jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chenru Duan | Aditya Nandy | Subodh Chandra Pal | Xin Yang | Wenhao Gao | Yuanqi Du | Hendrik Kraß | Yeonghun Kang | Varinia Bernales | Zongyu Ye | Tristan Pyle | Ray Yeng Yang | Zeqi Gu | Philippe Schwaller | Shengqian Ma | Shijing Sun | Alán Aspuru‐Guzik | Seyed Mohamad Moosavi | Robert B. Wexler | Zhiling Zheng
Source: Matter (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

The rise of generative AI for metal-organic framework design and synthesis Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This Perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches that can autonomously propose and synthesize in the laboratory new porous reticular structures on demand. We outline the progress of employing deep learning models, such as variational autoencoders, diffusion models, and large language model-based agents, that are fueled by the growing amount of available data from the MOF community and suggest novel crystalline materials designs. These generative tools can be combined with high-throughput computational screening and even automated experiments to form accelerated, closed-loop discovery pipelines. The result is a new paradigm for reticular chemistry in which AI algorithms more efficiently direct the search for high-performance MOF materials for clean air and energy applications. Finally, we highlight remaining challenges such as synthetic feasibility, dataset diversity, and the need for further integration of domain knowledge. Generative grammar ; Domain (mathematical analysis) ; Perspective (graphical) ; Generative model ; Generative Design Machine Learning in Materials Science
Authors: Chuwen Zhang | Lixiang Yang | Yanjia Qin | Danjing Li | Shimao Dong | Meng Yang
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

An AI-Native Biofoundry for Autonomous Enzyme Engineering: Integrating Active Learning with Automated Experimentation Abstract The engineering of enzymes with novel functions is a cornerstone of synthetic biology but remains bottlenecked by the fragmentation between computational design and physical execution. While “self-driving” laboratories promise to resolve this, existing systems often rely on rigid, device-specific scripts that lack the flexibility to handle complex, evolving scientific tasks. Here, we report an AI-native autonomous biofoundry that fundamentally redefines laboratory automation through a “cloud-edge synergistic” architecture. The platform features an Agent-Native control system powered by Large Language Models (LLMs) and the Model Context Protocol (MCP), which bridges the semantic gap between abstract scientific intent and heterogeneous hardware execution. This architecture enables non-experts to orchestrate the entire Design-Build-Test-Learn (DBTL) cycle via natural language. By integrating deep phylogenetic mining, zero-shot protein language models (ESM-2), and supervised active learning, our system efficiently navigates rugged fitness landscapes. As a rigorous proof of concept, we applied this platform to evolve a Family B DNA polymerase for CoolMPS sequencing, a task requiring the incorporation of non-natural 3’-blocked nucleotides. In just three autonomous rounds, the platform achieved a hit rate of &gt;66% and identified variants with a 37% reduction in sequencing error rate compared to a commercial reference. This work demonstrates that AI-native infrastructures can not only accelerate trait evolution by orders of magnitude but also provide a scalable, brand-agnostic paradigm for the future of automated scientific discovery. Synthetic biology ; Scripting language ; Context (archaeology) ; Flexibility (engineering) ; Automation ; Active learning (machine learning) ; Genetic programming ; Workflow Machine Learning in Materials Science ; Genomics and Phylogenetic Studies ; Genomics and Rare Diseases
Authors: Ankit Roy | Ram Devanathan | Sarah I. Allec | Giridhar Nandipati | Andrew M. Casella | David J. Senor | D. Gale Johnson | Ganesh Balasubramanian | Ayoub Soulami
Source: Advanced Intelligent Discovery (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective Accurate and efficient interatomic potentials are essential for molecular dynamics (MD) simulations of radiation damage, gas diffusion, and phase stability in complex ceramics such as LiAlO 2 , especially under extreme conditions relevant to tritium production. Here, we evaluate the performance of six machine‐learned interatomic potentials (MLIPs), moment tensor potential (MTP), Gaussian approximation potential, deep potential (DeePMD), atomic cluster expansion (ACE), message‐passing ACE (multilayer atomic cluster expansion (MACE) pretrained) and MACE (trained from‐scratch), all trained on the same density functional theory dataset with inclusion of tritium. The MLIPs are benchmarked against traditional Buckingham and ReaxFF potentials in terms of energy accuracy, density predictions, thermal equilibration behavior, threshold displacement energy ( E d ), tritium diffusivity, and computational cost. Among the models, MTP shows the best overall balance between efficiency and accuracy, with low force and energy errors and realistic E d values for Li and Al. The ACE and MACE (pretrained and trained from scratch) models exhibit high E d (&gt;200 eV) and unphysical pair interactions. DeePMD underestimates E d due to overly repulsive behavior even at equilibrium distances. All models over‐estimate tritium diffusion but the pretrained MACE model behaves well during tritium‐diffusion simulations up to 500 K, maintaining diffusivities in the physically consistent 10 −11 m 2 /s range. Finally, we quantify the computational cost of each potential in large‐scale atomic/molecular massively parallel simulator, finding that only MTP is more efficient than traditional empirical potentials, while others are significantly more expensive. These findings explain the trade‐offs between accuracy and computational cost in MLIP development and provide essential guidance for use in high‐throughput radiation damage and gas diffusion simulations in nuclear ceramics. Molecular dynamics ; Massively parallel ; Potential energy ; Density functional theory ; Computational model ; Mace Fusion materials and technologies ; Nuclear Materials and Properties ; Machine Learning in Materials Science
Authors: Abdullah Bin Faheem | Zengyu Han | Dongshuang Wu | Haobo Li
Source: Advanced Materials (article, 2026-01-10)
Field: Materials Science — Materials Chemistry

AI‐Driven Big Data Frameworks for Electrode–Electrolyte Interphases in Batteries This review presents a comprehensive perspective on how AI and big data strategies can transform the understanding and design of the electrode-electrolyte interphases (EEI) in rechargeable batteries, highlighting their pivotal role in battery performance and longevity. Through uniting high-throughput experimentation and high-throughput computation (HTC), which includes automated cell fabrication, advanced characterization, large-scale HTC screening, and reaction network modeling, diverse datasets can be generated to reveal the mechanistic foundations of interfacial processes. The integration of these datasets with artificial intelligence-orchestrated workflows and machine learning models, such as closed-loop optimization and large language model-assisted hypothesis generation, enables the prediction of interphase behavior, linking molecular-level EEI understanding and macroscale device performance, and data-driven discovery of optimal material combinations. Critically, the review identifies persistent challenges, including limited data standardization, a shortage of high-quality interoperable datasets, the gap between optimization and generalizable understanding, the limits of currently available self-driving labs, and outlines mitigation strategies for building intelligent, data-centric frameworks for rational engineering of next-generation battery systems. Workflow ; Big data ; Interoperability ; Battery (electricity) ; Economic shortage ; Perspective (graphical) ; Data integration ; SPARK (programming language) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced battery technologies research
Authors: Turab Lookman | YuJie Liu | Zhibin Gao
Source: Advanced Materials (article, 2026-01-08)
Field: Materials Science — Materials Chemistry

Materials Informatics: Emergence to Autonomous Discovery in the Age of AI We provide a perspective on the evolution of materials informatics, tracing its conceptual roots to foundational ideas in physics and information theory and its maturation through the integration of machine learning and artificial intelligence (AI). Early contributions from Chelikowsky, Phillips, and Bhadeshia laid the groundwork for what has become a transformative approach to materials discovery. The U.S. Materials Genome Initiative catalyzed a surge in activity, and the period from 2014-2016 marked the first impactful applications of machine learning to materials problems. Since then, the field has seen rapid advances, particularly with the advent of deep learning and transformer-based large language models (LLMs), which now underpin tools for property prediction, synthesis planning, and inverse design. We present the subject not as a collection of tools, but as an evolving research ecosystem - tracing the historical context, current situation, and future development direction of the field. We review key methodologies-including approaches for sequential design, such as Bayesian Optimization and Reinforcement Learning, and transformers -highlighting their role in accelerating discovery and automating experimentation with growing efforts in building autonomous self-driving laboratories. We discuss common pitfalls, issues, and solutions associated with LLMs and Bayesian Optimization as applied to the study of specific materials systems. Given the costs of pre-trained AI models for specific materials, we consider the merits of specialist LLMs versus today's state-of-the-art generalists. Finally, we assess emerging challenges and the potential for AI to evolve from a predictive tool into a collaborative partner in research. We argue that with advances in active learning, uncertainty quantification, and retrieval-augmented generation, a new era of autonomous materials science is within reach-one in which the human is increasingly taken out of the loop. Transformative learning ; Tracing ; Reinforcement learning ; Bayesian optimization ; Big data ; Field (mathematics) ; Bayesian probability Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: Jiaxin Chen | Tianjiao Wan | Hui Geng | Liang Xiong | Guohong Wang | Yihan Zhao | Linfeng Deng | Gao, Zijian | Fang, Susu | Luo, Zheng | Shanshan Wang | Wang, Shanshan | Kele Xu
Source: arXiv (Cornell University) (preprint, 2026-01-11)
Field: Materials Science — Materials Chemistry

Data-driven active learning approaches for accelerating materials discovery Materials discovery is a cornerstone of modern technological advancement, yet it remains constrained by traditional trial-and-error paradigms and the inherent bias of human intuition. Artificial intelligence (AI) has emerged as a transformative tool in materials science by effectively modeling structure-property relationships. Despite substantial efforts to enhance model expressiveness, data efficiency remains an equally critical challenge, given the limited availability of experimental and computational resources. Active learning (AL), as a data-driven machine learning paradigm, has shown great promise for discovering novel materials and enabling the efficient navigation of vast materials spaces. In this review, we follow the evolution of sampling strategy design techniques in AL, from Bayesian optimization to advanced deep learning-based strategies. We then highlight how AL enhances data efficiency across various data regimes, ranging from task-specific settings with limited data to the development of general-purpose datasets and large-scale models. We further provide a systematic overview of AL applications throughout the materials research pipeline, including computational simulation, composition and structural design, process optimization, and self-driving laboratory systems. Finally, we pinpoint key challenges and future perspectives of AL in materials discovery. Bayesian optimization ; Process (computing) ; Transformative learning ; Key (lock) ; Active learning (machine learning) ; Cornerstone ; Deep learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Rogério Almeida Gouvêa | Pierre-Paul De Breuck | Tatiane Pretto | Gian-Marco Rignanese | Marcos J. L. Santos
Source: npj Computational Materials (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

Combining feature-based approaches with graph neural networks and symbolic regression for synergistic performance and interpretability Abstract This study introduces MatterVial, an innovative hybrid framework for feature-based machine learning in materials science. MatterVial expands the feature space by integrating latent representations from a diverse suite of pretrained graph-neural network (GNN) models—including structure-based (MEGNet), composition-based (ROOST), and equivariant (ORB) graph networks—with computationally efficient, GNN-approximated descriptors and novel features from symbolic regression. Our approach combines the chemical transparency of traditional feature-based models with the predictive power of deep learning architectures. When augmenting the feature-based model MODNet on Matbench tasks, this method yields significant error reductions and elevates its performance to be competitive with, and in several cases superior to, state-of-the-art end-to-end GNNs, with accuracy increases exceeding 40% for multiple tasks. An integrated interpretability module, employing surrogate models and symbolic regression, decodes the latent GNN-derived descriptors into explicit, physically meaningful formulas. This unified framework advances materials informatics by providing a high-performance, transparent tool that aligns with the principles of explainable AI, paving the way for more targeted and autonomous materials discovery. Interpretability ; Graph ; Expressive power ; Representation (politics) ; Suite ; Deep learning ; Artificial neural network ; Feature (linguistics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Caimeng Lv | Wenjun Zeng | Yangchao Luo | Ying Liang
Source: The Chemical Record (article, 2026-01-24)
Field: Chemistry — Organic Chemistry

Precision Synthesis of Polysaccharides: Unlocking the Sugar Code for Tailored Biological Functions As vital biomacromolecules, polysaccharides play pivotal roles in biological processes, including cell recognition, immune modulation, and signal transduction. Their bioactivities hinge on the precise "sugar code", comprising monosaccharide composition, glycosidic linkages, chain length, branching, and modifications. However, natural extraction faces challenges, including yield variability, low purity, and batch inconsistencies, which impede research and applications. Thus, synthetic approaches have emerged as an essential strategy for producing well-defined polysaccharides. This review summarizes recent progress in polysaccharide synthesis across chemical, enzymatic, and chemoenzymatic approaches. Framing the "structure-synthesis-function" nexus, it elucidates the design principles and application potential of bioactive polysaccharides, traces their evolution from fundamental research to industrial implementation, and offers strategic insights for drug discovery, biomaterials engineering, and functional food development. Glycosidic bond ; Monosaccharide ; Framing (construction) ; Polysaccharide ; Yield (engineering) Carbohydrate Chemistry and Synthesis ; Polysaccharides and Plant Cell Walls ; Seaweed-derived Bioactive Compounds
Authors: Imon Mia | Armi Tiihonen | Alexander U. Ernst | Anuj Srivastava | Tonio Buonassisi | William G. Vandenberghe | Julia W. P. Hsu
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Multi-variable batch Bayesian optimization in materials research: Synthetic data analysis of noise sensitivity and problem landscape effects Abstract Bayesian optimization (BO) holds promise for accelerating materials science research; however, it faces challenges with high-dimensional inputs and experimental noise in real-world problems. This study addresses these issues by benchmarking batch BO on two synthetic six-variable optimization tasks at varying noise levels: a needle-in-a-haystack task (Ackley function) representing rare materials properties, and a smooth landscape (Hartmann function) simulating process optimization. We evaluate key BO strategies, including acquisition functions, batch-picking methods, and exploration hyperparameter tuning, while presenting a framework for tracking high-dimensional optimization progress. Results show optimization outcomes are highly sensitive to noise levels and landscape shapes. This information enables the design of robust materials optimization campaigns with pre-planned experimental budgets that account for real-world uncertainties. Our methodology facilitates greater BO utilization in experimental materials research, particularly for multi-variable optimization problems, by providing practical guidance for configuring BO campaigns in challenging scientific applications. Graphical abstract Bayesian optimization ; Hyperparameter ; Noise (video) ; Sensitivity (control systems) ; Range (aeronautics) ; Synthetic data ; Bayesian probability ; Design of experiments Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Ocean Cheung | Shun Tokuda | Damian Jędrzejowski | Evelyn Ploetz | Bettina Baumgartner | Marzena Pander | Faqing Yang | Jack D. Evans | Romy Ettlinger | Stefan Wuttke | Dariusz Matoga
Source: Advanced Materials (article, 2026-02-15)
Field: Chemistry — Inorganic Chemistry

Material Preparation Information File (MPIF): A Community‐Driven Standard for Reporting MOF Syntheses The rapid growth of research on metal-organic frameworks (MOFs) and related porous materials has highlighted a critical need for standardized reporting of synthetic procedures and material properties. Synthesis consistency is often compromised by incomplete protocols and inconsistent nomenclature, with even well-established MOFs exhibiting substantial variability depending on subtle and frequently unreported factors. To address this critical challenge, we introduce the Material Preparation Information File (MPIF), a modular, machine- and human-readable format for documenting synthesis protocols alongside key characterization data. Built on the Self-defining Text Archive and Retrieval (STAR) file architecture, MPIF consolidates essential information, including reagents, conditions, equipment, and handling procedures-into a structured format that supports both digital processing and practical laboratory use. To streamline MPIF creation and editing, a web-based interface has been developed (mpif.jackdevans.com). By aligning with existing standards such as CIF and AIF, MPIF enables integration with databases, promotes FAIR data principles, and provides a foundation for data-driven materials discovery, informatics, and automation. We envision MPIF becoming a community-wide standard for transparent and consistent reporting of MOF and related material syntheses. Consistency (knowledge bases) ; Interface (matter) ; Characterization (materials science) ; Key (lock) ; Materials processing ; Protocol (science) ; Electronic materials ; Foundation (evidence) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Anyang Peng | Chun Cai | Mingyu Guo | Duo Zhang | Chengqian Zhang | Wanrun Jiang | Yinan Wang | Antoine Loew | Chengkun Wu | E Weinan | Linfeng Zhang | Han Wang
Source: npj Computational Materials (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

LAMBench: a benchmark for large atomistic models Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first-principles calculations of atomistic systems. However, our understanding of the extent to which these models achieve true universality, as well as their comparative performance across different models, remains limited. This gap is largely due to the lack of comprehensive benchmarks capable of evaluating the effectiveness of LAMs as approximations to the universal potential energy surface. In this study, we introduce LAMBench, a benchmarking system designed to evaluate LAMs in terms of their generalizability, adaptability, and applicability. These attributes are crucial for deploying LAMs as ready-to-use tools across a diverse array of scientific discovery contexts. We benchmark ten state-of-the-art LAMs released prior to August 1, 2025, using LAMBench. Our findings reveal a significant gap between the current LAMs and the ideal universal potential energy surface. They also highlight the need for incorporating cross-domain training data, supporting multi-fidelity modeling, and ensuring the models’ conservativeness and differentiability. As a dynamic and extensible platform, LAMBench is intended to continuously evolve, thereby facilitating the development of robust and generalizable LAMs capable of significantly advancing scientific research. The LAMBench code is open-sourced at https://github.com/deepmodeling/lambench, and an interactive leaderboard is available at https://www.aissquare.com/openlam?tab=Benchmark. Benchmarking ; Benchmark (surveying) ; Energy (signal processing) ; Ideal (ethics) ; Code (set theory) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Block Copolymer Self-Assembly