jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luca Brugnoli | Mathieu Salanne | A. Marco Saitta | Alessandra Serva | Arthur France-Lanord
Source: arXiv (Cornell University) (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

Overcoming sampling limitations using machine-learned interatomic potentials: the case of water-in-salt electrolytes Machine-learned interatomic potentials hold the promise to enable the modeling of highly concentrated liquids over meaningful timescales, far from reach for current ab initio electronic structure methods. Here we evaluate the performances of various MACE potentials in modeling a $21 m$ water-in-salt electrolyte based on lithium bis(trifluoromethanesulfonyl)imide. We test out-of-the-box foundation models, as well as both fine tuning and from scratch training strategies. Our simulations demonstrate that surrogate models allow to overcome sampling limitations of ab initio molecular dynamics, reaching an excellent agreement with experimental observables such as the structure factor. We also demonstrate the benefit of fine tuning a foundation model over training from scratch: in terms of data efficiency, but most importantly as a means to provide information regarding configurations hard to sample, such as short Li$^+$--Li$^+$ distances. Finally, we show that depending on the reference exchange-correlation functional, empirical dispersion correction schemes can be detrimental. All in all, our work shows that machine-learned interatomic potentials are a good fit for the modeling of highly concentrated electrolytes over long timescales. Observable ; Ab initio ; Electrolyte ; Sampling (signal processing) ; Work (physics) ; Dispersion (optics) ; Interatomic potential ; Foundation (evidence) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Block Copolymer Self-Assembly
Authors: Fei Wang | Xinye Zheng | Kun Li | Yanyan Wei | Yuxin Liu | Ganpeng Hu | Tong Bao | Jingwen Yang
Source: ArXiv.org (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

Multimodal Protein Language Models for Enzyme Kinetic Parameters: From Substrate Recognition to Conformational Adaptation Predicting enzyme kinetic parameters quantifies how efficiently an enzyme catalyzes a specific substrate under defined biochemical conditions. Canonical parameters such as the turnover number ($k_\text{cat}$), Michaelis constant ($K_\text{m}$), and inhibition constant ($K_\text{i}$) depend jointly on the enzyme sequence, the substrate chemistry, and the conformational adaptation of the active site during binding. Many learning pipelines simplify this process to a static compatibility problem between the enzyme and substrate, fusing their representations through shallow operations and regressing a single value. Such formulations overlook the staged nature of catalysis, which involves both substrate recognition and conformational adaptation. In this regard, we reformulate kinetic prediction as a staged multimodal conditional modeling problem and introduce the Enzyme-Reaction Bridging Adapter (ERBA), which injects cross-modal information via fine-tuning into Protein Language Models (PLMs) while preserving their biochemical priors. ERBA performs conditioning in two stages: Molecular Recognition Cross-Attention (MRCA) first injects substrate information into the enzyme representation to capture specificity; Geometry-aware Mixture-of-Experts (G-MoE) then integrates active-site structure and routes samples to pocket-specialized experts to reflect induced fit. To maintain semantic fidelity, Enzyme-Substrate Distribution Alignment (ESDA) enforces distributional consistency within the PLM manifold in a reproducing kernel Hilbert space. Experiments across three kinetic endpoints and multiple PLM backbones, ERBA delivers consistent gains and stronger out-of-distribution performance compared with sequence-only and shallow-fusion baselines, offering a biologically grounded route to scalable kinetic prediction and a foundation for adding cofactors, mutations, and time-resolved structural cues. Scalability ; Substrate (aquarium) ; Representation (politics) ; A priori and a posteriori ; Protein structure ; Enzyme ; Michaelis–Menten kinetics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Ivan Kodrin | Ivana Biljan
Source: Preprints.org (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

Porous Organic Polymers with Azo, Azoxy and Azodioxy Linkages: Design, Synthesis and CO&lt;sub&gt;2&lt;/sub&gt; Adsorption Properties Rising atmospheric CO2 levels have increased the demand for robust, scalable adsorbents for practical CO2 capture and separation. Porous organic polymers (POPs) are attractive candidates because their pore architecture and binding site properties can be precisely tuned via building blocks and linkage formation. This review summarizes experimental and computational studies of azo-linked POPs and, more broadly, nitrogen-nitrogen (N−N) linked systems, emphasizing how synthetic routes, building blocks, and framework topology govern CO2 uptake. We highlight key synthetic strategies and representative systems, including porphyrin-azo networks, and discuss the relatively sparse experimental literature on alternative N−N linked POPs incorporating azoxy and azodioxy motifs. Emphasis is placed on reversible nitroso/azodioxide chemistry as a potential pathway to ordered porous organic materials. Computational studies provide a practical route to connect structure with adsorption behavior in largely amorphous or partially ordered networks. We review hierarchical workflows combining periodic DFT and electrostatic potential properties, grand canonical Monte Carlo (GCMC) simulations and binding-energy calculations to rationalize trends and identify favorable binding environments. Computational findings demonstrate that pore accessibility and stacking models can strongly influence predicted CO2 adsorption. This review provides guidelines for designing POPs with enhanced CO2 adsorption, offering an outlook and discussing challenges for future studies. Stacking ; Adsorption ; Polymer ; Porous medium ; Porosity ; Monte Carlo method ; Scalability ; Workflow Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Nila Mandal | James Maniscalco | Mark Aindow | Qian Yang
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Siamese graph neural networks for melting temperature prediction of molten salt eutectics A Siamese GNN predicts eutectic melting temperatures from molecular structure without engineered features, improves with data augmentation from single-component training data, and enables transfer from inorganic molten salts to organic eutectics. Eutectic system ; Artificial neural network ; Graph ; Melting temperature ; Temperature measurement Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Benjamin X. Shi | Timothy C. Berkelbach
Source: ArXiv.org (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Practical and improved density functionals for computational catalysis on metal surfaces Density functional theory (DFT) has been critical towards our current atomistic understanding of catalysis on transition-metal surfaces. It has opened new paradigms in rational catalyst design, predicting fundamental properties, like the adsorption energy and reaction barriers, linked to catalytic performance. However, such applications depend sensitively on the predictive accuracy of DFT and achieving experimental-level reliability, so-called transition-metal chemical accuracy (13 kJ/mol), remains challenging for present density functional approximations (DFAs) or even methods beyond DFT. We introduce a new framework for designing DFAs tailored towards studying molecules adsorbed on transition-metal surfaces, building upon recent developments in non-self-consistent DFAs. We propose two functionals within this framework, demonstrating that transition-metal chemical accuracy can be achieved across a diverse set of 39 adsorption reactions while delivering consistent performance for 17 barrier heights. Moreover, we show that these non-self-consistent DFAs address qualitative failures that challenge current self-consistent DFAs, such as CO adsorption on Pt(111) and graphene on Ni(111). The resulting functionals are computationally practical and compatible with existing DFT codes, with scripts and workflows provided to use them. Looking ahead, this framework establishes a path toward developing more accurate and sophisticated DFAs for computational heterogeneous catalysis and beyond. Density functional theory ; Workflow ; Scripting language ; Catalysis ; Set (abstract data type) ; Adsorption ; Heterogeneous catalysis Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Electrocatalysts for Energy Conversion
Authors: Xiang Li | Ping Wei | Kunhao Chen | Wantian Zhang | Dongrui Liu | Kefan Cai | Beiquan Jia | Didi Huo | Wenyu Zhao | Li‐Dong Zhao | Yao Lu
Source: Advanced Materials (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

Interface‐Engineered High‐Performance Flexible Thermoelectric Films for Self‐Powered Health Monitoring Harvesting low-grade discrete heat through flexible thermoelectrics (TEs) offers a transformative route toward self-powered wearable electronics, yet is hindered by the inherent trade-off among electrical/thermal transport and flexibility, as well as lack of application-driven co-design between materials and devices. Herein, we counterintuitively incorporate an insulating polymer-polyvinylpyrrolidone (PVP) into the flexible Ag<sub>2</sub>Se-based matrix, leveraging its multifunctional interfacial effects to achieve carrier-phonon decoupling. This yields a flexible TE film with record-high power factor of 3328 ± 332 µW m<sup>-1</sup> K<sup>-2</sup> and a figure of merit (ZT) of 1.1 at 341 K. The high-performance stems from the rational incorporation of PVP as a dual-functional additive, which simultaneously promotes coherent grain boundaries and mitigates Fermi-level pinning effect. The assembled flexible TE generator delivers a normalized power density of 81 W m<sup>-2</sup> under a temperature gradient of 35 K. Moreover, a proof-of-concept TE-cup that integrates physiological sensing and energy harvesting is demonstrated, which achieves 100%-accurate user identification via thermal-sensing signals and powers a physiological monitor using harvested energy (∼20 mV) through an ultra-low-power management circuit. Our work redefines the positive role of non-conductive polymers in TE nanocomposites, establishes an effective strategy for structure-property manipulation, and pioneers a self-sustained platform for next-generation healthcare monitoring. Wearable computer ; Thermoelectric effect ; Energy harvesting ; Work (physics) ; Figure of merit ; Power (physics) ; Thermoelectric materials ; Thermoelectric generator ; Wearable technology ; Cadmium telluride photovoltaics Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Nanomaterials and Printing Technologies
Authors: Ouafa Belkacem | Lokmane Abdelouahed | Kamel Aizi | Maamar Laidi | Abdelhafid Touil | Salah Hanini
Source: Processes (article, 2026-03-12)
Field: Materials Science — Biomaterials

Sustainable Design of Phosphonate Anti-Scale Additives for Oilfield Flow Assurance via 2D-QSAR-KNN and Global Inverse-QSAR Descriptor Profiling Mineral scale deposition remains a major flow-assurance constraint in oil and gas operations, especially in water-flooding and produced-water reinjection, where mixing between incompatible brines promotes super-saturation and precipitation of poorly soluble salts. This work introduces a novel extension of traditional methods used for modeling chemical inhibition and the predictive evaluation of oilfield scale-inhibitor molecules. A systematically optimized Two-Dimensional Quantitative Structure–Activity Relationship Model based on the k-Nearest Neighbors algorithm 2D-QSAR-KNN model was developed to quantitatively link molecular constitution of phosphonate inhibitors, brine chemistry, and operating factors with inhibition efficiency IE %. The optimized model achieved strong accuracy and generalization R2train = 0.9182, R2test = 0.9306, and R2global = 0.9208 with low prediction errors RMSEtrain = 4.7888%, RMSEtest = 4.5485%, and RMSEglobal = 4.7421%. Median absolute errors remained minimal for the train set = 0.80%, and test set = 1.63%, and model stability was confirmed by high correlation with experimental IE % r = 0.94 and R2train/R2test ≈ 0.99, showing no sign of overfitting. Additionally, an inverse-2D-QSAR framework was applied to identify the optimal molecular descriptor profile expected to maximize inhibitory performance within normalized bounds, providing rational rules for next-generation inhibitor design. The findings highlight the practical value of QSAR-inspired AI modeling to accelerate molecule screening and dosage exploration prior to laboratory validation, supporting more cost-effective, interpretable, and environmentally aware sulfate-scale inhibition strategies under high-salinity reservoir conditions. Flow assurance ; Profiling (computer programming) ; Compatibility (geochemistry) ; Test set ; Phosphonate ; Generalization ; Brine Calcium Carbonate Crystallization and Inhibition ; Hydraulic Fracturing and Reservoir Analysis ; Microbial Applications in Construction Materials
Authors: THOMAS LYNCH | Cibrán Rico Lopez | Claudio Cazorla | Keith P. McKenna
Source: The Journal of Physical Chemistry Letters (article, 2026-03-04)
Field: Materials Science — Materials Chemistry

Structural and Electronic Reconstruction of Extended Defects in Pnictogen Chalcohalides With growing global demand for renewable energy, thin-film photovoltaic technologies are emerging as a promising route to low-cost, scalable solar power. However, for many candidate materials extended defects in polycrystalline thin films are associated with deep gap states that limit carrier lifetimes and reduce device efficiency. Pnictogen chalcohalide semiconductors with the general formula MChX (M = pnictogen, Ch = chalcogen, X = halogen) have been proposed as defect-tolerant alternatives. Using density functional theory, we predict the structure and electronic properties of surface defects for eight pnictogen chalcohalide compounds and analyze their behavior upon surface reconstruction. Our results reveal that, despite the cleavage of covalent bonds, these materials undergo reconstructions that eliminate detrimental gap states. The facile formation of new interchain bonds at the surface preserves the electronic performance of the materials and suggests intrinsic resilience to extended defects. These findings position pnictogen chalcohalides as promising candidates for defect-tolerant, stable, thin-film photovoltaic absorbers. Pnictogen ; Key (lock) ; Trajectory ; Object (grammar) Solid-state spectroscopy and crystallography ; Inorganic Chemistry and Materials ; Advancements in Solid Oxide Fuel Cells
Authors: Haeshini Jegan¹ | Enpei Yao¹ | Alexandros Filippas² | Juraj Liska³ | Nian Liu²
Source: ChemRxiv (article, 2026-02-11)
Field: Chemistry — Analytical Chemistry

Automated EIS Data Acquisition and Analysis for Monitoring Degradation of Aluminum Beverage Can Lids Electrochemical Impedance Spectroscopy (EIS) is a powerful, non-destructive tool for probing corrosion phenomena in metallic systems.In this work, we present an automated platform for highthroughput acquisition and analysis of EIS data to characterize corrosion in aluminum beverage can lids.Using a lowcurrent potentiostat in combination with a multiplexer and custom-fabricated electrochemical reactors, EIS measurements were performed with the aluminum lid as the working electrode and a graphite rod as the counter electrode, directly immersed in commercial beverage solutions.To extract meaningful corrosion parameters, we applied 2D fitting techniques to the impedance spectra using a hybrid optimization strategy: global exploration via Particle Swarm Optimization (PSO), followed by local refinement using gradientbased techniques.The automated workflow facilitates rapid, consistent measurement and analysis, providing detailed insight into coating breakdown and interfacial degradation.This framework significantly reduces manual effort and subjectivity in EIS interpretation and offers a scalable approach for corrosion testing in packaging applications. Data acquisition ; Degradation (telecommunications) ; Aluminium Spectroscopy and Chemometric Analyses ; Soil Geostatistics and Mapping ; Sugarcane Cultivation and Processing
Authors: Youssef Basem | Alamer Ata | Fayek Sabry | Maria Tamer | Elaria Raaft | Rehab Abdelmonem
Source: Polymers (article, 2026-03-30)
Field: Materials Science — Biomaterials

Sustainable Green Polymer Production for Pharmaceutical Manufacturing: A Review of Environmental and Economic Impacts Polymers are fundamental components of modern pharmaceutical manufacturing, serving critical roles as excipients, binders, coatings, and matrices for controlled drug delivery systems. However, the conventional production of pharmaceutical polymers relies heavily on petrochemical feedstocks, energy-intensive processes, and hazardous solvents, leading to significant environmental and economic burdens. In recent years, increasing regulatory pressure, environmental awareness, and sustainability goals have driven the pharmaceutical industry toward greener manufacturing strategies. This review critically examines sustainable green polymer production for pharmaceutical applications, with a focus on both environmental and economic impacts. The review discusses the role of polymers in pharmaceutical manufacturing, outlines the limitations of conventional polymer synthesis, and highlights the relevance of green chemistry principles in addressing these challenges. Key green polymer synthesis techniques, including biopolymer production, enzymatic polymerization, microwave-assisted synthesis, supercritical CO2 processing, and the use of ionic liquids and deep eutectic solvents, are systematically evaluated. Additionally, life-cycle assessment (LCA) approaches are explored to assess the environmental performance of green polymer processes in comparison with traditional methods. Beyond environmental sustainability, this review emphasizes the importance of pharmacoeconomic evaluation in determining the feasibility of adopting green polymers at an industrial scale. Cost–benefit analyses, manufacturing cost comparisons, long-term economic advantages, and health–economic outcomes are discussed in the context of pharmaceutical supply chains. Regulatory perspectives, industrial implementation challenges, and future directions are also addressed. Overall, this review highlights sustainable polymer innovation as a critical pathway toward environmentally responsible, economically viable, and future-ready pharmaceutical manufacturing. Sustainability ; Context (archaeology) ; Environmental impact assessment ; Pharmaceutical manufacturing ; Pharmaceutical industry ; Production (economics) ; Petrochemical ; Sustainable development biodegradable polymer synthesis and properties ; Chemistry and Chemical Engineering ; Enzyme Catalysis and Immobilization
Authors: Ingrid Suzana | Michel Giorgi | Anthony Kermagoret | Alexandre Martinez | David Bardelang
Source: ChemRxiv (article, 2026-03-18)
Field: Chemistry — Inorganic Chemistry

Molecular Adsorbing Host•Guest (MAHG) Crystals: A New Paradigm for Porous Materials? Since the discovery of hostguest complexes, we have assisted to huge developments of this part of chemistry, liquid state studies brightly leading the way.In this context, numerous macrocycles and cages have shown exceptional relevance for selective and controllable guest capture and release mainly based on 1:1 hostguest complexes.With such attributes, applications pertaining to sequestration, separation, catalysis, template synthesis and more generally to biology and materials science have flourished over time.Comparatively, the solid-state chemistry of hostguest complexes is less developed, especially that involving macrocyclic hosts.In this context, we recently reported the crystal structure of what could be the first of a series of Molecular Adsorbing HostGuest (MAHG) crystals featured by several new properties.Whereas a rapidly growing field today exists about porous Hydrogen-bonded Organic Frameworks (HOFs), some composed of macrocyclic "hosts only", an unchartered territory appears within reach for (macrocyclic) "hostguest complexes".Given the huge diversity of hostguest complexes described in the liquid state, rapid developments of MAHG type materials are expected in the near future. Porosity ; Solid-state ; Porous medium ; Adsorption ; Crystal engineering Metal-Organic Frameworks: Synthesis and Applications ; Supramolecular Chemistry and Complexes ; Surface Chemistry and Catalysis
Authors: Brenden W. Hamilton | Travis E. Jones | Timothy C. Germann | Benjamin Nebgen
Source: ArXiv.org (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforward and {\it ab initio} approaches are not computationally feasible for many properties such as melting and low temperature diffusion. Therefore, we develop a machine-learned interatomic potential for cerium hydride that is valid for H to Ce ratios from 2.0 to 3.0. A query-by-committee active learning approach is used to develop the training set. Leveraging classical molecular dynamics simulations, we assess a range of properties and provide fundamental mechanisms for the trends with stoichiometry. A majority of the properties follow the trend of lattice contraction, being governed by the stronger lattice binding induced by adding octahedral atoms. Stoichiometry ; Cerium ; Interatomic potential ; Lattice (music) ; Octahedron ; Hydride ; Hydrogen Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Rare-earth and actinide compounds