Accelerating Materials Discovery For Optical Applications Using Machine Learning Natural Language Processing And Density Functional Theory
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Accelerating Materials Discovery for Optical Applications Using Machine Learning, Natural Language Processing and Density Functional Theory
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Catalysis Volume 33
Author: James Spivey
language: en
Publisher: Royal Society of Chemistry
Release Date: 2021-06-16
This volume looks at modern approaches to catalysis and reviews the extensive literature which bridges the gap from academic studies in the laboratory to practical applications in industry not only for catalysis field but also for environmental protection.
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track
This multi-volume set, LNAI 14941 to LNAI 14950, constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2024, held in Vilnius, Lithuania, in September 2024. The papers presented in these proceedings are from the following three conference tracks: - Research Track: The 202 full papers presented here, from this track, were carefully reviewed and selected from 826 submissions. These papers are present in the following volumes: Part I, II, III, IV, V, VI, VII, VIII. Demo Track: The 14 papers presented here, from this track, were selected from 30 submissions. These papers are present in the following volume: Part VIII. Applied Data Science Track: The 56 full papers presented here, from this track, were carefully reviewed and selected from 224 submissions. These papers are present in the following volumes: Part IX and Part X.