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Alan Aspuru-Guzik

19 accepted papers

2026

Coupled Cluster con MoLe: Molecular Orbital Learning for Neural Wavefunctions

Luca Anthony Thiede, Abdulrahman Aldossary, Andreas Burger, Jorge Campos-Gonzalez-Angulo, Alex Zook, Melisa Alkan +5
ICML 2026poster

Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and predicting properties that cl…

Cited by 0Source ↗Scholar ↗
2026

Derivative Informed Learning of Exchange-Correlation Functionals

Eike S. Eberhard, Luca Anthony Thiede, Abdulrahman Aldossary, Andreas Burger, Nicholas Gao, Vignesh Bhethanabotla +2
ICML 2026poster

Machine-learned (ML) XC-functionals promise improved accuracy, but overfit to training energies and basis sets without proper regularization. We introduce Derivative Informed XC-Loss (DI-Loss), a loss that regularizes ML-XC training by supervising energy gradients on the Grassmannian of density matr…

Cited by 0Source ↗Scholar ↗
2026

Global Plane Waves From Local Gaussians: Periodic Charge Densities in a Blink

Jonas Elsborg, Felix Aertebjerg, Luca Anthony Thiede, Alan Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik
ICML 2026poster

We introduce ELECTRAFI, a fast, end-to-end differentiable model for predicting periodic charge densities in crystalline materials. ELECTRAFI constructs anisotropic Gaussians in real space and exploits their closed-form Fourier transforms to analytically evaluate plane-wave coefficients via the Poiss…

Cited by 0Source ↗Scholar ↗
2025

AnyPlace: Learning Generalizable Object Placement for Robot Manipulation

Yuchi Zhao, Miroslav Bogdanovic, Chengyuan Luo, Steven Tohme, Kourosh Darvish, Alan Aspuru-Guzik +2
CoRL 2025poster

Object placement in robotic tasks is inherently challenging due to the diversity of object geometries and placement configurations. We address this with AnyPlace, a two-stage method trained entirely on synthetic data, capable of predicting a wide range of feasible placement poses for real-world task…

Cited by 0Source ↗code ↗Scholar ↗
2025

ELECTRA: A Cartesian Network for 3D Charge Density Prediction with Floating Orbitals

Jonas Elsborg, Luca Thiede, Alan Aspuru-Guzik, Tejs Vegge, Arghya Bhowmik
NeurIPS 2025spotlight

We present the Electronic Tensor Reconstruction Algorithm (ELECTRA) - an equivariant model for predicting electronic charge densities using floating orbitals. Floating orbitals are a long-standing concept in the quantum chemistry community that promises more compact and accurate representations by p…

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2025

Efficient Evolutionary Search Over Chemical Space with Large Language Models

Haorui Wang, Marta Skreta, Cher Tian Ser, Wenhao Gao, Lingkai Kong, Felix Strieth-Kalthoff +8
ICLR 2025poster

Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in molecular discovery, traverse chemical space by perf…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2025

Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts

Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alan Aspuru-Guzik, Arnaud Doucet +3
ICML 2025spotlight

While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling inference-time behavior in a principled manner, e.g. for composing multiple pretrained models. Existing classifier-free guidance methods use a simple heuristic to mix…

Cited by 2Source ↗PDF ↗code ↗Scholar ↗
2025

Stiefel Flow Matching for Moment-Constrained Structure Elucidation

Austin Henry Cheng, Alston Lo, Kin Long Kelvin Lee, Santiago Miret, Alan Aspuru-Guzik
ICLR 2025poster

Molecular structure elucidation is a fundamental step in understanding chemical phenomena, with applications in identifying molecules in natural products, lab syntheses, forensic samples, and the interstellar medium. We consider the task of predicting a molecule's all-atom 3D structure given only it…

Cited by 0Source ↗PDF ↗Scholar ↗
2024

A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules?

Agustinus Kristiadi, Felix Strieth-Kalthoff, Marta Skreta, Pascal Poupart, Alan Aspuru-Guzik, Geoff Pleiss
ICML 2024poster

Automation is one of the cornerstones of contemporary material discovery. Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior domain knowledge into efficient exploration of a large molecular space. While such prior knowledge can take many forms, t…

Cited by 22Source ↗PDF ↗code ↗Scholar ↗
2024

Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling

Yuanqi Du, Michael Plainer, Rob Brekelmans, Chenru Duan, Frank Noe, Carla P Gomes +2
NeurIPS 2024spotlight

Rare event sampling in dynamical systems is a fundamental problem arising in the natural sciences, which poses significant computational challenges due to an exponentially large space of trajectories. For settings where the dynamical system of interest follows a Brownian motion with known drift, the…

Cited by 16Source ↗PDF ↗code ↗Scholar ↗
2024

Infusing Synthetic Data with Real-World Patterns for Zero-Shot Material State Segmentation

Sagi Eppel, Jolina Yining Li, Manuel S. Drehwald, Alan Aspuru-Guzik
NeurIPS 2024poster

Visual recognition of materials and their states is essential for understanding the physical world, from identifying wet regions on surfaces or stains on fabrics to detecting infected areas or minerals in rocks. Collecting data that captures this vast variability is complex due to the scattered and…

Cited by 1Source ↗PDF ↗Scholar ↗
2024

Position: Application-Driven Innovation in Machine Learning

David Rolnick, Alan Aspuru-Guzik, Sara Beery, Bistra Dilkina, Priya L. Donti, Marzyeh Ghassemi +5
ICML 2024poster

In this position paper, we argue that application-driven research has been systemically under-valued in the machine learning community. As applications of machine learning proliferate, innovative algorithms inspired by specific real-world challenges have become increasingly important. Such work offe…

Cited by 4Source ↗PDF ↗Scholar ↗
2024

Quantum Deep Equilibrium Models

Philipp Schleich, Marta Skreta, Lasse Bjørn Kristensen, Rodrigo Vargas-Hernandez, Alan Aspuru-Guzik
NeurIPS 2024poster

The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circuits (PQCs). Higher depth increases expressivity, but also results in a detrimen…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2023

GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths, Leo Klarner, Henry Moss, Aditya Ravuri, Sang T. Truong, Yuanqi Du +22
NeurIPS 2023poster

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to molecular repr…

Cited by 37Source ↗PDF ↗code ↗Scholar ↗
2023

One-Shot Recognition of Any Material Anywhere Using Contrastive Learning with Physics-Based Rendering

Manuel S. Drehwald, Sagi Eppel, Jolina Li, Han Hao, Alan Aspuru-Guzik
ICCV 2023poster

Visual recognition of materials and their states is essential for understanding the world, from determining whether food is cooked, metal is rusted, or a chemical reaction has occurred. However, current image recognition methods are limited to specific classes and properties and can't handle the vas…

Cited by 12PDF ↗code ↗Scholar ↗
2023

Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

AkshatKumar Nigam, Robert Pollice, Gary Tom, Kjell Jorner, John Willes, Luca Thiede +2
NeurIPS 2023poster

The efficient exploration of chemical space to design molecules with intended properties enables the accelerated discovery of drugs, materials, and catalysts, and is one of the most important outstanding challenges in chemistry. Encouraged by the recent surge in computer power and artificial intelli…

Cited by 36Source ↗PDF ↗code ↗Scholar ↗
2021

Seeing Glass: Joint Point-Cloud and Depth Completion for Transparent Objects

Haoping Xu, Yi Ru Wang, Sagi Eppel, Alan Aspuru-Guzik, Florian Shkurti, Animesh Garg
CoRL 2021oral

The basis of many object manipulation algorithms is RGB-D input. Yet, commodity RGB-D sensors can only provide distorted depth maps for a wide range of transparent objects due light refraction and absorption. To tackle the perception challenges posed by transparent objects, we propose TranspareNet,…

Cited by 62Source ↗Scholar ↗
2020

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space

AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alan Aspuru-Guzik
ICLR 2020poster

Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical…

Cited by 169Source ↗code ↗Scholar ↗
2015

Convolutional Networks on Graphs for Learning Molecular Fingerprints

David K. Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik +1
NeurIPS 2015poster

We introduce a convolutional neural network that operates directly on graphs.These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape.The architecture we present generalizes standard molecular feature extraction methods based on circular fi…

Cited by 4663Source ↗PDF ↗code ↗Scholar ↗