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Unpublished MSci dissertation. The majority of the dissertation is an exposition of prior books and papers, but the final 10 pages contain original material, culminating in Theorem 7.19 which gives a lower bound on the Hausdorff dimension of a certain class of planar fractals.
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Published in The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
We propose a new method for estimating the parameters of energy-based, latent variable models. The core contribution is the derivation of a variational lower bound for the noise-contrastive estimation objective function.
Recommended citation: Rhodes, B. and Gutmann, M. U. (2019). Variational noise-contrastive estimation. InThe 22nd InternationalConference on Artificial Intelligence and Statistics, pages 2741–2750. http://benrhodes.github.io/files/vnce.pdf
Published in Advances in Neural Information Processing Systems - Spotlight (top 4% of submissions), 2020
We propose a new framework, Telescoping Density-ratio Estimation (TRE), that enables the estimation of ratios between highly dissimilar densities in high-dimensional spaces.
Recommended citation: Rhodes, B., Xu, K., and Gutmann, M. (2020). Telescoping Density-Ratio Estimation. In Advances in Neural Information Processing Systems http://benrhodes26.github.io/files/tre.pdf
Published in Transactions on Machine Learning Research, 2022
We introduce several discrete Metropolis-Hastings samplers that are conceptually inspired by MALA, and demonstrate their strong empirical performance across a range of challenging sampling problems in Bayesian inference and energy-based modelling. Methodologically, we identify why discrete analogues to \emph{preconditioned} MALA are generally intractable, motivating us to introduce a new kind of preconditioning based on auxiliary variables and the “Gaussian integral trick”.
Recommended citation: Enhanced gradient-based MCMC in discrete spaces. Rhodes, B. and Gutmann, M. Transactions on Machine Learning Research (2022). http://benrhodes26.github.io/files/enhanced_mcmc.pdf
We introduce Orb, a family of universal interatomic potentials for atomistic modelling of materials. Orb models are 3-6 times faster than existing universal potentials, stable under simulation for a range of out of distribution materials and, upon release, represented a 31% reduction in error over other methods on the Matbench Discovery benchmark. We explore several aspects of foundation model development for materials, with a focus on diffusion pretraining. We evaluate Orb as a model for geometry optimization, Monte Carlo and molecular dynamics simulations.
Recommended citation: Neumann, Mark, James Gin, Benjamin Rhodes, Steven Bennett, Zhiyi Li, Hitarth Choubisa, Arthur Hussey, and Jonathan Godwin. "Orb: A fast, scalable neural network potential." arXiv preprint arXiv:2410.22570 (2024). https://arxiv.org/abs/2410.22570
We introduce Orb-v3, the next generation of the Orb family of universal interatomic potentials. Models in this family expand the performance-speed-memory Pareto frontier, offering near SoTA performance across a range of evaluations with a >10x reduction in latency and > 8x reduction in memory. Our experiments systematically traverse this frontier, charting the trade-off induced by roto-equivariance, conservatism and graph sparsity. Contrary to recent literature, we find that non-equivariant, non-conservative architectures can accurately model physical properties, including those which require higher-order derivatives of the potential energy surface. This model release is guided by the principle that the most valuable foundation models for atomic simulation will excel on all fronts: accuracy, latency and system size scalability. The reward for doing so is a new era of computational chemistry driven by high-throughput and mesoscale all-atom simulations.
Recommended citation: Benjamin Rhodes, Sander Vandenhaute, Vaidotas Šimkus, James Gin, Jonathan Godwin, Tim Duignan and Mark Neumann. "Orb-v3: atomistic simulation at scale." arXiv preprint arXiv:2504.06231 (2025). https://arxiv.org/abs/2504.06231
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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