Machine Learning Configuration-Dependent Friction Tensors in Langevin Heatbaths; M. Sachs, W.G Stark, RJ Maurer, C Ortner, to appear in Machine Learning: Science & Technology
ACEpotentials. jl: A Julia implementation of the atomic cluster expansion; W. C. Witt, C. van der Oord, E. Gelžinytė, T. Järvinen, A. Ross, J. P. Darby, C. H. Ho, W. J. Baldwin, M. Sachs, J. Kermode, N. Bernstein, G. Csányi, C. Ortner; The Journal of Chemical Physics, 2023
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials; C. van der Oord, M. Sachs, D. P. Kovács, C. Ortner, G. Csányi; npj Computational Materials, 2023
Posterior computation with the Gibbs zig-zag sampler; M. Sachs*, D. Sen*, J. Lu, and D. B. Dunson, Bayesian Analysis, 2023.
Efficient Numerical Algorithms for the Generalized Langevin Equation; with B. Leimkuhler; SIAM Journal on Scientific on Computing, 2022
Non-reversible Markov chain Monte Carlo for sampling of districting maps; with G. Herschlag, J. C. Mattingly, and E. Wyse; preprint
Hypocoercivity Properties of Adaptive Langevin Dynamics; with B. Leimkuhler and G. Stoltz; SIAM J. Appl. Math., 2020.
Efficient posterior sampling for high-dimensional imbalanced logistic regression; D. Sen*, M. Sachs*, J. Lu, and D. B. Dunson; Biometrika, 2020.
Quadrature Points via Heat Kernel Repulsion; with J. Lu and S. Steinerberger; Constr. Approx., 2020.
Langevin Dynamics with Variable Coefficients and Nonconservative Forces: From Stationary States to Numerical Methods; M. Sachs, B. Leimkuhler, and V. Danos; Entropy. 2017.
Ergodic Properties of Quasi-Markovian Generalized Langevin Equations with Configuration Dependent Noise and Non-conservative Force; with B. Leimkuhler; Springer Proceedings in Mathematics and Statistics, 2019.
The generalised Langevin equation : asymptotic properties and numerical analysis; M. Sachs; The University of Edinburgh, 2018.
* equal contribution