Generative structure search for efficient and diverse discovery of molecular and crystal structures
Yifang Qin, Yu Shi, Junfu Tan, Chang Liu, Ming Zhang, Ziheng Lu
Abstract
Predicting stable and metastable structures is central to molecular and materials discovery, but remains limited by the cost of searching high-dimensional energy landscapes. Deep generative models offer efficient structure sampling, yet their outputs remain shaped by training data and can underexplore minima that are rare but physically relevant. We introduce generative structure search (GSS), a unified framework that formulates diffusion-based generation and random structure search (RSS) as limiting regimes of a common sampling process driven by learned score fields and physical forces. Coupling these drivers lets GSS use data priors to accelerate sampling while retaining energy-guided exploration of local minima. Across molecular and crystalline systems, GSS recovers diverse metastable structures with more than tenfold lower sampling cost than RSS for broad coverage and remains effective for compositions outside the training distribution. The results establish a physically grounded generative search strategy for discovering structures beyond the reach of data-driven sampling alone.
AI Impact Assessments
(1 models)Scientific Impact Assessment: Generative Structure Search (GSS)
1. Core Contribution
The paper introduces Generative Structure Search (GSS), a framework that unifies diffusion-based generative models and random structure search (RSS) into a single sampling process. The key insight is that both RSS and diffusion generation can be expressed as iterative structure updates (Eq. 1) that differ only in their driving forces—energy gradients for RSS and learned score functions for diffusion. GSS interpolates between these two regimes via a sigmoid schedule that transitions from diffusion-dominated early steps (providing global structural priors) to energy-dominated late steps (ensuring convergence to physically valid local minima). This addresses a genuine gap: diffusion models concentrate on training-data modes (often just the global minimum), while RSS explores broadly but inefficiently.
2. Methodological Rigor
Strengths in formulation: The mathematical unification of RSS and diffusion sampling is clean and well-motivated. The classifier-guidance-style combination (Eq. 3) with complementary weights (β_i = 1 - α_i) is a principled choice, and the authors correctly note that the sigmoid schedule ending in the energy-dominated regime guarantees convergence to PES local minima.
Concerns about rigor:
Missing baselines: The paper compares only against vanilla diffusion and RSS. No comparisons are made against other structure search methods (basin hopping, evolutionary algorithms, metadynamics) or against other guided diffusion strategies (e.g., the M_N-guided variant is relegated to supplementary). Comparisons with recent ML-accelerated structure search methods would significantly strengthen the claims.
3. Potential Impact
The practical utility is significant. Structure search is a genuine bottleneck in materials and molecular discovery, and the reported >10× reduction in sampling cost compared to RSS for full coverage is substantial if it generalizes. Key impact vectors include:
However, impact is limited by several factors: (1) the method requires both a pretrained diffusion model and an MLFF, creating dependencies on these components' quality; (2) the evaluation systems, while spanning elemental to quaternary compositions, are relatively small unit cells; (3) scalability to larger, more complex systems (proteins, amorphous materials) remains speculative.
4. Timeliness & Relevance
The paper is highly timely. The convergence of universal MLFFs (MatterSim, MACE, UMA) and crystal structure prediction diffusion models (MatterGen, DiffCSP) creates a natural opportunity for their combination. The structure search problem remains a critical bottleneck, and the community is actively seeking ways to leverage generative models for discovery beyond training distributions. The framework is also timely given the growing recognition that diffusion models alone are insufficient for exhaustive polymorph discovery.
5. Strengths & Limitations
Key Strengths:
Notable Limitations:
6. Additional Observations
The paper would benefit from: (1) ablation studies on the schedule parameters and the relative contribution of each component; (2) analysis of failure modes—when does GSS miss structures that RSS finds?; (3) computational cost breakdowns including MLFF inference time; (4) larger molecular test cases beyond aspirin. The framing as a "unified framework" is somewhat overclaimed—the combination is straightforward classifier guidance applied to structure search, though the physical interpretation and practical implementation are valuable.
Generated May 5, 2026
Comparison History (90)
Paper 2 is more novel methodologically: it unifies diffusion generation and random structure search into a single physically grounded sampling framework, addressing a core bottleneck (energy-landscape exploration) with clear, generalizable gains (order-of-magnitude cost reduction, OOD compositions). Its rigor is supported by cross-domain evaluation (molecules and crystals) and explicit coupling to physical forces, which strengthens reliability and adoption. The potential impact spans computational chemistry, materials science, and generative modeling, enabling faster discovery pipelines. Paper 1 is large-scale and highly applicable but is more a scaling/deployment integration of established paradigms.
Paper 1 presents a highly actionable, novel methodology (GSS) that directly accelerates materials and molecular discovery by tenfold. This has profound real-world applications in drug discovery, materials science, and clean energy. In contrast, Paper 2 is a benchmarking study reporting primarily negative results about AI's current inability to forecast scientific progress. While Paper 2 is relevant to meta-science and AI evaluation, Paper 1 offers a concrete, computationally rigorous tool that immediately solves a major bottleneck in high-dimensional energy landscape exploration, providing more immediate and tangible scientific impact.
Paper 1 presents a concrete, technically novel framework unifying diffusion-based generative modeling with physics-driven random structure search, demonstrating >10× sampling efficiency gains and out-of-distribution effectiveness for molecular/crystal structure discovery. This has immediate real-world applicability in materials and drug discovery and broad downstream impact across chemistry, physics, and ML, with clear methodological grounding and measurable benchmarks. Paper 2 introduces a valuable benchmark for forecasting scientific progress and highlights limitations of current models, but it is primarily evaluative/meta-scientific with less direct translational payoff and narrower near-term practical impact.
While Paper 1 offers a strong advance in materials discovery, Paper 2 has a higher potential for immediate, widespread scientific impact. DPO is currently the dominant methodology for aligning Large Language Models. By mathematically proving the failure modes of DPO and introducing a state-of-the-art solution (CPO), Paper 2 addresses a critical bottleneck in AI alignment. Its fundamental theoretical contributions combined with practical improvements ensure broad adoption and high citation rates across the rapidly expanding and highly resourced field of artificial intelligence.
Paper 2 addresses a critical theoretical flaw in DPO, a dominant algorithm for LLM alignment. Given the ubiquitous use of LLMs, providing provable alignment guarantees and fixing fundamental failure modes offers immense and immediate impact across the AI community. While Paper 1 is highly significant for materials science, Paper 2's foundational relevance to core AI models gives it a wider and more immediate scientific footprint.
Paper 1 likely has higher scientific impact: it introduces a novel, physically grounded framework unifying diffusion generation and random structure search, directly addressing a core bottleneck in materials/molecular discovery (efficient exploration of high-dimensional energy landscapes). The claimed >10× sampling efficiency and out-of-distribution effectiveness suggest strong real-world applicability to drug/materials design and broad relevance across chemistry, physics, and materials science. Paper 2 is timely and useful for LLM evaluation, but benchmarks tend to have narrower downstream scientific impact than methods enabling new molecular/crystal discoveries, and can be superseded quickly.
Paper 2 introduces a fundamentally novel framework (GSS) that unifies two paradigms—generative diffusion models and random structure search—into a principled sampling process. This has broad, lasting impact across materials science, chemistry, and drug discovery by enabling efficient exploration of energy landscapes with >10x cost reduction. It addresses a core bottleneck in molecular/materials discovery with strong methodological innovation. Paper 1, while useful as a benchmark for AI evaluation, is more incremental—benchmarks have shorter lifespans and narrower impact, primarily within the AI/NLP community.
Paper 1 offers a novel, physically grounded unification of diffusion generative modeling and random structure search, delivering large efficiency gains and improved coverage of metastable minima, including out-of-distribution compositions. Its applications to molecular/crystal structure discovery are immediate and broadly valuable across chemistry, materials science, and computational physics, with clear methodological substance and measurable performance improvements. Paper 2 is timely and important for AI evaluation, but is primarily a benchmarking/diagnostic study with narrower direct scientific utility and less enduring cross-domain impact than a new structure-search paradigm for real-world materials discovery.
Paper 1 significantly accelerates molecular and materials discovery by elegantly bridging deep generative models with physical search methods. Its >10x efficiency gain and out-of-distribution capabilities have profound, tangible implications for discovering new drugs, catalysts, and materials. This directly advances foundational physical sciences, offering broader and more enduring scientific impact compared to the software-engineering and AI-safety focus of Paper 2.
Paper 1 likely has higher long-term scientific impact due to its methodological innovation bridging diffusion generative models with physics-based random structure search into a unified, physically grounded sampling framework. It targets a central bottleneck in materials/molecular discovery—exploration of high-dimensional energy landscapes—with clear real-world applications (drug/materials design) and cross-domain relevance across chemistry, physics, and materials science. The claimed out-of-distribution effectiveness and order-of-magnitude efficiency gains suggest strong practical value. Paper 2 is timely and broadly useful for LLM reliability, but inference-time heuristics may be more incremental and field-specific than a new paradigm for structure discovery.
While Paper 1 offers a strong theoretical framework for LLM-based discovery, Paper 2 addresses a fundamental bottleneck in materials science and chemistry. By combining generative models with physical forces to achieve a tenfold reduction in sampling costs for molecular and crystal structures, Paper 2 promises immediate, profound real-world applications in discovering novel materials and drugs, extending robustly beyond its training distribution.
Paper 2 has higher likely scientific impact: it introduces a unified, physically grounded framework (GSS) that bridges diffusion generation and random structure search, yielding large practical gains (10× lower sampling cost) and demonstrated out-of-distribution effectiveness across molecules and crystals. This targets a core bottleneck in materials/pharma discovery with clear downstream applications and broad relevance to ML for science, computational chemistry, and materials engineering. Paper 1 is timely and valuable infrastructure for formal math/AI evaluation, but its impact is more domain-specific and indirect compared to a method that can immediately accelerate real-world discovery pipelines.
Paper 1 tackles a fundamental challenge in the physical sciences by bridging generative AI and physics-based search. Its potential to significantly accelerate the discovery of new materials and pharmaceuticals gives it a broader and more profound scientific impact across multiple disciplines (chemistry, physics, materials science) compared to Paper 2, which primarily focuses on optimizing software engineering and systems infrastructure.
Paper 1 introduces a unified framework (GSS) bridging generative models and random structure search for molecular/crystal structure discovery, addressing a fundamental challenge in materials science with demonstrated >10x efficiency gains and out-of-distribution generalization. This has broad real-world impact in drug discovery and materials design. Paper 2 provides interesting analysis of reasoning trace redundancy in LLMs, but is more diagnostic/analytical in nature with narrower immediate applications. Paper 1's methodological innovation combining physics-based and data-driven approaches represents a more transformative contribution to a high-impact domain.
Paper 2 addresses a fundamental bottleneck in materials science and chemistry, offering a 10x efficiency improvement in discovering new molecular and crystal structures. While Paper 1 provides a valuable computational optimization for LLMs, Paper 2's potential to accelerate the discovery of novel materials and drugs presents a significantly broader and more profound real-world scientific impact across multiple physical science domains.
While Paper 1 provides valuable insights into LLM reasoning limitations, Paper 2 offers a broader and more enduring scientific impact. By unifying diffusion models and physical forces for molecular and crystal structure search, it significantly accelerates materials discovery and drug design. Its tenfold efficiency improvement and out-of-distribution generalization directly translate to real-world applications in chemistry, physics, and medicine, offering tangible advancements beyond the rapidly shifting landscape of LLM diagnostics.
Paper 1 introduces a fundamental methodological advance bridging machine learning and physical sciences, offering a tenfold acceleration in materials and molecular discovery. Its potential to accelerate discoveries across chemistry, physics, and drug development gives it profound scientific impact. Paper 2, while highly timely and practically important for AI safety, is primarily a software engineering and security framework rather than a fundamental scientific breakthrough.
Paper 2 addresses a fundamental challenge in materials science and chemistry—efficient structure prediction across high-dimensional energy landscapes—with broad applicability to molecular and materials discovery. Its unified framework bridging generative models and random structure search offers >10x efficiency gains and works beyond training distributions, making it highly impactful for computational chemistry, drug discovery, and materials design. While Paper 1 introduces a novel and timely auditing framework for AI-mediated democracy with strong methodological rigor, its impact is more domain-specific (AI governance/policy). Paper 2's potential to accelerate discovery across multiple scientific fields gives it broader and deeper impact.
Paper 1 (GSS) addresses a fundamental challenge in materials and molecular discovery by unifying generative models with physics-based structure search, demonstrating >10x efficiency gains and out-of-distribution generalization. This has broad impact across chemistry, materials science, and drug discovery. Paper 2 (MoE-FM/YAN) offers impressive speedups for non-autoregressive language modeling, but NAR language models have historically struggled to gain adoption over AR models. GSS's novel physics-ML integration and its applicability to high-impact scientific discovery problems give it greater potential for lasting scientific impact.
Paper 2 introduces a unified framework (GSS) bridging generative models and random structure search for molecular/materials discovery—a fundamental problem in chemistry and materials science. Its >10x efficiency gain, ability to discover metastable structures, and generalization beyond training data address critical bottlenecks in computational materials science with broad real-world applications (drug design, materials engineering). While Paper 1 addresses an important and timely question about LLM independence with solid methodology, its impact is more niche—focused on LLM evaluation/ensemble practices—and yields relatively modest improvements (4.5% accuracy gain). Paper 2's cross-disciplinary impact and practical utility are greater.