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Journal of Chemical Theory and Computation|January 7, 2022
Bridging the Experiment-Calculation Divide: Machine Learning Corrections to Redox Potential Calculations in Implicit and Explicit Solvent ModelsEugen Hruska, Ariel Gale, Fang LiuThe Journal of Chemical Physics|July 9, 2021
Quantum chemistry for molecules at extreme pressure on graphical processing units: Implementation of extreme-pressure polarizable continuum modelAriel Gale, Eugen Hruska, Fang LiuThe Journal of Chemical Physics|April 2, 2022
AutoSolvate: A toolkit for automating quantum chemistry design and discovery of solvated moleculesEugen Hruska, Ariel Gale, Xiao Huang, et al.Physical Chemistry Chemical Physics : PCCP|May 9, 2023
Δ-Machine learning for quantum chemistry prediction of solution-phase molecular properties at the ground and excited statesXu Chen, Pinyuan Li, Eugen Hruska, et al.Journal of Chemical Theory and Computation|November 10, 2020
Extensible and Scalable Adaptive Sampling on SupercomputersEugen Hruska, Vivekanandan Balasubramanian, Hyungro Lee, et al.The Journal of Chemical Physics|January 3, 2019
Quantitative comparison of adaptive sampling methods for protein dynamicsEugen Hruska, Jayvee R Abella, Feliks Nüske, et al.Statistics in Medicine|June 26, 2018
Assessment of Bayesian expected power via Bayesian bootstrapFang LiuBiometrics|June 4, 2013
A revisit to sample size and power calculations for testing odds ratio in two independent binomialsFang LiuJournal of Biopharmaceutical Statistics|August 20, 2010
An extension of Bayesian expected power and its application in decision makingFang LiuPageof 864