Mahule Roy

Alexander Hackett, Srikanth Thudumu, Ginny Fisher et al.

Extreme low-data fine-grained classification is common in expert domains where labeling is expensive, yet practitioners still need principled guidance for selecting pretrained encoders. We study emerald inclusion grading with a custom dataset of labeled images across three classes and ask: under matched backbone capacity, how does pretraining objective affect downstream representation quality? We compare four frozen ViT-B/16 encoders trained with supervised classification, contrastive learning (SigLIP2), masked reconstruction (MAE), and self-distillation (DINOv3), and evaluate them with leave-one-out cross-validation using linear and nonlinear probes. To control statistical noise in the low-N regime, we use permutation testing (N=1000) on macro one-vs-rest AUC. Supervised and contrastive encoders provide the strongest linear separability (logistic AUC: 0.768 and 0.735; SVM AUC: 0.739 and 0.697), while MAE improves under nonlinear probes (XGBoost AUC: 0.713). We find that DINOv3 underperforms across probe families in this domain. These results support a practical recommendation for extreme low-data FGVC: prioritize margin-enforcing pretraining objectives when data scarcity restricts probing to linear decision rules, and consider reconstruction-style encoders when nonlinear classifiers are feasible given dataset constraints.

Aritra Roy, Kevin Shen, Andrew MacBride et al.

Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowledge. This paper analyzes a broad set of community-developed LLM applications in an effort to identify emerging patterns in how these systems can be used across the scientific research lifecycle. We organize the projects into two complementary categories: Knowledge Infrastructure, systems that structure, retrieve, synthesize, and validate scientific information; and Action Systems, systems that execute, coordinate, or automate scientific work across computational and experimental environments. The submissions reveal a shift from single-purpose LLM tools toward integrated, multi-agent workflows that combine retrieval, reasoning, tool use, and domain-specific validation. Prominent themes include retrieval-augmented generation as grounding infrastructure, persistent structured knowledge representations, multimodal and multilingual scientific inputs, and early progress toward laboratory-integrated closed-loop systems. Together, these results suggest that LLMs are evolving from general-purpose assistants into composable infrastructure for scientific reasoning and action. This work provides a community snapshot of that transition and a practical taxonomy for understanding emerging LLM-enabled workflows in materials science and chemistry.

Mahule Roy, Guillaume Lambard

Conventional generative models for materials discovery are predominantly trained and validated using data from Density Functional Theory (DFT) with approximate exchange-correlation functionals. This creates a fundamental bottleneck: these models inherit DFT's systematic failures for strongly correlated systems, leading to exploration biases and an inability to discover materials where DFT predictions are qualitatively incorrect. We introduce a quantum-aware generative AI framework that systematically addresses this limitation through tight integration of multi-fidelity learning and active validation. Our approach employs a diffusion-based generator conditioned on quantum-mechanical descriptors and a validator using an equivariant neural network potential trained on a hierarchical dataset spanning multiple levels of theory (PBE, SCAN, HSE06, CCSD(T)). Crucially, we implement a robust active learning loop that quantifies and targets the divergence between low- and high-fidelity predictions. We conduct comprehensive ablation studies to deconstruct the contribution of each component, perform detailed failure mode analysis, and benchmark our framework against state-of-the-art generative models (CDVAE, GNoME, DiffCSP) across several challenging material classes. Our results demonstrate significant practical gains: a 3-5x improvement in successfully identifying potentially stable candidates in high-divergence regions (e.g., correlated oxides) compared to DFT-only baselines, while maintaining computational feasibility. This work provides a rigorous, transparent framework for extending the effective search space of computational materials discovery beyond the limitations of single-fidelity models.