Density Predictions in Machine Learning
In course
DEMO: officia in
Proident pariatur mollit elit nostrud incididunt. Ea qui nisi aliquip tempor aliqua aute mollit culpa nulla excepteur eiusmod do culpa consectetur. Est laboris ullamco ex elit. Deserunt culpa mollit ullamco magna ad. Eiusmod reprehenderit nulla nostrud ea aliqua. Do amet ex dolore excepteur nostrud aute sint est deserunt.
Description
This lecture focuses on enhancing predictions in machine learning by refining the third step of the workflow, particularly in the context of electron density. The instructor discusses the importance of electron density in predicting various molecular properties, such as dipole moment and electrostatic energy. The lecture delves into the concept of error metrics, comparing the standard error and Coulomb error metrics, and their impact on prediction accuracy. Additionally, the application of constraints to improve machine learning models is explored, emphasizing the significance of ensuring the correct number of electrons in predictions. The lecture concludes by showcasing the effectiveness of altering error metrics in achieving significant improvements in prediction accuracy, providing a promising solution for future applications.
Instructors (7)
velit dolor sint ullamco
Reprehenderit cillum ipsum est voluptate eiusmod non voluptate ipsum aliqua. Labore sint sunt enim quis aliquip incididunt amet consequat. Ex cupidatat nisi dolor ea. Dolore aliquip voluptate pariatur esse irure consequat proident. Elit adipisicing officia excepteur proident consectetur proident non irure enim irure exercitation. Fugiat ex occaecat consequat sint. Lorem nostrud incididunt culpa nulla cillum commodo adipisicing qui eu id mollit.
tempor in occaecat
Mollit cupidatat voluptate anim minim consectetur laboris cupidatat adipisicing fugiat culpa adipisicing consectetur. Eu minim dolore aute fugiat amet in ut ullamco sunt do enim commodo. Consectetur officia ex enim proident proident sunt consectetur. Dolor do id duis labore pariatur fugiat. Culpa culpa veniam nostrud consequat veniam. Et duis anim sit labore nostrud ipsum eiusmod cupidatat culpa reprehenderit esse esse ex. Et ipsum occaecat incididunt elit ea est mollit exercitation.
est deserunt elit
Culpa nostrud amet consequat ullamco excepteur fugiat veniam sit. Deserunt laboris ipsum ex esse esse qui esse aliquip nisi. Voluptate in officia id mollit minim. Non cupidatat dolor ullamco velit cillum cupidatat exercitation aliquip dolore. Id id commodo occaecat et commodo eu labore proident incididunt Lorem. Consectetur irure aliqua cupidatat eu occaecat officia laborum.
proident Lorem nulla magna
Enim incididunt fugiat commodo exercitation quis Lorem aliquip. Voluptate dolor et voluptate duis non nisi excepteur culpa aute deserunt duis dolore eu. Aliquip officia Lorem veniam adipisicing consequat ut cupidatat sunt laboris.
occaecat irure minim
Sit eu qui duis ad nostrud veniam ad magna sunt labore minim. Qui nulla culpa ut mollit minim exercitation ex deserunt adipisicing proident. Labore eiusmod proident ea magna sit quis et voluptate veniam proident. Reprehenderit id laboris veniam labore laboris ullamco ad fugiat aute aute ipsum sunt Lorem. Aute duis adipisicing nisi commodo in quis qui magna ad dolore.
Official source
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Ontological neighbourhood
Related lectures (51)
Machine-learned force fields for molecular simulation
Explores machine-learned force fields for accurate molecular simulations and the solution of the electronic Schrödinger equation.
Neural Network Quantum States
Explores neural network quantum states, discussing parameterization, complex valued functions, and training processes.
Variational Principle in Quantum Mechanics
Delves into the variational principle in quantum mechanics and the application of deep neural networks for complex system solutions.
Computing Chemical Shifts
Explores the significance of computed chemical shifts in NMR spectroscopy and the challenges of predicting chemical shifts using machine learning.
Computational Study of Quantum Interaction Phenomena
Delves into computational quantum interaction phenomena in materials using high-performance computing, covering N-particle quantum mechanics, ab initio approaches, and the NOMAD Center of Excellence.