Kiran Vaddi

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

Structure elicitation from small angle scattering (i.e. SAXS, SANS) curves of large biomolecular assemblies is notoriously challenging. This is because the simulation of high-resolution features in the structure of large macromolecular assemblies, such as de-novo protein assemblies, is computationally demanding when it needs to cover a broad range of length scales. Conventional methods such as the numerical approximation to the Debye equation or the use of spherical harmonics do not scale well as the size of the assembly increases, which limits their application to small structures (e.g. individual proteins). This work explores the effectiveness of a Monte Carlo method to simulate and fit scattering curves for large biomolecular assemblies spanning over ranges covering atomic and molecular detail, as well as large scale (100’s of nm) features. Due to its excellent speed and scalability, it can be combined with a fitting algorithm to extract structural features from experimental small-angle scattering curves in biomolecular assemblies that are otherwise intractable for interpretation. This work first demonstrates the effectiveness of the tool using experimental small angle X-ray scattering (SAXS) data from tile-like proteins that assemble into 1D tube-like macromolecular structures. The diameter distribution of tubes is extracted from SAXS fits, and this quantitatively compared with distributions from electron microscopy. SAXS data is also obtained from 2D sheet-like protein assemblies and the proposed method is used to quantify structural features such as the separation distance between protein building blocks and the flexing of the sheet. An open-source implementation of the methodology is provided for use in a broad range of biological systems involving multi-scale scattering analysis.

Autonomous experimentation, or self-driving labs as they are popularly known, is a novel way of experimental planning and scheduling to systematically integrate automated material synthesis, characterization, and data analysis for accelerated materials design and discovery. This paper presents an autonomous experimentation workflow to conduct an iterative, on-demand synthesis, and structural characterization of colloidal gold nanoparticles to map composition information to nano-scale structure in a closed loop. We introduce a unified experimental planning framework based on differentiable models of the shape of a spectrum to solve the two categories of problems tackled using autonomous experimentation: a) quantitatively mapping compositional parameters to regions with particular behavior in structure or property i.e. phase mapping; b) inverse design of materials with a target structure or function i.e. material retrosynthesis. Using functional data analysis, we describe a data-driven computational model to map correlations between the compositions (i.e., synthesis or processing conditions) to the shape of a characterization curve (e.g.: UV-Vis spectroscopy). By integrating generative pre-training, active learning, and high-throughput experimentation, we train a surrogate model capable of predicting characterization curves across compositional spaces. The model-based approach to experimental planning provides convergence criteria for terminating iterative experimentation by measuring the model's test accuracy. We demonstrate the utility of trained differentiable models in generating phase maps of a seed-mediated growth of colloidal gold nanoparticles for extracting design rules, secondary interaction effects (between synthesis components and their impact on nanoparticle structure), and efficient navigation of different gold nanoparticle morphologies space. We then describe an application of the differentiable models to colloidal gold nanoparticle retrosynthesis using a gradient-based optimization to showcase the construction of a self-driving lab doubly capable of structure optimization and phase map generation.

The properties of conducting polymers are strongly influenced by structural changes induced by long-range order, which can be achieved using block copolymers that self-assemble into crystalline structures. These blends result in unique mesophases distinct from the pure components, with self-assembly behavior modulated by solution conditions and polymer architectures. High-throughput small angle x-ray scattering data of aqueous Pluronic P123 (PEO20- PPO70-PEO20) and conjugated polyelectrolyte poly[3-(potassium-4-butanoate) thiophene-2,5-diyl] (PPBT) blends at various concentrations and temperatures were automatically classified into phase maps by autophasemap, an unsupervised statistical analysis algorithm. The outlined phase boundaries revealed that adding PPBT to high P123 concentrations induced a transition from cubic-ordered spherical micelles to hexagonally packed cylindrical micelles. Shear alignment via rheological small angle neutron scattering of the blends produced monolithic oriented cubic and hexagonal crystal gels. These insights into the self-assembly of conductive polymer blends will aid in the design of soft materials with tunable structural and electronic properties.

We present an automated method to extract phase maps from experimental data that is of the functional form (e.g.: spectroscopy, scattering, diffraction) using the notion of shape distance between two curves represented as one dimensional functions.

We present an autonomous data-driven framework that iteratively explores the experimental design space of silver nanoparticle synthesis to obtain control over the formation of a desired morphology and size. The objective of the method is to autonomously identify design rules such as the effects of the design variables on the structure of the nanoparticle. The framework balances multimodal characterization methods (i.e. UV-Vis spectroscopy, SAXS, TEM), taking into account the cost of performing a measurement and the quality of information gained. By integrating with an AI agent, we identify important design variables in the synthesis of plate-like silver particles and outline how each variable affects plate thickness, radius, and polydispersity. Our findings are consistent with the literature, demonstrating that the automation framework could be further applied to new systems that have not been well characterized and understood. The framework is generalizable and allows tangible knowledge extraction from the high-throughput experimental runs while still taking inherent stochasticity into consideration.

Extracting a phase map that provides a hierarchical summary of high-throughput experiments is a long-standing bottleneck for the modern goal of achieving automation and acceleration in material discovery. A phase map that underpins the inherent properties of materials is typically denoted using a composition-structure map but can be extended to other relevant parameters such as synthesis. This paper describes a computational statistical tool to efficiently obtain a phase map from multi-scale experimental measurement profiles obtained from high-throughput measurements. We motivate the construction of a phase map as the problem of learning the underlying metric geometry defined by a set of templates in infinite-dimensional function spaces. We provide a statistical analysis tool to obtain a phase map as an asymptotic of the diffusion of resulting distance functions on the composition. Using examples from small-angle X-ray scattering experiments of polymer blend systems, we show that learned metric geometry can efficiently differentiate ordered phase regions with shifted, missing, and broad Bragg peaks along with features related to non-Bragg behavior of soft-matter systems. The metric geometry allows us to define a shape distance between scattering profiles invariant to phase-independent transformations thus valuable for obtaining a phase map. We also apply the methodology to benchmark experimental diffraction data to showcase potential utility and broad applicability.

We present a complete open-hardware and software Materials Acceleration Platform (MAP) for sonochemical synthesis of nanocrystals using a versatile tool-changing platform (Jubilee) configured for automated ultrasound application, a liquid-handling robot (Opentrons OT2) and a well-plate spectrometer. An automated high-throughput protocol was developed demonstrating the synthesis of CdSe nanocrystals using sonochemistry and different combinations of sample conditions, including precursor and ligand compositions and concentrations. Cavitation caused by ultrasound fields cause local and transient increases in temperature and pressure sufficient to drive the decomposition of organometalic precursors to drive the chemical reaction leading to nanocrystal formation. A total of 625 unique sample conditions were prepared and analyzed in triplicate with an individual sample volume of as little as 0.5 mL, which drastically reduced chemical waste and experimental times. The rapid onset of cavitation and quick dissipation of energy results in fast nucleation with little nanocrystal growth leading to the formation of small nanocrystals or magic-size clusters (MSCs) depending on composition. Using the effective mass approximation, the calculated QDs diameters obtained in all our experimental conditions ranged between 1.3 and 2.1 nm, which was also validated with small angle xray scattering (SAXS). Polydispersity, QD shape and optical properties largely varied depending on the concentration of ligands present in solution. Statistical analysis of the spectroscopic data corroborates the qualitative relationships observed from the optical characterization of the samples with the model-agnostic SHAP analysis. The complete workflow relies on relatively low-cost and open-source systems (<$15k for both robotic platforms). Automation and the reduced volumes also allows for cost-efficient experimentation, increasing the accessibility of this MAP. The high-throughput capabilities of the automated sonication platform, the extensible nature of the Jubilee system, and the modular nature of the protocol, make the workflow adaptable to a variety of future studies, including other nanocrystal design spaces, emulsification processes, and nanoparticle re-dispersion or exfoliation.

Cyclic Voltammetry (CV) is an electro-chemical characterization technique used in an initial material screening for desired properties and to extract information about electro-chemical reactions. In some applications, to extract kinetic information of the associated reactions (e.g., rate constants and turn over frequencies), CV curve should have a specific shape (for example an S-shape). However, often the characterization settings to obtain such curve are not known a priori. In this paper, an active search framework is defined to accelerate identification of characterization settings that enable knowledge extraction from CV experiments. Towards this goal, a representation of CV responses is used in combination with Bayesian Model Selection (BMS) method to efficiently label the response to be either S-shape or not S-shape. Using an active search with BMS oracle, we report a linear target identification in a six-dimensional search space (comprised of thermodynamic, mass transfer, and solution variables as dimensions). Our framework has the potential to be a powerful virtual screening technique for molecular catalysts, bi-functional fuel cell catalysts, and other energy conversion and storage systems.