jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jing Lan | Hexiao Ding | Hongzhao Chen | Yufeng Jiang | Nga-Chun Ng | Gwing Kei Yip | Guiping Cheng | Yunlin Mao | Jing Cai | D. Lin | Jung Sun Yoo
Source: ArXiv.org (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

DeepMoLM: Leveraging Visual and Geometric Structural Information for Molecule-Text Modeling AI models for drug discovery and chemical literature mining must interpret molecular images and generate outputs consistent with 3D geometry and stereochemistry. Most molecular language models rely on strings or graphs, while vision-language models often miss stereochemical details and struggle to map continuous 3D structures into discrete tokens. We propose DeepMoLM: Deep Molecular Language M odeling, a dual-view framework that grounds high-resolution molecular images in geometric invariants derived from molecular conformations. DeepMoLM preserves high-frequency evidence from 1024 $\times$ 1024 inputs, encodes conformer neighborhoods as discrete Extended 3-Dimensional Fingerprints, and fuses visual and geometric streams with cross-attention, enabling physically grounded generation without atom coordinates. DeepMoLM improves PubChem captioning with a 12.3% relative METEOR gain over the strongest generalist baseline while staying competitive with specialist methods. It produces valid numeric outputs for all property queries and attains MAE 13.64 g/mol on Molecular Weight and 37.89 on Complexity in the specialist setting. On ChEBI-20 description generation from images, it exceeds generalist baselines and matches state-of-the-art vision-language models. Code is available at https://github.com/1anj/DeepMoLM. PubChem ; Language model ; Property (philosophy) ; Folding (DSP implementation) ; Geometric modeling ; Baseline (sea) ; 3D modeling ; Generalist and specialist species ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Multimodal Machine Learning Applications
Authors: Boxiao Wang | Kai Li | Tianyi Liu | Chen Li | Junzhe Wang | Yifan Zhang | Jian Cheng
Source: arXiv (Cornell University) (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

LLM-Based Scientific Equation Discovery via Physics-Informed Token-Regularized Policy Optimization Symbolic regression aims to distill mathematical equations from observational data. Recent approaches have successfully leveraged Large Language Models (LLMs) to generate equation hypotheses, capitalizing on their vast pre-trained scientific priors. However, existing frameworks predominantly treat the LLM as a static generator, relying on prompt-level guidance to steer exploration. This paradigm fails to update the model's internal representations based on search feedback, often yielding physically inconsistent or mathematically redundant expressions. In this work, we propose PiT-PO (Physics-informed Token-regularized Policy Optimization), a unified framework that evolves the LLM into an adaptive generator via reinforcement learning. Central to PiT-PO is a dual-constraint mechanism that rigorously enforces hierarchical physical validity while simultaneously applying fine-grained, token-level penalties to suppress redundant structures. Consequently, PiT-PO aligns LLM to produce equations that are both scientifically consistent and structurally parsimonious. Empirically, PiT-PO achieves state-of-the-art performance on standard benchmarks and successfully discovers novel turbulence models for challenging fluid dynamics problems. We also demonstrate that PiT-PO empowers small-scale models to outperform closed-source giants, democratizing access to high-performance scientific discovery. Scientific discovery ; Generator (circuit theory) ; Code (set theory) ; Structural equation modeling ; Mechanism (biology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Rongchao Zhang | Yiwei Lou | yu huang | Yi Xin | Yongzhi Cao | Hanpin Wang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Beyond Conservation: Flexible Molecular Assembly with Unbalanced Diffusion Bridge Molecular assembly (MA) has long been a fundamental task in chemistry and biology, with the potential to create new materials and enable novel functions beyond the molecular scale. However, its vast conformational search space poses substantial challenges, and current generative models remain limited in capturing molecular flexibility and preventing non-physical poses. In this paper, we propose AssemUDB, a diffusion bridge–based framework that learns transport mappings between two distinct flexible domains for molecular assembly generation. We reformulate the marginal matching constraint of diffusion bridges as a coupling distribution governed by unbalanced transport rather than imposing strict conservation. Subsequently, we employ a progressive process from structural relaxation in Euclidean space to assembly on the SE(3) manifold. This relaxation of marginal conservation grants the generative model greater flexibility and leads to more physically plausible atom placements. Comprehensive experiments demonstrate the superior performance of AssemUDB. Notably, we find that the method demonstrates performance comparable to, or even better than, mature tools such as PackMol for packing tasks. Flexibility (engineering) ; Relaxation (psychology) ; Constraint (computer-aided design) ; Coupling (piping) ; Process (computing) ; Matching (statistics) ; Euclidean space ; Task (project management) Machine Learning in Materials Science ; Supramolecular Chemistry and Complexes ; Modular Robots and Swarm Intelligence
Authors: Soumitra Samanta | Bob Luigjes | S.B. Lyon | Douglas B. Kell
Source: npj Materials Degradation (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Novel corrosion protective coating formulations suggested by machine learning of historic test data Abstract Protective organic coatings are formulated products comprising ingredients that are selected, combined and processed to final state with desired functionality. Generally formulated products are developed using ‘craft’ approaches based on domain knowledge and experience. However the process can involve repeated cycles of (re-)formulation, laboratory trial and field test before novel products can be brought to market. In ‘recipe’ problems such as this, the combinations scale exponentially with the number of ingredients but the number of empirical trials that can be performed is limited to a small fraction of the possibilities. In this proof-of-principle contribution, we use a machine learning tool to inform selection of ingredients, and ingredient combinations, by mapping existing coating formulations to performance characteristics from laboratory testing. The available dataset was relatively sparse, comprising just 492 salt-spray test results on coatings containing 148 different ingredients and biased towards more effective formulations. The resulting multi-objective optimisation problem was solved using a differential evolution, heuristic-based method designed to restrict over-fitting. Initial model outcomes suggested two optimal formulations using novel ingredient combinations whose performance was then verified under standard cyclic salt-spray test conditions. Both novel formulations were shown to provide similar, or better, corrosion protective performance than the reference formulation. Active ingredient ; Process (computing) ; Coating ; Ingredient ; Calibration ; Test method ; Support vector machine ; Test data Corrosion Behavior and Inhibition ; Machine Learning in Materials Science ; Structural Integrity and Reliability Analysis
Authors: Zipeng Sun | Can Chen | Ye Yuan | Haolun Wu | Jiayao Gu | Christopher Pal | Xue Nan Liu
Source: ArXiv.org (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Training Diffusion Language Models for Black-Box Optimization We study offline black-box optimization (BBO), aiming to discover improved designs from an offline dataset of designs and labels, a problem common in robotics, DNA, and materials science with limited labeled samples. While recent work applies autoregressive LLMs to BBO by formatting tasks as natural-language prompts, their left-to-right design generation struggles to capture the strong bidirectional dependencies inherent in design problems. To address this, we propose adapting diffusion LLMs to offline BBO to leverage their bidirectional modeling capabilities. However, a domain gap exists between the natural text pre-training of diffusion LLMs and the heterogeneous signals in BBO (prompts, designs, and labels). To bridge this gap, we construct a unified prompt-response corpus and introduce delimiter tokens to explicitly mark field boundaries for domain adaptation. We further propose a two-stage post-training framework to align the diffusion LLM generation with high-label designs. The first stage performs supervised fine-tuning on the unified dataset via masked-response prediction, and the second stage adopts reinforcement learning with rewards defined by label improvements. Our method achieves state-of-the-art results on Design-Bench small-data settings. Leverage (statistics) ; Domain (mathematical analysis) ; Domain adaptation ; Construct (python library) ; Field (mathematics) ; Bridge (graph theory) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Generative Adversarial Networks and Image Synthesis
Authors: Rong Wang | Huiyun Wang | Yifan Chen | Chongling Cheng | Dayang Wang
Source: Communications Chemistry (article, 2026-03-19)
Field: Materials Science — Polymers and Plastics

A versatile self-cleaning fabric coating as a detergent-free laundry product Laundry is an everyday necessity in modern life, yet its role in converting vast amounts of clean household water to wastewater remains underappreciated primarily due to the excessive reliance on detergents to achieve effective cleanliness and hygiene. To tackle this enduring issue particularly amid mounting global concerns over water scarcity, here a self-cleaning polyelectrolyte multilayer coating was developed by alternately spraying poly(diallyldimethylammonium chloride) (PDADMAC) and poly(vinylsulfonic acid) (PVS) onto various fabrics-including both hydrophobic synthetic fibers and hydrophilic cotton textiles-forming an ultra-high dense sulfonate-derived complete hydration layer that enables the effective removal of food stains, oily residues, bacteria, and fungi through simple rinsing with tap water. This innovation transforms conventional detergent-assisted, multi-step laundry practices - typically requiring 1 washing cycles and 4 rinsing cycles - to a single detergent-free rinsing cycle with enhanced cleanliness and hygiene. As a result, the overall consumption of water, electricity, and time is reduced by approximately 82%, while the discharge of detergent- and microplastic-laden wastewater is entirely eliminated. This study presents an approach for sustainable textile care that has the potential to advance environmentally responsible and resource-efficient hygiene technologies in everyday life. Coating ; Product (mathematics) ; Laundry ; Raw material Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Advanced Cellulose Research Studies
Authors: Tamanna Begam
Source: Journal of Research in Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven prediction of polymer properties: Biodegradability, strength, and diffusion The computational prediction of polymer properties represents a transformative frontier in materials science, leveraging machine learning to accelerate the discovery and optimization of advanced polymeric materials. This comprehensive review examines state-of-the-art artificial intelligence methodologies for predicting three critical polymer properties: biodegradability, mechanical strength, and gas diffusion characteristics. Recent advances in graph neural networks, physics-informed neural networks, and multi-task learning frameworks have achieved unprecedented prediction accuracies (R² > 0.96) while addressing fundamental challenges in data scarcity, chemical space extrapolation, and interpretability [1-3]. This paper synthesizes current knowledge, presents quantitative performance metrics, and discusses future research directions in AI-driven polymer informatics. Interpretability ; Polymer ; Artificial neural network ; Curse of dimensionality ; Diffusion ; Graph ; Current (fluid) Machine Learning in Materials Science ; Polymer composites and self-healing ; Advanced Polymer Synthesis and Characterization
Authors: Deaglan J. Bartlett | Harry Desmond | Pedro G. Ferreira | Gabriel Kronberger
Source: Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Introduction to the Special issue on symbolic regression in the physical sciences Abstract Symbolic regression (SR) has emerged as a powerful method for uncovering interpretable mathematical relationships from data, offering a novel route to both scientific discovery and efficient empirical modelling. This article introduces the Special issue on symbolic regression for the physical sciences, motivated by the Royal Society discussion meeting held in April 2025. The contributions collected here span applications from automated equation discovery and emergent-phenomena modelling to the construction of compact emulators for computationally expensive simulations. The introductory review outlines the conceptual foundations of SR, contrasts it with conventional regression approaches and surveys its main use cases in the physical sciences, including the derivation of effective theories, empirical functional forms and surrogate models. We summarize methodological considerations such as search-space design, operator selection, complexity control, feature selection and integration with modern AI approaches. We also highlight ongoing challenges, including scalability, robustness to noise, overfitting and computational complexity. Finally, we emphasize emerging directions, particularly the incorporation of symmetry constraints, asymptotic behaviour and other theoretical information. Taken together, the papers in this Special issue illustrate the accelerating progress of SR and its growing relevance across the physical sciences. This article is part of the discussion meeting issue ‘Symbolic regression in the physical sciences’. Overfitting ; Symbolic regression ; Regression ; Robustness (evolution) ; Relevance (law) ; Regression analysis ; Feature selection Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Evolutionary Algorithms and Applications
Authors: Rui Jiao | Xiangzhe Kong | Li Zhang | Ziyang Yu | Fangyuan Ren | Wenjuan Tan | Wenbing Huang | Yang Liu
Source: Nature Communications (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

An equivariant pretrained transformer for unified 3D molecular representation learning Pretraining on a large number of unlabeled 3D molecules has showcased superiority in various scientific applications. However, prior efforts typically focus on pretraining models in a specific domain, missing the opportunity to leverage cross-domain knowledge. To mitigate this gap, we introduce Equivariant Pretrained Transformer, an all-atom foundation model that can be pretrained from multiple domain 3D molecules. Built upon an E(3)-equivariant transformer, the model learns both atom-level interactions and graph-level structural features (e.g. residuals in proteins), allowing it to generalize across diverse tasks. The model achieves strong gains in ligand binding affinity prediction, while also performing competitively in predicting properties of proteins and small molecules. We further show that the model can help identify potential antiviral compounds against the main protease of the COVID-19 virus, and validate promising candidates through computational and experimental studies. Leverage (statistics) ; Transformer ; Equivariant map ; Representation (politics) ; Feature learning ; Focus (optics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Fanjie Xu | Jinyuan Hu | Jingxiang Zou | Junjie Wang | Boying Huang | Zhifeng Gao | Xiaohong Ji | Weinan E | Zhong-Qun Tian | Fujie Tang | Jun Cheng
Source: arXiv (Cornell University) (article, 2026-02-09)
Field: Chemistry — Spectroscopy

NMRPeak: a ready-to-use intelligent system for molecular structure elucidation enabled by synergistic cross-modal learning One-dimensional nuclear magnetic resonance (NMR) spectroscopy is essential for molecular structure elucidation in organic synthesis, drug discovery, natural product characterization, and metabolomics, yet its interpretation remains heavily dependent on expert knowledge and difficult to scale. Although machine learning has been applied to NMR spectrum prediction, library retrieval, and structure generation, these tasks have evolved in isolation using simulated data and incompatible spectral representations, limiting their utility under real experimental scenarios. Here we present NMRPeak, a unified cross-modal learning system that integrates these three tasks through experimentally grounded design. We curate approximately 1.8 million experimental and simulated spectra to construct the largest benchmark for NMR-based structure elucidation and systematically quantify the distribution shift between these domains. We introduce a chemically-aware adaptive tokenizer that dynamically balances discretization granularity to preserve spectral semantics while controlling vocabulary size, and an assignment-free peak-aware similarity metric that enables direct comparison between predicted and experimental spectra. Through a unified molecule-to-spectrum paradigm and synergistic coupling of prediction, retrieval, and generation modules, NMRPeak achieves transformative performance on experimental benchmarks: it overcomes the longstanding simulation-to-experiment gap in spectrum prediction while delivering over 95% top-1 accuracy in molecular retrieval and approximately 75% top-1 accuracy in stereochemistry-aware de novo structure generation. These capabilities establish a foundation for automated, high-throughput molecular structure elucidation in organic synthesis, drug discovery, and chemical biology. Granularity ; Metric (unit) ; Chemical space ; Construct (python library) ; Benchmark (surveying) ; Similarity (geometry) ; Semantics (computer science) ; Automatic summarization Molecular spectroscopy and chirality ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Chengqian Zhang | Duo Zhang | Anyang Peng | Mingyu Guo | Yuzhi Zhang | Lei Wang | Guolin Ke | Linfeng Zhang | Tiejun Li | Han Wang
Source: ArXiv.org (article, 2026-01-13)
Field: Materials Science — Materials Chemistry

Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization in Atomistic Models Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-distribution accuracy after fine-tuning, their reliability under out-of-distribution (OOD) conditions remains unclear. We identify a critical failure mode in downstream adaptation: standard fine-tuning induces representation collapse, erasing pretrained chemical and structural priors and severely degrading OOD performance. To address this limitation, we propose multi-task fine-tuning (MFT), which jointly optimizes downstream property prediction with a physically grounded force-field objective inherited from pretraining. This approach preserves essential chemical priors while enabling task-specific adaptation. Across molecular and materials benchmarks, MFT consistently improves OOD generalization, approaching the theoretical limit set by in-distribution accuracy, while outperforming standard fine-tuning, training from scratch, and state-of-the-art task-specific models. These results establish safe adaptation as a central requirement for large atomistic models and position MFT as a practical and data-efficient pathway toward robust molecular and materials discovery. Generalization ; Prior probability ; Reliability (semiconductor) ; Representation (politics) ; Property (philosophy) ; Set (abstract data type) ; Limit (mathematics) ; Position (finance) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Authors: Haobo Guo | Karandeep Grover | Elizabeth J. New
Source: Advanced Sensor Research (article, 2026-02-01)
Field: Chemistry — Spectroscopy

Machine Learning Assisted Fluorescent Sensor Array for Sensing Applications ABSTRACT Fluorescent sensor arrays provide pattern‑based, multidimensional optical fingerprints for detecting chemically and biologically diverse analytes across complex matrices. By leveraging orthogonal readouts (intensity, ratiometric channels, lifetime, and excitation–emission matrices) from cross‐reactive and target‐specific elements, fluorescent sensor arrays achieve sensitive, rapid measurements suitable for environmental, biomedical, and food‑safety applications. The data richness of fluorescent sensor arrays, however, exceeds the capabilities of traditional analytical approaches. Classical chemometrics, exemplified by principal component analysis for exploratory visualisation and linear discriminant analysis for baseline classification, assumes linear structure and homoscedasticity, and therefore struggles with non‑linear photophysical responses, multicollinearity, and mixture quantification. This review surveys machine‑learning methods that address these limitations for both discrimination and quantification, including support vector machines and k‑nearest neighbours, tree ensembles, Gaussian process and support‑vector regression, and neural/deep‑learning models tailored for spectra, excitation–emission matrices, and images. Practical guidance is provided on acquisition and pre‑processing, rigorous validation (nested cross‑validation, external tests), uncertainty quantification, and interpretability to inform array design and deployment. Case studies demonstrate improved sensitivity, selectivity, robustness, and calibration transfer. Remaining challenges, dataset size, drift, and matrix effects, are discussed alongside opportunities in excitation‑multiplexed “virtual arrays”, active learning, and explainable AI for next‑generation, data‑driven fluorescent sensing. Interpretability ; Calibration ; Linear discriminant analysis ; Principal component analysis ; Analyte ; Pattern recognition (psychology) ; Process (computing) ; Support vector machine ; Sensor array Molecular Sensors and Ion Detection ; Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Sensors