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Benjamin Kurt Miller

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5 papers

2 author rows

TMLR Journal 2026 Journal Article

Generalizing Coverage Plots for Simulation-based Inference

Maximilian Lipp
Benjamin Kurt Miller
Lyubov Amitonova
Patrick Forré

Simulation-based inference (SBI) aims to find the probabilistic inverse of a non-linear function by fitting the posterior with a generative model on samples. Applications demand accurate uncertainty quantification, which can be difficult to achieve and verify. Since the ground truth model is implicitly defined in SBI, we cannot compute likelihood values nor draw samples from the posterior. This renders two-sample testing against the posterior impossible for any practical use and calls for proxy verification methods such as expected coverage testing. We introduce a differentiable objective that encourages coverage in the generative model by parameterizing the dual form of the total variation norm with neural networks. However, we find that coverage tests can easily report a good fit when the approximant deviates significantly from the target distribution and give strong empirical evidence and theoretical arguments why the expected coverage plot is, in general, not a reliable indicator of posterior fit. To address this matter, we introduce a new ratio coverage plot as a better alternative to coverage, which is not susceptible to the same blind spots. It comes at the price of estimating a ratio between our model and the ground truth posterior, which can be done using standard algorithms. We provide experimental results that back up this claim, and provide multiple algorithms for estimating ratio coverage.

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ICML Conference 2025 Conference Paper

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching

Aaron J. Havens
Benjamin Kurt Miller
Bing Yan
Carles Domingo-Enrich
Anuroop Sriram
Daniel S. Levine 0003
Brandon M. Wood
Bin Hu

We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the first on-policy approach that allows significantly more gradient updates than the number of energy evaluations and model samples, allowing us to scale to much larger problem settings than previously explored by similar methods. Our framework is theoretically grounded in stochastic optimal control and shares the same theoretical guarantees as Adjoint Matching, being able to train without the need for corrective measures that push samples towards the target distribution. We show how to incorporate key symmetries, as well as periodic boundary conditions, for modeling molecules in both cartesian and torsional coordinates. We demonstrate the effectiveness of our approach through extensive experiments on classical energy functions, and further scale up to neural network-based energy models where we perform amortized conformer generation across many molecular systems. To encourage further research in developing highly scalable sampling methods, we plan to open source these challenging benchmarks, where successful methods can directly impact progress in computational chemistry. Code & and benchmarks provided at https: //github. com/facebookresearch/adjoint_sampling.

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ICML Conference 2025 Conference Paper

All-atom Diffusion Transformers: Unified generative modelling of molecules and materials

Chaitanya K. Joshi
Xiang Fu 0005
Yi-Lun Liao
Vahe Gharakhanyan
Benjamin Kurt Miller
Anuroop Sriram
Zachary W. Ulissi

Diffusion models are the standard toolkit for generative modelling of 3D atomic systems. However, for different types of atomic systems – such as molecules and materials – the generative processes are usually highly specific to the target system despite the underlying physics being the same. We introduce the All-atom Diffusion Transformer (ADiT), a unified latent diffusion framework for jointly generating both periodic materials and non-periodic molecular systems using the same model: (1) An autoencoder maps a unified, all-atom representations of molecules and materials to a shared latent embedding space; and (2) A diffusion model is trained to generate new latent embeddings that the autoencoder can decode to sample new molecules or materials. Experiments on MP20, QM9 and GEOM-DRUGS datasets demonstrate that jointly trained ADiT generates realistic and valid molecules as well as materials, obtaining state-of-the-art results on par with molecule and crystal-specific models. ADiT uses standard Transformers with minimal inductive biases for both the autoencoder and diffusion model, resulting in significant speedups during training and inference compared to equivariant diffusion models. Scaling ADiT up to half a billion parameters predictably improves performance, representing a step towards broadly generalizable foundation models for generative chemistry. Open source code: https: //github. com/facebookresearch/all-atom-diffusion-transformer

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ICML Conference 2024 Conference Paper

FlowMM: Generating Materials with Riemannian Flow Matching

Benjamin Kurt Miller
Ricky T. Q. Chen
Anuroop Sriram
Brandon M. Wood

Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrangements of atoms in a periodic lattice only a vanishingly small percentage are thermodynamically stable, which is a key indicator of the materials that can be experimentally realized. Two fundamental tasks in this area are to (a) predict the stable crystal structure of a known composition of elements and (b) propose novel compositions along with their stable structures. We present FlowMM, a pair of generative models that achieve state-of-the-art performance on both tasks while being more efficient and more flexible than competing methods. We extend Riemannian Flow Matching to suit the symmetries inherent to crystals: translation, rotation, permutation, and periodic boundary conditions. Our framework enables the freedom to choose the flow base distributions, drastically simplifying the problem of learning crystal structures compared with diffusion models. In addition to standard benchmarks, we validate FlowMM’s generated structures with quantum chemistry calculations, demonstrating that it is $\sim$3x more efficient, in terms of integration steps, at finding stable materials compared to previous open methods.

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ICML Conference 2022 Conference Paper

Generative Coarse-Graining of Molecular Conformations

Wujie Wang
Minkai Xu
Chen Cai
Benjamin Kurt Miller
Tess E. Smidt
Yusu Wang 0001
Jian Tang 0005
Rafael Gómez-Bombarelli

Coarse-graining (CG) of molecular simulations simplifies the particle representation by grouping selected atoms into pseudo-beads and therefore drastically accelerates simulation. However, such CG procedure induces information losses, which makes accurate backmapping, i. e. , restoring fine-grained (FG) coordinates from CG coordinates, a long-standing challenge. Inspired by the recent progress in generative models and equivariant networks, we propose a novel model that rigorously embeds the vital probabilistic nature and geometrical consistency requirements of the backmapping transformation. Our model encodes the FG uncertainties into an invariant latent space and decodes them back to FG geometries via equivariant convolutions. To standardize the evaluation of this domain, we further provide three comprehensive benchmarks based on molecular dynamics trajectories. Extensive experiments show that our approach always recovers more realistic structures and outperforms existing data-driven methods with a significant margin.

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