A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published "in-house" efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties

Existencialismo en La Lucha por La Vida

El proposito principal de esta tesis es el de exponer los elernentos existencialistas que vernos en la trilogia La lucha por la vida, del escritor de la generaci6n de 1898, Pio Baroja. Esta la cornponen: La busca, Mala hierba, y Aurora roja. Partes de esta trilogia fueron escritas en 1904. Los existencialistas, nos hablan del incurable asilarniento del individuo, los absurdos rnecanisrnos de la sociedad que lo destruyen, el coraje del individuo al encarar la rnuerte pero a su vez aferrandose a la vida, la soledad, la agonia, la angustia, la incornunicabilidad de las almas, el peso y lo absurdo de la existencia. Baroja nunca se manifesto abiertamente corno un existencialista pero algunos estudiosos de la obra barojiana sefialan esta caracteristica del escritor vasco, entre ellos se destaca Gonzalez Lopez: Su individualismo existencialista le hizo mirar con recelo las instituciones politicas existentes, comenzando por el estado, corno los partidos deseosos de reforrnarlas. 11 1 Entre muchos de los rasgos de Baroja destaca su actitud frente a la vida. Se ha dicho que Baroja ve la sociedad y la vida como una farsa, impidiendo que el hombre se exprese a su libre albedrio: Su concepci6n de la vida es inseparable de su temperarnento. De sus paginas se desprenden incesantemente undas ideas sobre el hombre y el mundo que se inscriben a la perfecci6n en la linea del pesimismo existencial. 112 Baroja protesta abiertarnente contra lo que no permite que el hombre alcance SU maxima potencial. Baroja protesta contra todo: La iglesia, las universidades, y el gobierno. Y es aqui donde podemos ver esto rasgos existenciales

A community-powered search of machine learning strategy space to find NMR property prediction models

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published ‘in-house’ efforts. A meta-ensemble model constructed as a linear combination of the top predictions has a prediction accuracy which exceeds that of any individual model, 7-19x better than our previous state-of-the-art. The results highlight the potential of transformer architectures for predicting quantum mechanical (QM) molecular properties.</p