jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pengfei Liu | Shuang Ge | Jun Tao | Zhixiang Ren
Source: ArXiv.org (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

A Multi-task Large Reasoning Model for Molecular Science Advancements in artificial intelligence for molecular science are necessitating a paradigm shift from purely data-driven predictions to knowledge-guided computational reasoning. Existing molecular models are predominantly proprietary, lacking general molecular intelligence and generalizability. This underscores the necessity for computational methods that can effectively integrate scientific logic with deep learning architectures. Here we introduce a multi-task large reasoning model designed to emulate the cognitive processes of molecular scientists through structured reasoning and reflection. Our approach incorporates multi-specialist modules to provide versatile molecular expertise and a chain-of-thought (CoT) framework enhanced by reinforcement learning infused with molecular knowledge, enabling structured and reflective reasoning. Systematic evaluations across 10 molecular tasks and 47 metrics demonstrate that our model achieves an average 50.3% improvement over the base architecture, outperforming over 20 state-of-the-art baselines, including ultra-large-parameter foundation models, despite using significantly fewer training data and computational resources. This validates that embedding explicit reasoning mechanisms enables high-efficiency learning, allowing smaller-scale models to surpass massive counterparts in both efficacy and interpretability. The practical utility of this computational framework was validated through a case study on the design of central nervous system (CNS) drug candidates, illustrating its capacity to bridge data-driven and knowledge-integrated approaches for intelligent molecular design. Computational model ; Reinforcement learning ; Bridge (graph theory) ; Cognition ; Qualitative reasoning ; Embedding ; Model-based reasoning ; Task (project management) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Sergei V Kalinin | Boris N. Slautin | Yu Liu | Elham Foadian | Gerd Duscher | Mahshid Ahmadi | Timur Bazhirov
Source: ChemRxiv (article, 2026-02-08)
Field: Materials Science — Materials Chemistry

The Missing Piece of Automated Scientist: Enabling lab autonomy with reward functions and executable workflows Autonomous scientists enabled by large language models and agentic reasoning have become a compelling narrative for accelerating research. Here, we pose that laboratory autonomy is not primarily a text problem, since experiments have to be run in the world of instrument-addressable actions and constraints, and planned based on costs, latencies, and rewards. We argue for a bottomup foundation for self-driving laboratories (SDLs) that complements language-first planning by introducing frameworks that translate hypotheses into decisions executable on real tools. We propose three operational principles, including (a) adopt an information-efficiency objective that maximizes decision-relevant information per unit experimental cost through comprehensive acquisition and minimal decisive experimental design, (b) require hypotheses to compile into experimental footprints defined by experimentally-accessible actions, constraints, observables, and decision rules, and (c) treat reward design as the central bottleneck in closed-loop experimentation, especially when mapping high-dimensional measurements to utility and extending to sequential decision-making. We frame SDLs as long-horizon, out-of-distribution discovery systems where accelerating the full experiment–model–decision loop and emphasizing physical and chemical knowledge discovery over short-term correlations is necessary to translate lab-scale optimization into durable technological and manufacturing impact. Executable ; Workflow ; Bottleneck ; Frame (networking) ; Inference ; Compiler ; Knowledge acquisition Machine Learning in Materials Science ; Scientific Computing and Data Management ; Biomedical Text Mining and Ontologies
Authors: Jie Cui | Yao Zhang | Yang Zhang | Yi Luo | Zhen-Chao Dong
Source: arXiv (Cornell University) (article, 2026-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

TERS-ABNet: A Deep Learning Approach for Automated Single-Molecule Structure Reconstruction with Atomic Precision from TERS Mapping Determining the chemical structure for a single molecule on surface from spectroscopic data represents a challenging high-dimensional inverse problem. Tip-enhanced Raman spectroscopy (TERS) enables chemically specific imaging of single molecules with sub-nanometer spatial resolution, yet reconstructing complete molecular structures from TERS maps remains difficult owing to the ambiguous vibrational signatures and reliance on expert interpretation. Here, we introduce TERS-ABNet, a deep-learning framework that formulates single-molecule structure determination from spectroscopic images as an image-to-graph inference task. Using a "two-track" architecture, the model jointly predicts probabilistic atom and bond maps, enabling direct construction of explicit atom-bond graphs without relying on predefined chemical rules. Trained on simulated datasets, TERS-ABNet achieves about 94% atom-type classification accuracy (with a mean coordinate error of about 0.23 Å), enabling to reliably recovering molecular connectivity and fully reconstruct single-molecule structure from its TERS maps. The framework generalizes across varying spatial resolutions and structural complexity through transfer learning, and successfully reconstructs the atomic structure of a single porphyrin molecule from experimental TERS data. This work establishes a general deep-learning strategy for inferring explicit atom-bond graph representations from high-dimensional spectroscopic imaging data, providing a new pathway towards automated molecular structure determination in nanoscale characterization. Inverse problem ; Probabilistic logic ; Inference ; Deep learning ; Iterative reconstruction ; Graph ; Inverse ; Computation Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Luminescence and Fluorescent Materials
Authors: Christoph Bartmann | Johannes Schimunek | Mykyta Ielanskyi | Philipp Seidl | Gnter Klambauer | Sohvi Luukkonen
Source: ArXiv.org (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

MolecularIQ: Characterizing Chemical Reasoning Capabilities Through Symbolic Verification on Molecular Graphs A molecule's properties are fundamentally determined by its composition and structure encoded in its molecular graph. Thus, reasoning about molecular properties requires the ability to parse and understand the molecular graph. Large Language Models (LLMs) are increasingly applied to chemistry, tackling tasks such as molecular name conversion, captioning, text-guided generation, and property or reaction prediction. Most existing benchmarks emphasize general chemical knowledge, rely on literature or surrogate labels that risk leakage or bias, or reduce evaluation to multiple-choice questions. We introduce MolecularIQ, a molecular structure reasoning benchmark focused exclusively on symbolically verifiable tasks. MolecularIQ enables fine-grained evaluation of reasoning over molecular graphs and reveals capability patterns that localize model failures to specific tasks and molecular structures. This provides actionable insights into the strengths and limitations of current chemistry LLMs and guides the development of models that reason faithfully over molecular structure. Molecular graph ; Parsing ; Benchmark (surveying) ; Verifiable secret sharing ; Property (philosophy) ; Qualitative reasoning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: He Cao | Siyu Liu | Fan Zhang | Zijing Liu | Hao Li (31608) | Bin Feng | Shengyuan Bai | Leqing Chen | Kai Xie | Yu Li
Source: arXiv (Cornell University) (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Mozi: Governed Autonomy for Drug Discovery LLM Agents Tool-augmented large language model (LLM) agents promise to unify scientific reasoning with computation, yet their deployment in high-stakes domains like drug discovery is bottlenecked by two critical barriers: unconstrained tool-use governance and poor long-horizon reliability. In dependency-heavy pharmaceutical pipelines, autonomous agents often drift into irreproducible trajectories, where early-stage hallucinations multiplicatively compound into downstream failures. To overcome this, we present Mozi, a dual-layer architecture that bridges the flexibility of generative AI with the deterministic rigor of computational biology. Layer A (Control Plane) establishes a governed supervisor--worker hierarchy that enforces role-based tool isolation, limits execution to constrained action spaces, and drives reflection-based replanning. Layer B (Workflow Plane) operationalizes canonical drug discovery stages -- from Target Identification to Lead Optimization -- as stateful, composable skill graphs. This layer integrates strict data contracts and strategic human-in-the-loop (HITL) checkpoints to safeguard scientific validity at high-uncertainty decision boundaries. Operating on the design principle of ``free-form reasoning for safe tasks, structured execution for long-horizon pipelines,'' Mozi provides built-in robustness mechanisms and trace-level audibility to completely mitigate error accumulation. We evaluate Mozi on PharmaBench, a curated benchmark for biomedical agents, demonstrating superior orchestration accuracy over existing baselines. Furthermore, through end-to-end therapeutic case studies, we demonstrate Mozi's ability to navigate massive chemical spaces, enforce stringent toxicity filters, and generate highly competitive in silico candidates, effectively transforming the LLM from a fragile conversationalist into a reliable, governed co-scientist. Robustness (evolution) ; Flexibility (engineering) ; Hierarchy ; Drug discovery ; Action (physics) ; Identification (biology) ; Generative grammar ; Orchestration Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Liheng Yu | Zhe Zhao | X. Wang | Di Wu | Pengkun Wang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Rethinking Crystal Symmetry Prediction: A Decoupled Perspective Efficiently and accurately determining the symmetry is a crucial step in the structural analysis of crystalline materials. Existing methods usually mindlessly apply deep learning models while ignoring the underlying chemical rules. More importantly, experiments show that they face a serious sub-property confusion SPC problem. To address the above challenges, from a decoupled perspective, we introduce the XRDecoupler framework, a problem-solving arsenal specifically designed to tackle the SPC problem. Imitating the thinking process of chemists, we innovatively incorporate multidimensional crystal symmetry information as superclass guidance to ensure that the model's prediction process aligns with chemical intuition. We further design a hierarchical PXRD pattern learning model and a multi-objective optimization approach to achieve high-quality representation and balanced optimization. Comprehensive evaluations on three mainstream databases (e.g., CCDC, CoREMOF, and InorganicData) demonstrate that XRDecoupler excels in performance, interpretability, and generalization. Symmetry (geometry) ; Perspective (graphical) ; Process (computing) ; Representation (politics) ; Confusion ; Face (sociological concept) ; Crystal (programming language) ; Phase (matter) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Yongqiang Chen | Chenxi Liu | Zhenhao Chen | Tongliang Liu | Bo Jun Han | Kun Zhang
Source: arXiv (Cornell University) (article, 2026-03-15)
Field: Materials Science — Materials Chemistry

CausalEvolve: Towards Open-Ended Discovery with Causal Scratchpad Evolve-based agent such as AlphaEvolve is one of the notable successes in using Large Language Models (LLMs) to build AI Scientists. These agents tackle open-ended scientific problems by iteratively improving and evolving programs, leveraging the prior knowledge and reasoning capabilities of LLMs. Despite the success, existing evolve-based agents lack targeted guidance for evolution and effective mechanisms for organizing and utilizing knowledge acquired from past evolutionary experience. Consequently, they suffer from decreasing evolution efficiency and exhibit oscillatory behavior when approaching known performance boundaries. To mitigate the gap, we develop CausalEvolve, equipped with a causal scratchpad that leverages LLMs to identify and reason about guiding factors for evolution. At the beginning, CausalEvolve first identifies outcome-level factors that offer complementary inspirations in improving the target objective. During the evolution, CausalEvolve also inspects surprise patterns during the evolution and abductive reasoning to hypothesize new factors, which in turn offer novel directions. Through comprehensive experiments, we show that CausalEvolve effectively improves the evolutionary efficiency and discovers better solutions in 4 challenging open-ended scientific tasks. Surprise ; Key (lock) ; Evolutionary algorithm ; Abductive reasoning ; Causal reasoning Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Language and cultural evolution
Authors: Yue Wua | Tianhao Su | Rui Hu | Mingchuan Zhao | Shunbo Hu | Deng Pan | Jizhong Huang
Source: ArXiv.org (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Epistemic Closure: Autonomous Mechanism Completion for Physically Consistent Simulation The integration of Large Language Models (LLMs) into scientific discovery is currently hindered by the Implicit Context problem, where governing equations extracted from literature contain invisible thermodynamic assumptions (e.g., undrained conditions) that standard generative models fail to recognize. This leads to Physical Hallucination: the generation of syntactically correct solvers that faithfully execute physically invalid laws. Here, we introduce a Neuro-Symbolic Generative Agent that functions as a cognitive supervisor atop traditional numerical engines. By encapsulating physical laws into modular Constitutive Skills and leveraging latent intrinsic priors, the Agent employs a Chain-of-Thought reasoning workflow to autonomously validate, prune, and complete physical mechanisms. We demonstrate this capability on the challenge of thermal pressurization in low-permeability sandstone. While a standard literature-retrieval baseline erroneously predicts catastrophic material failure by blindly adopting a rigid "undrained" simplification, our Agent autonomously identifies the system as operating in a drained regime (Deborah number De << 1) via dimensionless scaling analysis. Consequently, it inductively completes the missing dissipation mechanism (Darcy flow) required to satisfy boundary constraints, predicting a stable stress path consistent with experimental reality. This work establishes a paradigm where AI agents transcend the role of coding assistants to act as epistemic partners, capable of reasoning about and correcting the theoretical assumptions embedded in scientific data. Generative grammar ; Context (archaeology) ; Modular design ; Workflow ; Satisfiability modulo theories ; Physical system ; Interface (matter) ; Domain (mathematical analysis) ; Mechanism (biology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topic Modeling
Authors: Xiaohan Qin | Chao Wang | Zhengyang Zhou | Linjiang Chen | Wenjie Du | Yang Wang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Chemistry — Spectroscopy

MSAnchor: De Novo Molecular Generation from Mass Spectrometry Data with Anchor-Extended Molecular Scaffolds Tandem mass spectrometry (MS/MS) is a critical tool for identifying molecular structures. By efficiently separating molecular fragments based on their mass-to-charge (m/z) ratios, it facilitates molecular generation and subsequent scientific discoveries. However, de novo molecular generation from MS/MS spectra remains fundamentally constrained by two paramount challenges: the vast chemical space requires effective structural constraints, and the absence of fine-grained substructural generation weakens the correspondences between spectral features and molecular structures. In this work, we propose MSAnchor, a novel two-stage framework for MS/MS-based molecular structure generation. We mitigate the search space challenge through the introduction of Anchor-Extended Molecular Scaffold (AEMS) representation that explicitly encodes side-chain anchoring points, thereby dramatically reducing combinatorial complexity. Leveraging the explicit attachment sites provided by AEMS, we develop anchor-specific priors that establish effective alignments between spectral features and molecular substructures. This fine-grained substructural correspondence is further enhanced by a modified Conditional Information Bottleneck (CIB) module that extracts the most informative spectral components in a structure-aware manner. These innovations enable MSAnchor to generate molecular structures that closely reflect spectral characteristics while constraining combinatorial complexity. Extensive experiments on the CANOPUS and MassSpecGym datasets demonstrate that MSAnchor achieves state-of-the-art performance in molecular structure prediction from MS/MS spectra, with performance improvements that are particularly more pronounced for molecules with higher complexity. Molecular model ; Molecular dynamics ; Molecular mass ; Representation (politics) ; Bottleneck ; Chemical space ; Molecular recognition ; Mass spectrometry Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Yougang Lyu | Xi Zhang | Xinhao Yi | Yuyue Zhao | Shuyu Guo | Wenxiang Hu | Jan A. Piotrowski | Jakub Kaliski | Jacopo Urbani | Zaiqiao Meng | Lun Zhou | Xiaohui Yan
Source: ArXiv.org (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

EvoScientist: Towards Multi-Agent Evolving AI Scientists for End-to-End Scientific Discovery The increasing adoption of Large Language Models (LLMs) has enabled AI scientists to perform complex end-to-end scientific discovery tasks requiring coordination of specialized roles, including idea generation and experimental execution. However, most state-of-the-art AI scientist systems rely on static, hand-designed pipelines and fail to adapt based on accumulated interaction histories. As a result, these systems overlook promising research directions, repeat failed experiments, and pursue infeasible ideas. To address this, we introduce EvoScientist, an evolving multi-agent AI scientist framework that continuously improves research strategies through persistent memory and self-evolution. EvoScientist comprises three specialized agents: a Researcher Agent (RA) for scientific idea generation, an Engineer Agent (EA) for experiment implementation and execution, and an Evolution Manager Agent (EMA) that distills insights from prior interactions into reusable knowledge. EvoScientist contains two persistent memory modules: (i) an ideation memory, which summarizes feasible research directions from top-ranked ideas while recording previously unsuccessful directions; and (ii) an experimentation memory, which captures effective data processing and model training strategies derived from code search trajectories and best-performing implementations. These modules enable the RA and EA to retrieve relevant prior strategies, improving idea quality and code execution success rates over time. Experiments show that EvoScientist outperforms 7 open-source and commercial state-of-the-art systems in scientific idea generation, achieving higher novelty, feasibility, relevance, and clarity via automatic and human evaluation. EvoScientist also substantially improves code execution success rates through multi-agent evolution, demonstrating persistent memory's effectiveness for end-to-end scientific discovery. CLARITY ; Code (set theory) ; Quality (philosophy) ; Scientific discovery ; Ideation ; Codebase Machine Learning in Materials Science ; Scientific Computing and Data Management ; Topic Modeling
Authors: Xinwei Zhu | Diandong Tang | Lin Shen | Wei-Hai FANG
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Fewest-Switches Surface Hopping with Combined Deep Learning Potential and Long Short-Term Memory Network Propagator for Simulating Realistic Photochemical Processes Fewest-switches surface hopping (FSSH) is the most popular method for simulating photochemical processes of molecular systems. Recently, we have constructed long short-term memory (LSTM) networks as a propagator for electronic subsystems in FSSH dynamics simulations. The collective results on Tully's three models have been reproduced satisfactorily. In the present work, we develop an extended LSTM-FSSH framework to simulate realistic photochemical reactions. The input features of LSTM as well as the training procedure are redesigned to represent high-dimensional nuclear degrees of freedom in an effective way. Equivariant neural networks are integrated with LSTM to build adiabatic potential energy surfaces in ground and excited states. Photoisomerizations of $\mathrm{CH_2NH}$ and azobenzene are simulated, showing that our new proposed LSTM-FSSH method can produce excited-state lifetimes and product yields accurately in comparison with conventional FSSH simulations as reference. Only 10 reference trajectories are required for training LSTM networks, and then a trajectory ensemble can be generated with very efficient LSTM-FSSH dynamics simulations to obtain collective results. Surface hopping ; Propagator ; Trajectory ; Excited state ; Adiabatic process ; Artificial neural network ; Degrees of freedom (physics and chemistry) ; Surface (topology) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Photochromic and Fluorescence Chemistry