Alán Aspuru‐Guzik

AI-enhanced adaptive virtual screening of large libraries for ligand discovery

El Agente Cuántico: Automating quantum simulations

Evaluating Global Optimization and Generative Methods for Low-Energy and Diverse Crystal Structure Prediction Via Two Case Studies

Quantitative prediction of siRNA complexation by ionizable drugs enables their codelivery in nanoparticles

Faster Algorithmic Quantum and Classical Simulations by Corrected Product Formulas

Hamiltonian simulation using product formulas is arguably the most straightforward and practical approach for algorithmic simulation of a quantum system’s dynamics on a quantum computer. Here we present corrected product formulas (CPFs), a variation of product formulas achieved by injecting auxiliary terms called correctors into standard product formulas. We establish several correctors that improve the accuracy of standard product formulas by orders of magnitude when simulating Hamiltonians comprised of two exactly simulatable partitions, a common structure of lattice Hamiltonians. Importantly, injecting these correctors increases the overall simulation cost by only a small additive or multiplicative factor. We show that correctors are particularly advantageous for perturbed systems, where one partition has a relatively small norm compared to the other, as they allow the small norm to be utilized as an additional parameter for controlling the simulation error. We demonstrate the performance of CPFs by numerical simulations for several lattice Hamiltonians. Numerical results show that our theoretical error bounds for CPFs match or outperform the empirical error scaling of standard product formulas for these systems. We also demonstrate improvements offered by CPFs by implementing small-size systems on actual quantum hardware, as well as on noisy and noiseless quantum simulators. CPFs could be a valuable algorithmic tool for early fault-tolerant quantum computers with limited computing resources. As for standard product formulas, CPFs could also be used for simulations on a classical computer.

Adsorption Hysteresis Under Control: Tuning Host–Guest Interactions via a Genetic Algorithm

Mesoporous adsorbent materials offer a large volumetric capacity; however, cyclic adsorption/desorption processes in these systems often suffer from hysteresis and may require a significant pressure swing to access this capacity. To mitigate hysteresis, a proposed strategy is to include nucleation sites on the walls of the mesoporous material to facilitate droplet and bubble formation, lowering the free energy barriers to the respective phase transitions. It is unclear, however, what combination of adsorbate-adsorbent interactions and spatial patterning would be beneficial for a given application, considering that improvements to some sorption properties may come at the expense of other attributes. To understand these interconnected observables, we examine two model systems, planar-slit and cylindrical pores with tunable interaction sites, using GPU-accelerated transition matrix Monte Carlo simulations. The simulations provide a free energy map of the pressure-adsorption space in a matter of minutes, which we use to track adsorption isotherm characteristics as a function of adsorbent properties. We then leverage the rapid acquisition of simulation data to construct a genetic algorithm to iteratively modify interaction sites of the slit-pore wall to minimize the hysteresis of this system without sacrificing uptake. We find that the adsorption branch of the isotherm is easily modulated via the average host-guest interaction strength, but desorption is only adjustable if there is a suitable bubble nucleation site. Within the context of a slit-pore system, we identify relative interaction strengths and patch sizes required to gain control over both branches of the hysteresis loop.

Active Learning‐Accelerated Discovery of Fibrous Hydrogels with Tissue‐Mimetic Viscoelasticity

The rise of generative AI for metal-organic framework design and synthesis

Mapping the crystallization landscape of rare earth MOFs: a high-throughput investigation of structure, kinetics, and selectivity

Systematically exploring the multidimensional parameter space of metal-organic framework (MOF) crystallization remains challenging due to limited adoption of high-throughput (HT), automated experimental workflows. MOF development is dominated by manual synthesis and characterization methods and a trial-and-error approach, and the integration of HT MOF synthesis with HT characterization and analysis is uncommon. Here, we present a practical HT MOF discovery workflow that combines automated solvothermal synthesis with three scalable characterization methods. First, we characterize bulk structure using HT powder X-ray diffraction (PXRD) and rapid matching of PXRD data to reported MOF crystal structures. We also employ a custom machine-learning based computer vision (CV) model to identify MOF formation rates from images of sample vials. Finally, we develop a HT X-ray fluorescence (XRF) method to quantify elemental ratios in bimetallic MOF samples. As a case study, we investigate the crystallization of rare earth (RE) MOFs, systematically probing the effects of reaction conditions such as metal identity, linker structure, temperature, and acid concentration. We then leverage these insights to demonstrate a proof-of-concept selective crystallization from a mixed RE solution. Using our HT workflow, we performed 1488 unique MOF crystallization reactions and characterized the resulting samples through the collection of >800 PXRD patterns, CV analysis of >13 000 images, and elemental analysis measurements of 144 bimetallic crystallization reactions. We identified 5 previously unreported rare earth MOFs (NU-2501-NU-2505) and characterized their structures with single-crystal X-ray diffraction (SCXRD) and microcrystal electron diffraction (MicroED). Our HT approach enabled us to construct phase diagrams mapping out the crystallization preferences and formation kinetics for 18 distinct RE-MOF products. By unifying automated MOF synthesis with multimodal characterization, we demonstrate the efficient exploration of a complex synthetic landscape, generating insights into MOF structure, crystallization kinetics, and composition.

Bulky Phosphine Ligands Promote Palladium-Catalyzed Protodeboronation

The Suzuki-Miyaura cross-coupling reaction is plagued by protodeboronation, an undesirable side reaction with water that consumes the boronic acid derivatives required for the cross-coupling reaction. Meticulous mechanistic studies have previously established protodeboronation to be highly sensitive to the nature of the boronic reagent and reaction conditions. Particularly, the presence of bases, which are essential for the Suzuki-Miyaura coupling, is known to catalyze protodeboronation. However, protodeboronation catalyzed by palladium-phosphine complexes, the benchmark catalyst system for Suzuki-Miyaura cross-coupling, has been understudied compared to its base-catalyzed counterpart. We demonstrate, using automated high-throughput experimentation, comprehensive computational mechanistic analyses and kinetic modeling, that protodeboronation is accelerated by palladium(II) complexes bound to bulky phosphine ligands. While sterically hindered ligands are typically used to facilitate difficult cross-couplings, these ligands can instead paradoxically impede cross-coupling product formation, requiring careful and judicious consideration when choosing ligands for Suzuki-Miyaura cross-couplings.

Frame‐Shifted Synthesis of Oligoheterocycles as a Platform for Molecular Design

Chemists continue to develop strategies that leverage abundant feedstocks to accelerate the production of medicines, agrochemicals, and materials. Oligoheterocycles are a broad class of molecules that span these disciplines. Cross-coupling strategies are commonly used to assemble oligoheterocycles; however, these methods can be challenging and often rely on precious metal catalysts and building blocks whose production entails significant environmental and economic costs. We present a strategy for the iterative construction of oligoheterocycles that leverages sustainable building blocks designed around high-oxidation-state carbon. By utilizing feedstocks from the base of the chemical supply chain, we generate bifunctional building blocks that chemoselectively engage with a variety of partners through simple condensation manifolds. This approach has enabled the creation of novel scaffolds previously inaccessible by conventional means, exemplifying sustainable design strategies.

Grammar-driven SMILES standardization with TokenSMILES

TokenSMILES is a grammatical framework that redefines the syntactic rules of SMILES , representing molecules as structured sentences.

Methane photolysis to clean hydrogen and carbon nanotubes

Light-driven methane photolysis produces clean H 2 and MWCNTs from natural gas, enabling low-energy hydrogen fuel and advanced materials for Li-ion storage, with 70% less energy than conventional methods.

EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture

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

Stereochemistry-aware string-based molecular generation

Automating quantum computing laboratory experiments with an agent-based AI framework

Fully automated self-driving laboratories promise high-throughput, large-scale scientific discovery by reducing repetitive labor. However, they require deep integration of laboratory knowledge, which is often unstructured, multimodal, and hard to incorporate into current AI systems. This paper introduces the "k-agents" framework, designed to support experimentalists in organizing laboratory knowledge and automating experiments with agents. The framework uses large-language-model-based agents to encapsulate laboratory knowledge, including available operations and methods for analyzing results. To automate experiments, execution agents break multistep procedures into agent-based state machines, interact with other agents to execute steps, and analyze results. These results drive state transitions, enabling closed-loop feedback control. We demonstrate the system on a superconducting quantum processor, where agents autonomously planned and executed experiments for hours, successfully producing and characterizing entangled quantum states at human-level performance. Our knowledge-based agent system opens new possibilities for managing laboratory knowledge and accelerating scientific discovery.

Quantum Algorithm for Vibronic Dynamics: Case Study on Singlet Fission Solar Cell Design

Connecting the concepts of quantum state tomography and molecular representations for machine learning

A borindolizine platform for the design of fluorophores with tun-able emissions