arXiv subjects

Gustavo Malkomes

Publications and source records attributed to Gustavo Malkomes.

4 recordsLinked to original sources

arXiv2026

AI-guided high-throughput discovery of iridium- and ruthenium-free palladium-oxide catalysts for durable acidic oxygen evolution

Catalyzing acidic oxygen evolution at the proton-exchange-membrane water electrolysis (PEMWE) anode relies almost entirely on iridium or ruthenium, drawn from concentrated supply chains that constrain gigawatt-scale deployment. We report an artificial intelligence (AI)-guided, human-supervised closed-loop platform (>90% automation) integrating combinatorial sputter synthesis, high-throughput screening, machine-learning composition-property models, adaptive multi-objective optimization, and context-aware large-language-model reasoning, where lead catalysts advanced to long-term validation in 1 M H2SO4 at 10 mA cm-2. Navigating a combinatorial metal oxide space, the platform iteratively evaluated the activity-stability trade-off of 2,942 catalysts across 53 material systems and 26 elements, surfacing Ir- and Ru-free complex oxides such as InMnPdOx and NiTaPdOx that conventional design logic, and off-the-shelf language models, would not predict. In retrospective benchmarking, our sequential learning agent advanced the activity-stability frontier faster than fixed-policy Bayesian optimization or in-context language-model selection. During long-term testing, NiTaPdOx operated at lower overpotential than PdOx, but both eventually exceeded 0.5 V: PdOx at ~200 h and NiTaPdOx at ~470 h. InMnPdOx showed a similar overpotential improvement in addition to a dramatic increase in operational stability, retaining overpotential below 0.5 V over 1,000 h of operation. The additive elements promote the formation of a nanostructure that is associated with catalytic activity while stabilizing Pd against corrosion. The results highlight the power of AI-driven science in addressing long-standing challenges in materials chemistry, and the greater availability of Pd relative to incumbent Ir and Ru offers a near-term option to ease supply constraints on scaled electrochemical H2 generation.

cond-mat.mtrl-sci↗

arXiv2022

Group SELFIES: A Robust Fragment-Based Molecular String Representation

We introduce Group SELFIES, a molecular string representation that leverages group tokens to represent functional groups or entire substructures while maintaining chemical robustness guarantees. Molecular string representations, such as SMILES and SELFIES, serve as the basis for molecular generation and optimization in chemical language models, deep generative models, and evolutionary methods. While SMILES and SELFIES leverage atomic representations, Group SELFIES builds on top of the chemical robustness guarantees of SELFIES by enabling group tokens, thereby creating additional flexibility to the representation. Moreover, the group tokens in Group SELFIES can take advantage of inductive biases of molecular fragments that capture meaningful chemical motifs. The advantages of capturing chemical motifs and flexibility are demonstrated in our experiments, which show that Group SELFIES improves distribution learning of common molecular datasets. Further experiments also show that random sampling of Group SELFIES strings improves the quality of generated molecules compared to regular SELFIES strings. Our open-source implementation of Group SELFIES is available online, which we hope will aid future research in molecular generation and optimization.

cs.LG↗

arXiv2020

Accelerating Psychometric Screening Tests With Bayesian Active Differential Selection

Classical methods for psychometric function estimation either require excessive measurements or produce only a low-resolution approximation of the target psychometric function. In this paper, we propose a novel solution for rapid screening for a change in the psychometric function estimation of a given patient. We use Bayesian active model selection to perform an automated pure-tone audiogram test with the goal of quickly finding if the current audiogram will be different from a previous audiogram. We validate our approach using audiometric data from the National Institute for Occupational Safety and Health NIOSH. Initial results show that with a few tones we can detect if the patient's audiometric function has changed between the two test sessions with high confidence.

cs.LG↗

arXiv2018

Efficient nonmyopic active search with applications in drug and materials discovery

Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In this paper, we approach this problem in Bayesian decision framework. We first derive the Bayesian optimal policy under a natural utility, and establish a theoretical hardness of active search, proving that the optimal policy can not be approximated for any constant ratio. We also study the batch setting for the first time, where a batch of $b>1$ points can be queried at each iteration. We give an asymptotic lower bound, linear in batch size, on the adaptivity gap: how much we could lose if we query $b$ points at a time for $t$ iterations, instead of one point at a time for $bt$ iterations. We then introduce a novel approach to nonmyopic approximations of the optimal policy that admits efficient computation. Our proposed policy can automatically trade off exploration and exploitation, without relying on any tuning parameters. We also generalize our policy to batch setting, and propose two approaches to tackle the combinatorial search challenge. We evaluate our proposed policies on a large database of drug discovery and materials science. Results demonstrate the superior performance of our proposed policy in both sequential and batch setting; the nonmyopic behavior is also illustrated in various aspects.

cs.LG↗