jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zheng Wang | Zehui Lin | Wanyu Lin | Ming Yang | Minggang Zeng | Kay Chen Tan
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models Providing explainable molecular property predictions is critical for many scientific domains, such as drug discovery and material science. Though transformer-based language models have shown great potential in accurate molecular property prediction, they neither provide chemically meaningful explanations nor faithfully reveal the molecular structure-property relationships. In this work, we develop a framework for explainable molecular property prediction based on language models, dubbed as Lamole, which can provide chemical concepts-aligned explanations. We take a string-based molecular representation - Group SELFIES - as input tokens to pre-train and fine-tune our Lamole, as it provides chemically meaningful semantics. By disentangling the information flows of Lamole, we propose considering both self-attention weights and gradients for better quantification of each chemically meaningful substructure's impact on the model's output. To make the explanations more faithful to the structure-property relationship, we then carefully craft a marginal loss to explicitly optimize the explanations to align with the chemists' annotations. We bridge the manifold hypothesis with the elaborated marginal loss to prove that the loss can align the explanations with the tangent space of the data manifold, leading to concept-aligned explanations. Experimental results over eight datasets demonstrate Lamole can achieve comparable prediction accuracy and boost the explanation accuracy by up to $14.3\%$, being the state-of-the-art in explainable molecular property prediction. To further illustrate the actionable utility of the explanations derived from Lamole, we integrated the framework with an evolutionary algorithm. This integration established an interpretable optimization pipeline for molecular editing, demonstrating that Lamole functions beyond simple post-hoc analysis but serves as a practical guide for molecule discovery. The code is available at the provided link: https://anonymous.4open.science/r/Lamole-7482. Property (philosophy) ; Representation (politics) ; Pipeline (software) ; Chemical space ; Bridge (graph theory) ; Heuristics ; Language model ; Natural language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Jiahe Song | Chuang Wang | Yinfan Wang | Hao Zheng | Rui Nie | Bowen Jiang | Xingjian Wei | Junyuan Gao | Yubin Wang | Bin Wang | Lijun Wu | Jiang Wu | Qian Yu | Conghui He
Source: ArXiv.org (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Molecular Identifier Visual Prompt and Verifiable Reinforcement Learning for Chemical Reaction Diagram Parsing Reaction diagram parsing (RxnDP) is critical for extracting chemical synthesis information from literature. Although recent Vision-Language Models (VLMs) have emerged as a promising paradigm to automate this complex visual reasoning task, their application is fundamentally bottlenecked by the inability to align visual chemical entities with pre-trained knowledge, alongside the inherent discrepancy between token-level training and reaction-level evaluation. To address these dual challenges, this work enhances VLM-based RxnDP from two complementary perspectives: prompting representation and learning paradigms. First, we propose Identifier as Visual Prompting (IdtVP), which leverages naturally occurring molecule identifiers (e.g., bold numerals like 1a) to activate the chemical knowledge acquired during VLM pre-training. IdtVP enables powerful zero-shot and out-of-distribution capabilities, outperforming existing prompting strategies. Second, to further optimize performance within fine-tuning paradigms, we introduce Re3-DAPO, a reinforcement learning algorithm that leverages verifiable rewards to directly optimize reaction-level metrics, thereby achieving consistent gains over standard supervised fine-tuning. Additionally, we release the ScannedRxn benchmark, comprising scanned historical reaction diagrams with real-world artifacts, to rigorously assess model robustness and out-of-distribution ability. Our contributions advance the accuracy and generalization of VLM-based reaction diagram parsing. We will release data, models, and code on GitHub. Identifier ; Reinforcement learning ; Parsing ; Robustness (evolution) ; Verifiable secret sharing ; Representation (politics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: M. Alexander Eltester | Claudia D. Roth | Michael E. Tauchert
Source: Inorganic Chemistry (article, 2026-01-28)
Field: Chemistry — Organic Chemistry

Tuning Au → M Interactions: d-Block Metalloligands Enhance Au-Catalyzed CO <sub>2</sub> Hydrosilylation L<sub>2</sub>Z-type ligands are powerful platforms for fine-tuning π-acidic Au<sup>I</sup> catalysis. In this paper, we present a series of [(L<sub>2</sub>Z)Au<sup>I</sup>Cl]<sup><i>n</i>+</sup> and [(L<sub>2</sub>Z)Au<sup>I</sup>]<sup>(<i>n</i>+1)+</sup> complexes in which Mn<sup>2+</sup>, Fe<sup>2+</sup>, Co<sup>2+</sup>, Zn<sup>2+</sup>, Cu<sup>+</sup>, Ag<sup>+</sup>, Cd<sup>2+</sup>, and Tl<sup>+</sup> act as Z-type metalloligands. The resulting Au→M bonds are characterized by single-crystal X-ray diffraction analysis and further studied by computational methods. The catalytic activity of these complexes in Au hydride-mediated CO<sub>2</sub> to formate hydrosilylation catalysis was explored in detail, including in situ Raman spectroscopic monitoring. The leverage of the metalloligands to fine-tune our Au<sup>I</sup> catalyst resulted in a TOF<sub>50</sub> of up to 2410 h<sup>-1</sup> under mild conditions. Hydrosilylation ; Catalysis ; Formate ; Methyl formate ; In situ ; Raman spectroscopy Organoboron and organosilicon chemistry ; Asymmetric Hydrogenation and Catalysis ; Catalytic Alkyne Reactions
Authors: Eduardo Pereira Soares | Emílio Vital Brazil | Victor Shirasuna | Dmitry Yu. Zubarev | Renato Cerqueira | Kristin Schmidt
Source: Communications Chemistry (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Author Correction: An open-source family of large encoder-decoder foundation models for chemistry Foundation (evidence) ; Work (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Fabian Mehner | Aniket R. Bukane | Daniel J. Keddie | Martin Geisler | Albena Lederer | Brigitte Voit | Jens Gaitzsch
Source: Communications Chemistry (article, 2026-04-09)
Field: Chemistry — Organic Chemistry

RAFT enables controlled radical ring-opening polymerisation of cyclic ketene acetals for degradable nanoparticles Following the demand for biodegradable polymers in biomedicine and cosmetics, the radical ring-opening polymerisation (RROP) of cyclic ketene acetals (CKAs) offers a robust synthesis approach to prepare a wide range of polyesters. Here we demonstrate a method to control the RROP of 2-methylene-1,3,6-trioxocane (MTC) in terms of molar mass, dispersity, end-group control and kinetics using the reversible addition and fragmentation chain transfer polymerisation (RAFT) methodology. Fine-tuning the ratio between the reactants allows for a better understanding of the interplay of RAFT and RROP. With so optimised reaction conditions, a well-defined PMTC-macroinitiator was obtained and a chain extension with MTC and 2-methylene-1,3-dioxepane (MDO) was applied to form P(CKA-b-CKA) block copolymers. These were then formulated into fully biodegradable polymeric nanoparticles with tuneable degradation time. This work hence pushes the boundaries of RROP towards a whole expanded range of defined homopolymers and fully CKA-based block-copolymers as well as their completely biodegradable nanoparticles. This controlled RROP opens more areas of application for RROP-based polyesters. Ketene ; Chain transfer ; Raft ; Polymerization ; Reversible addition−fragmentation chain-transfer polymerization ; Molar mass ; Polymer ; Nanoparticle Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties ; Carbon dioxide utilization in catalysis
Authors: Anne K. Ravn | Aimee L. Bangerter | Ethan M. Wagner | Shijia Li | Camille Rubel | Steven R. Wisniewski | Peng Liu | Will R. Gutekunst | Keary M. Engle
Source: ACS Central Science (article, 2026-02-20)
Field: Chemistry — Organic Chemistry

<i>B</i> -Alkyl-borabicyclo[3.3.1]nonane Reagents Promote Closed-Shell Nickel-Catalyzed Alkylarylation Toward Encoded Cyclooctene Monomers Access to new tailored monomers is essential to explore unprecedented polymer structures and modulate their properties. In previous work, we disclosed an iteroselective diarylation of 1,5-cyclooctadiene as an attractive platform for preparing diverse cyclooctene monomers; however, it was limited to C-(sp<sup>2</sup>) coupling partners. Established difunctionalization methods with C-(sp<sup>3</sup>) fragments rely on carbon-based radical formation, which is incompatible with 1,5-cyclooctadiene. This work describes a two-electron redox manifold for the nickel-catalyzed alkylarylation of 1,5-cyclooctadiene and discloses <i>B</i>-alkyl-borabicyclo-[3.3.1]-nonane (alkyl-9-BBN) as an effective transmetalating reagent for maintaining a polar reaction mechanism. The method provides 5,6-alkylarylated cyclooctenes suitable for ring-opening metathesis polymerization to obtain new materials. The properties of these polymers are benchmarked and fine-tuned by variation of coupling partners in the nickel catalysis. Density functional theory calculations revealed that destabilization of the pretransmetalation complex promotes the reactivity of alkyl-9-BBN in transmetalation compared to alkylboronic esters. Reagent ; Monomer ; Transmetalation ; Reactivity (psychology) ; Polymerization ; Metathesis ; Cyclooctene ; Polymer Organoboron and organosilicon chemistry ; Synthetic Organic Chemistry Methods ; Organometallic Complex Synthesis and Catalysis
Authors: Chuanzhi Ju | Zi Li | Yiyu Kang | Fancheng Meng | Jianxin Zou | Zhigang Hu
Source: Small Structures (article, 2026-02-01)
Field: Chemistry — Inorganic Chemistry

One‐Step Modulated Hydrothermal Synthesis of Nickel‐Doped HKUST‐1 for Improved Hydrogen Storage Performance Metal‐organic frameworks (MOFs) have shown exceptional capability of hydrogen storage due to the porous attributes and open metal sites (OMSs) for physical bonding, leading to attempts of hydrogen (H 2 ) sorption for clean energy purposes. However, it has been a great challenge to balance between developing a simple MOF synthesis method for industrialization and pursuing high H 2 uptake capacities for hydrogen storage applications. Herein, we report a one‐step modulated hydrothermal (MHT) method for doping nickel ions (Ni 2+ ) into [Cu 3 (BTC) 2 (H 2 O) 3 ] n (where BTC is benzene‐1,3,5‐tricarboxylate), or HKUST‐1. Such a Ni@HKUST‐1 MOF possesses a higher surface area (1536 m 2 g −1 vs. 1213 m 2 g −1 ) with fine‐tuned structure, which enables a H 2 uptake capacity of 4.5 wt%, or 39.8 g L −1 , at 77 K and 100 bar. This result is 25% greater than that of pristine HKUST‐1, which is 3.6 wt%. Molecular simulations demonstrate the advantages of introducing Ni 2+ to the parent MOF, including possessing stronger adsorption of H 2 and fine‐tuning the microstructure to allow more space for combination with OMSs. Therefore, this work gives a prospective solution to the challenge of industrially synthesizing MOF for practical hydrogen storage and paves the way for techno‐economically efficient application of MOF in multiple fields. Hydrogen storage ; Hydrogen ; Adsorption ; Hydrothermal synthesis ; Microstructure ; Nickel ; Hydrothermal circulation ; Sorption ; Porosity Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies
Authors: Xiangping Duan | Chaozhi Xiong | Zhen‐Wu Shao | Yuxin Liu | Xiaoting Li | Li Xiong | Chong Liu
Source: Chemistry - A European Journal (article, 2026-04-03)
Field: Chemistry — Inorganic Chemistry

An Ultra‐Stable Cr–Hydroxamate Metal–Organic Framework From Postsynthetic Transmetalation Hydroxamate-based metal-organic frameworks (MOFs) have attracted increasing interest due to their strong chelating ability and inherent chemical robustness; however, their stability has yet to rival that of conventional carboxylate-based MOFs. Herein, using a tris-hydroxamic acid linker (H<sub>3</sub>TATPHA), we report a family of isostructural MOFs (SUM-86) incorporating Ga(III), In(III), and Fe(III) with a two-fold interpenetrated srs topology. Remarkably, nearly quantitative transmetalation of SUM-86(In) with Cr(III) affords SUM-86(Cr) in a single-crystal-to-single-crystal manner, preserving framework topology and morphology. Benefiting from the inert coordination chemistry of Cr(III) and strong hydroxamate chelation, SUM-86(Cr) exhibits exceptional chemical stability, retaining crystallinity in boiling water and in highly corrosive aqueous solutions, including 3 M HCl and 5 M NaOH. Moreover, SUM-86(Cr) shows rapid and high-capacity iodine adsorption from solution. This work establishes an effective strategy for accessing ultra-stable hydroxamate-based MOFs and highlights postsynthetic transmetalation as a powerful approach for framework fine-tuning and stability enhancement. Transmetalation ; Isostructural ; Chemical stability ; Aqueous solution ; Chelation ; Metal-organic framework ; Inert Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Layered Double Hydroxides Synthesis and Applications
Authors: Ibrahim Elsharkawy | V. M. Mikuni | Wahid Bhimji | Benjamin Nachman
Source: ArXiv.org (article, 2026-01-15)
Field: Materials Science — Materials Chemistry

OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers We present OmniMol, a state-of-the-art transformer-based small molecule machine-learned interatomic potential (MLIP). OmniMol is built by adapting Omnilearned, a foundation model for particle jets found in high-energy physics (HEP) experiments such as at the Large Hadron Collider (LHC). Omnilearned is built with a Point-Edge-Transformer (PET) and pre-trained using a diverse set of one billion particle jets. It includes an interaction-matrix attention bias that injects pairwise sub-nuclear (HEP) or atomic (molecular-dynamics) physics directly into the transformer's attention logits, steering the network toward physically meaningful neighborhoods without sacrificing expressivity. We demonstrate OmniMol using the oMol dataset and find excellent performance even with relatively few examples for fine-tuning. This study lays the foundation for building interdisciplinary connections, given datasets represented as collections of point clouds. Large Hadron Collider ; Transformer ; Pairwise comparison ; Point (geometry) ; Set (abstract data type) ; Foundation (evidence) ; Particle dynamics Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Advanced Graph Neural Networks
Authors: Ibrahim Elsharkawy | V. M. Mikuni | Wahid Bhimji | Benjamin Nachman
Source: arXiv (Cornell University) (preprint, 2026-01-15)
Field: Materials Science — Materials Chemistry

OmniMol: Transferring Particle Physics Knowledge to Molecular Dynamics with Point-Edge Transformers We present OmniMol, a state-of-the-art transformer-based small molecule machine-learned interatomic potential (MLIP). OmniMol is built by adapting Omnilearned, a foundation model for particle jets found in high-energy physics (HEP) experiments such as at the Large Hadron Collider (LHC). Omnilearned is built with a Point-Edge-Transformer (PET) and pre-trained using a diverse set of one billion particle jets. It includes an interaction-matrix attention bias that injects pairwise sub-nuclear (HEP) or atomic (molecular-dynamics) physics directly into the transformer's attention logits, steering the network toward physically meaningful neighborhoods without sacrificing expressivity. We demonstrate OmniMol using the oMol dataset and find excellent performance even with relatively few examples for fine-tuning. This study lays the foundation for building interdisciplinary connections, given datasets represented as collections of point clouds. Large Hadron Collider ; Transformer ; Pairwise comparison ; Point (geometry) ; Set (abstract data type) ; Foundation (evidence) ; Particle dynamics Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Advanced Graph Neural Networks
Authors: Jordi R.-Galindo | Sebastian Oviedo-Ortiz | Antonino Arenaza-Corona | Adrian Ruiz-Martinez | Juan Sebastián Serrano-García | Simon Hernandez Ortega | Marcos Flores-Alamo | T. Keith Hollis | David Morales-Morales
Source: Dalton Transactions (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Novel non-symmetric POCOP–Ni( <scp>ii</scp> ) pincer complexes derived from 2,4-dihydroxybenzaldehyde Pincer ligands have long captivated chemists for their ability to merge rigidity, modularity, and fine-tuned electronic control into a single molecular scaffold. Within this class, POCOP-type ligands stand out for their capacity to stabilize reactive metal centers while enabling diverse catalytic and functional applications. Yet, the deliberate rupture of symmetry in these systems-particularly in non-symmetric POCOP frameworks-remains an underexplored route to unlocking new reactivity patterns. Herein, we report the synthesis of a family of non-symmetric Ni(II)-POCOP pincer complexes (2a-2c) and demonstrate that their formation is decisively governed by the presence or absence of base during the metalation step. Under basic conditions, selective formation of the aldehyde-type pincer complexes is achieved, whereas base-free conditions lead to acid-promoted ligand activation and the formation of imine-type addition complexes (3a-4c), which may partially hydrolyze during purification to yield aldehyde-type addition complexes (5a-5c). The structures and coordination modes of the isolated species were established by comprehensive spectroscopic methods and single-crystal X-ray diffraction. Subsequent optimization of the reaction conditions enabled the selective synthesis of the target non-symmetric Ni(II)-POCOP complexes in improved yield and purity, providing insight into the role of medium acidity in directing the stability and reactivity of phosphinite-based pincer systems. Pincer movement ; Merge (version control) ; Molecular electronics ; Electronic structure ; Pincer ligand Magnetism in coordination complexes ; Supramolecular Chemistry and Complexes ; Organometallic Complex Synthesis and Catalysis
Authors: Louis Lemetayer | Guillaume Le Breton | Asmae Bousfiha | Marie Cordier | Yoann Rousselin | H. Cattey | Jean‐Cyrille Hierso | Julien Roger | P. Fleurat-Lessard | Pierre‐Antoine Bouit
Source: Advanced Optical Materials (article, 2026-04-13)
Field: Chemistry — Organic Chemistry

Tuning of the Optical Properties of <i>Cis</i> ‐ and <i>Trans</i> ‐Bis‐Tetrazo[1,2‐b]indazoles Guided by Predictive DFT: An Isomer Story ABSTRACT We report the synthesis and structure‐property relationships of a new family of nitrogen‐rich polyaromatic derivatives, namely cis ‐ and trans ‐tetrazo[1,2‐ b ]indazoles. Through density functional theory (DFT)‐guided molecular engineering, substituents were introduced at selected positions on the polyaromatic scaffold via a modular strategy. The spectroscopic (absorption and emission) and redox (cyclic voltammetry) properties of these derivatives, though occasionally counterintuitive, were accurately predicted by DFT calculations and rationalized through molecular orbital interactions. Depending on the isomer ( cis / trans ), the nature of the substituents and their position, we achieved either intense luminescence in the visible range or near infrared (NIR) absorption up to 1050 nm, while maintaining an excellent electron affinity. This work highlights the versatility of the tetrazo[1,2‐ b ]indazole scaffold and its potential for precise fine‐tuning of the electronic properties of advanced functional materials. Density functional theory ; Time-dependent density functional theory ; Luminescence ; Molecular orbital ; Absorption (acoustics) ; Infrared ; Redox ; Range (aeronautics) Synthesis of Tetrazole Derivatives ; Synthesis and Properties of Aromatic Compounds ; Energetic Materials and Combustion
Authors: Jingbo Yu | T. H. Zhang | Chenhui Hu | Weike Su
Source: Organic Letters (article, 2026-01-07)
Field: Chemistry — Physical and Theoretical Chemistry

Divergent C–H Alkylation and Allylation of Indoles with Unactivated Alkene via Mechanochemical Rh(III) Catalysis A tunable mechanochemical platform is reported for Rh(III)-catalyzed C2-alkylation (via electrophilic C-H activation) or C2-allylation (via CMD) of indoles with unactivated alkenes, controlled simply by fine-tuning the reagent composition. 1,1-Disubstituted alkenes undergo Rh-free Ag-catalyzed C3-branched alkylation, enabling one-pot synthesis of <i>N</i>-free indoles via tandem coupling-deprotection. The protocol offers broad substrate scope, high yields and selectivity, scalability, and applicability to late-stage drug modification, providing a sustainable and efficient route to valuable indole derivatives. Alkene ; Catalysis ; Reagent ; Indole test ; Tandem ; Substrate (aquarium) ; Alkylation Crystallography and molecular interactions ; Catalytic C–H Functionalization Methods ; Axial and Atropisomeric Chirality Synthesis
Authors: Wentao Wei | Jiahua Rao | Bai Xue | Jiancong Xie | Dahao Xu | X.-F Sun | Ye Lu | Yuxuan Wang | Mingjun Yang | Yuedong Yang
Source: Journal of Chemical Information and Modeling (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

TripK <sub> <i>a</i> </sub> : Accurate and Scalable Acid–Base Dissociation Property Prediction via Triplet Interaction Networks and Physical Knowledge The acid-base dissociation constant (p<i>K</i><sub><i>a</i></sub>) characterizes a molecule's tendency to donate or accept protons, thereby fundamentally influencing its physicochemical properties and behavior. Consequently, p<i>K</i><sub><i>a</i></sub> is pivotal to diverse applications in drug discovery, including virtual screening and drug design. However, accurate p<i>K</i><sub><i>a</i></sub> prediction remains challenging, due to intricate atomic interactions among ionizable groups and the scarcity of experimental data. To address these, we propose TripK<sub><i>a</i></sub>, a quantum-informed aqueous p<i>K</i><sub><i>a</i></sub> predictor that employs a triplet interaction network to model complex thermodynamic coupling in multisite protonation equilibria. TripK<sub><i>a</i></sub> is fine-tuned using quantum-level descriptors as training targets, enabling the extraction of physical insights into molecular acid-base dissociation properties from limited high-quality p<i>K</i><sub><i>a</i></sub> data. Our proposed TripK<sub><i>a</i></sub> outperforms state-of-the-art methods on multiple p<i>K</i><sub><i>a</i></sub> benchmarks, including the SAMPL6-8 and Novartis data sets, achieving reductions of over 10% in both mean absolute error and root-mean-square error. Beyond p<i>K</i><sub><i>a</i></sub> prediction, TripK<sub><i>a</i></sub> further serves as a foundational model for diverse molecular properties prediction and interaction modeling tasks, benefiting from its chemically informed p<i>K</i><sub><i>a</i></sub>-pretraining. Scalability ; Virtual screening ; Dissociation (chemistry) ; Dissociation constant ; Protonation ; Molecular descriptor ; Property (philosophy) ; Physical property ; Limiting Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis