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Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
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Posts
Future Blog Post
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Blog Post number 4
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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Blog Post number 2
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Blog Post number 1
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portfolio
Portfolio item number 1
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publications
Dimension Formulae for Iterated Function Systems
Unpublished MSci dissertation.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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Variational Noise-Contrastive Estimation
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
Telescoping Density-Ratio Estimation
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
Enhanced gradient-based MCMC in discrete spaces
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
Orb: A Fast, Scalable Neural Network Potential
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
Orb-v3: atomistic simulation at scale
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
talks
Talk 1 on Relevant Topic in Your Field
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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