arXiv subjects

Gregory Bassen

Publications and source records attributed to Gregory Bassen.

6 recordsLinked to original sources

arXiv2026

Human and LLM Collaboration for Accelerated Materials Synthesis and Discovery

Although Large Language Models (LLM) and Artificial Intelligence (AI) tools have enabled a rapid increase in the generation rate of predicted materials, the rate of new materials discovery has lagged behind. This is due to the challenges associated with designing a sequence of chemical reactions to predictably produce new materials, especially in new structure types. Here, we report a study of human and LLM generated recipes for the synthesis of known and new materials. The success of the recipes is determined through in-lab experimentation, and the results are passed back to the humans and LLMs in a closed-loop process to study the effects of their collaboration. The Ruddlesden-Popper homologous series was selected for all material candidates to provide a materials phase space that is simultaneously well studied and likely to host undiscovered materials. We find that humans (H) and LLM (L) have similar success rates: 83(8)% (H) and 75(9)% (L) [known materials, round one], 17(9)% (H) and 22(10)% (L) [unknown materials, round one], 79(8)% (H) and 71(9)% (L) [known materials, round two], and 22(7)% (H) and 14(6)% (L) [unknown materials, round two]. Through this collaborative human-LLM effort, we discovered Ba3PtO5, a material with a new structural prototype that constitutes the missing 1D member of the herein reported dimensionally tunable Rock-Salt Perovskite (RSP) homologous series of the form (AX)m(ABX3)p, of which the Ruddlesden-Popper series is a subset.

cond-mat.mtrl-sci↗

arXiv2026

Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials

Modern generative machine learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning of these materials remains difficult due to the complexity of the associated physical processes and limited availability of computational tools. We introduce a novel hybrid framework to evaluate Large Language Models (LLMs) in inorganic synthesis planning by combining thermodynamic databases with simplified kinetics models to approximate realistic synthesis conditions. As a case study, we focus on the niobium-oxygen system, which features multiple industrially relevant oxide phases with well-characterized data. In computational simulations, we compare LLM-generated synthesis routes with classical path-planning algorithms, showing that the implicit priors in LLMs can yield more viable strategies. In our evaluation setting, classical search methods serve primarily as a foil rather than a direct competitor. This illustrates the relative complexity of the problem and highlights where the LLM's implicit priors add value.

cs.AI↗

arXiv2025

The superconducting diode effect in Josephson junctions fabricated from structurally chiral Mo$_3$Al$_2$C

The superconducting diode effect occurs in superconducting materials in which both spin and inversion symmetry are broken. The recently observed chirality-induced spin selectivity effect demonstrates that chiral materials break both symmetries. Thus a Josephson junction interface with the left-handed structure on one side of the junction and the right-handed structure on the other should exhibit a diode effect. Here, we report the electrical transport properties of right-handed/left-handed and right-handed/right-handed devices fabricated from single crystals of the structurally chiral superconductor Mo$_3$Al$_2$C. Fraunhofer-like magnetic diffraction patterns confirm the presence of Josephson effect in all but one of our devices. A magnetic field-induced superconducting diode effect is demonstrated in the right-handed/left-handed devices by a statistically significant difference in $I_{c+}$ and $I_{c-}$, with a maximum asymmetry of 5\%. The intrinsic superconducting diode effect is not observed in the right-handed/right-handed devices. We provide an explanation for the presence of the superconducting diode effect in the right-handed/left-handed devices.

cond-mat.supr-con↗

arXiv2025

A Crystallographic Metric for Continuous Quantification of Unit Cell Deformation

Describing the deviation of a real structure from a hypothetical higher-symmetry ideal can be a powerful tool to understand and interpret phase transitions. Here we introduce a simple yet effective metric that quantifies the degree of unit cell distortion relative to a cube, called the cubic deviation metric. This enables continuous comparisons between unit cells of different geometries. We demonstrate the potential of this tool with four separate case study applications to real material systems: 1) discontinuous structural phase transitions in pseudobrookites; 2) homological structure classification; 3) structure-correlated piezoelectricity in hexagonal materials; and 4) superconducting materials design in the cuprate family. Although this metric does not replace detailed structural or group theory analysis, it enables comparison across different compositional and structural compound variants, even in the presence of disorder or absence of group-subgroup correlation.

cond-mat.mtrl-sci↗

arXiv2025

Real-space orbital tiling approach for the design of novel superconductors

Despite substantial advances in the field, we still lack a predictive framework capable of guiding the discovery of new families of superconductors. While momentum-space approaches have advanced the microscopic understanding of superconductivity, they offer limited guidance for materials design based on atomic building blocks. Here, we propose a real-space framework which conceptualizes Cooper pairs as confined standing waves resulting from coherent tilings of atomic orbitals. We call this model the Real-space Orbital Superconducting Pathway (ROSP). Using a tight-binding toy model, we show that the energetics of electron pairing depend on the configuration and overlap of real-space orbitals, which motivates \textit{a priori} design of superconducting families from orbital tiling. We connect the ROSP model to Roald Hoffmann's isolobal analogy to classify families of superconductors based on shared orbital tilings, rather than structure or electron count. As an example, we suggest that superconductivity in La$_{3}$Ni$_{2}$O$_{7}$ and LaNiO$_{2}$, despite differing structures and electron counts, may arise from a common ROSP. We introduce a new notation to classify two-dimensional square-net ROSPs and further propose several new families of superconductors on the anti-cuprate lattice. This framework provides a new model for predicting and designing families of high-T$_c$ superconductors from real-space orbital architecture, even without microscopic knowledge of the attractive pairing interaction.

cond-mat.supr-con↗

arXiv2022

Closed-loop machine learning for discovery of novel superconductors

The discovery of novel materials drives industrial innovation, although the pace of discovery tends to be slow due to the infrequency of "Eureka!" moments. These moments are typically tangential to the original target of the experimental work: "accidental discoveries". Here we demonstrate the acceleration of intentional materials discovery - targeting material properties of interest while generalizing the search to a large materials space with machine learning (ML) methods. We demonstrate a closed-loop ML discovery process targeting novel superconducting materials, which have industrial applications ranging from quantum computing to sensors to power delivery. By closing the loop, i.e. by experimentally testing the results of the ML-generated superconductivity predictions and feeding data back into the ML model to refine, we demonstrate that success rates for superconductor discovery can be more than doubled. In four closed-loop cycles, we discovered a new superconductor in the Zr-In-Ni system, re-discovered five superconductors unknown in the training datasets, and identified two additional phase diagrams of interest for new superconducting materials. Our work demonstrates the critical role experimental feedback provides in ML-driven discovery, and provides definite evidence that such technologies can accelerate discovery even in the absence of knowledge of the underlying physics.

cond-mat.supr-con↗