Nanoporous materials (NPMs) are suitable for solving some of the society's biggest challenges including carbon capture and conversion, storing hydrogen and methane, and sensing gases. The key challenge in discovering high-performing NPMs for a target application is that making and evaluating candidate NPMs requires performing resource-expensive wet-lab experiments. We consider the problem of discovering NPMs using existing experimental data of NPM evaluations. The overall goal is to find better NPMs than the best NPMs from the past experimental data. A simple approach is to create a surrogate model to match the objective values on the given dataset and employ it to score candidate NPMs to discover optimized NPMs. However, this surrogate model will fail because it does not have the appropriate search bias for the goal of optimization. To address this challenge, we propose a novel surrogate modeling approach that combines value matching loss with an optimization bias as regularizer. The key idea is to algorithmically realize search bias is to mimic the search behavior of monotonically increasing sequences of NPMs from the given dataset. Experiments on multiple real-world NPM discovery tasks demonstrate that our proposed surrogate model discovers significantly better NPMs than baselines including value matching surrogate model and one-step Bayesian optimization.

Configuring the parameters of additive manufacturing processes for metal alloys is a challenging problem due to complex relationships between input parameters (e.g., laser power, scan speed) and quality of printed outputs. The standard trial-and-error approach to find feasible parameter configurations is highly inefficient because validating each configuration is expensive in terms of resources (physical and human labor) and the configuration space is very large. This paper combines the general principles of AI-driven adaptive experimental design with domain knowledge to address the challenging problem of discovering feasible configurations. The key idea is to build a surrogate model from past experiments to intelligently select a small batch of input configurations for validation in each iteration. To demonstrate the effectiveness of this methodology, we deploy it for Directed Energy Deposition process to print GRCop-42, a high-performance copper–chromium–niobium alloy developed by NASA for aerospace applications. Within three months, our approach yielded multiple defect-free outputs across a range of laser powers—dramatically reducing time-to-result and resource expenditure compared to several months of manual experimentation by domain scientists with no success. By enabling high-quality GRCop-42 fabrication on readily available infrared laser platforms for the first time, we democratize access to this critical alloy, paving the way for cost-effective, decentralized production for aerospace applications.

Offline design optimization problem arises in numerous science and engineering applications including material and chemical design, where expensive online experimentation necessitates the use of *in silico* surrogate functions to predict and maximize the target objective over candidate designs. Although these surrogates can be learned from offline data, their predictions are often inaccurate outside the offline data regime. This challenge raises a fundamental question about the impact of imperfect surrogate model on the performance gap between its optima and the true optima, and to what extent the performance loss can be mitigated. Although prior work developed methods to improve the robustness of surrogate models and their associated optimization processes, a provably quantifiable relationship between an imperfect surrogate and the corresponding performance gap, as well as whether prior methods directly address it, remain elusive. To shed light on this important question, we present a theoretical framework to understand offline black-box optimization, by explicitly bounding the optimization quality based on how well the surrogate matches the latent gradient field that underlines the offline data. Inspired by our theoretical analysis, we propose a principled black-box gradient matching algorithm to create effective surrogate models for offline optimization, improving over prior approaches on various real-world benchmarks.