jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Majdi Argoubi | Khaled Mili
Source: Frontiers in Sustainability (article, 2026-01-15)
Field: Chemistry — Organic Chemistry

Multi-objective optimization framework for sustainable olive oil supply chains: integrating environmental compliance with economic performance Introduction Mediterranean olive oil production faces a critical sustainability challenge: managing olive mill wastewater (OMW), a highly polluting byproduct (0.8–1.2 m 3 /ton) threatening environmental compliance and supply chain viability. Methods This study develops an integrated multi-objective Mixed-Integer Linear Programming framework that jointly optimizes harvest scheduling, processing allocation, and OMW management. The ε-constraint method generates Pareto frontiers revealing trade–offs between environmental performance, oil quality, and economic profit. Implementation used Python 3.9+ with Gurobi optimizer, validated through a Tunisian case study (18 groves, 4 teams, 3 mills, 14-day horizon). Results Operational optimization achieved 14.6% CO 2 emission reduction and maintained regulatory compliance. However, OMW valorization remained at 12.8%-far below 80% policy targets-revealing fundamental infrastructure-regulation disconnect. Multi-objective analysis shows balanced solutions achieve 80–85% of maximum performance across all dimensions, while single-objective approaches sacrifice 35–65% in non-prioritized objectives. Sensitivity analysis quantifies that doubling valorization capacity increases recovery to 23.6% with 30.8% profit improvement. Discussion The framework demonstrates that environmental constraints, when architecturally embedded, define opportunity spaces for competitive advantage rather than performance limitations. Infrastructure analysis provides actionable guidance for phased regulatory implementation with 3–4 season payback periods. Sustainability ; Supply chain ; Profit (economics) ; Linear programming ; Olive oil ; Exploit ; Mill ; Multi-objective optimization ; Environmental impact assessment Edible Oils Quality and Analysis ; Agriculture Sustainability and Environmental Impact ; Food Supply Chain Traceability
Authors: Thiago Reschützegger | Sarp Aykent | Gabriel Jacob Perin | Bruno Henrique Nunes | Flaviu Cipcigan | R. Ferreira | Mathias Steiner | Fabian L. Thiemann
Source: ArXiv.org (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Equivariant Interatomic Potentials without Tensor Products Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical spaces at a fraction of the cost of quantum-mechanical calculations. However, the most accurate equivariant architectures rely on Clebsch-Gordan tensor products whose computational cost scales steeply with angular resolution, creating a trade-off between model expressiveness and inference speed that ultimately limits practical applications. Here we introduce Geodite, an equivariant message-passing architecture that replaces tensor products while incorporating physical priors to ensure smooth, well-behaved potential energy surfaces. Trained on the Materials Project trajectories dataset of inorganic crystals, Geodite-MP achieves accuracy competitive with leading methods on benchmarks for materials stability prediction, thermal conductivity, phonon-derived properties, and nanosecond-scale molecular dynamics, while running $3\text{--}5\times$ faster than models performing similarly. By combining predictive accuracy, computational efficiency, and physicality, Geodite enables faster large-scale atomistic simulations and high-throughput screening that would otherwise be computationally prohibitive. Tensor (intrinsic definition) ; Equivariant map ; Inference ; Stability (learning theory) ; Prior probability ; Thermal ; Interatomic potential ; Tensor product Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Thiago Reschützegger | Sarp Aykent | Gabriel Jacob Perin | Bruno Henrique Nunes | Flaviu Cipcigan | R. Ferreira | Mathias Steiner | Fabian L. Thiemann
Source: arXiv (Cornell University) (preprint, 2026-01-21)
Field: Materials Science — Materials Chemistry

Equivariant Interatomic Potentials without Tensor Products Foundational machine-learned interatomic potentials have emerged as powerful tools for atomistic simulations, promising near first-principles accuracy across diverse chemical spaces at a fraction of the cost of quantum-mechanical calculations. However, the most accurate equivariant architectures rely on Clebsch-Gordan tensor products whose computational cost scales steeply with angular resolution, creating a trade-off between model expressiveness and inference speed that ultimately limits practical applications. Here we introduce Geodite, an equivariant message-passing architecture that replaces tensor products while incorporating physical priors to ensure smooth, well-behaved potential energy surfaces. Trained on the Materials Project trajectories dataset of inorganic crystals, Geodite-MP achieves accuracy competitive with leading methods on benchmarks for materials stability prediction, thermal conductivity, phonon-derived properties, and nanosecond-scale molecular dynamics, while running $3\text{--}5\times$ faster than models performing similarly. By combining predictive accuracy, computational efficiency, and physicality, Geodite enables faster large-scale atomistic simulations and high-throughput screening that would otherwise be computationally prohibitive. Tensor (intrinsic definition) ; Equivariant map ; Inference ; Stability (learning theory) ; Prior probability ; Thermal ; Interatomic potential ; Tensor product Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Caterina Momoli | Laura Palombi | Isabella Daidone | Erica Scarel | Massimiliano Aschi
Source: Chirality (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Resolution of a Chiral β‐Aminoketone via Diastereomeric Salt Formation: From Experimental Evidence to Molecular‐Level Insights Into Solution‐Phase Clusters The classical diastereomeric salt resolution approach was employed to separate (±)-1,3-diphenyl-3-(phenylamino)propan-1-one using both enantiomers of 10-camphorsulfonic acid (CSA) as resolving agents. Gentle stirring at room temperature resulted in the stereoselective precipitation of a single diastereomeric salt, yielding a solid phase highly enriched in one enantiomer of the target compound. Control experiments confirmed the crucial role of the chiral counterion in directing the selectivity of the process. Molecular Dynamics simulations and subsequent Principal Component Analysis revealed slight but significant differences in the pre-nucleation size distribution of ionic clusters and in the dynamics of their mutual interconversion, hence suggesting that these differences could play a role in the racemic resolution. Diastereomer ; Enantiomer ; Resolution (logic) ; Stereoselectivity ; Selectivity ; Salt (chemistry) ; Counterion ; Phase (matter) Crystallization and Solubility Studies ; Crystallography and molecular interactions ; Ionic liquids properties and applications
Authors: Zohreh Fallah | Sami Malola | María Francisca Matus | Hannu Häkkinen
Source: ACS Nano (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Chiral Ligand-Protected Gold Nanoclusters as Biosensors for Small Chiral Biomolecules: A Computational Study Detection of chiral biomolecules in biological environments presents an important challenge: to develop sensitive, noninvasive sensors that Could have an impact in several areas such as drug discovery, diagnostics of diseases, and care. In this work, we introduce a strategy for experimentally realizable, noninvasive sensing of small chiral biomolecules in aqueous solvents, validated via classical force-field molecular dynamics simulations and density functional theory calculations. We investigated the interactions of the L/D forms of glutathione and seven chiral amino acids (Ala, Arg, Asp, Cys, Glu, Ser, Tyr) with six chiral, water-soluble, thiolate-protected gold nanoclusters in the range of 25-144 gold atoms, via dynamical sampling extending up to 3 μs time scales in water at neutral pH. We found surprisingly large variations in the binding probability (from <1% to 100%) of these analytes to the nanoclusters, with the dominating interaction being electrostatics between the analyte and the nanoclusters' ligand surface, augmented by hydrogen bonding and van der Waals interactions. Computed circular dichroism spectra for several nanocluster-analyte complexes predict the identification of analyte-specific adsorption events and even the resolution of the adsorbed enantiomer in selected cases, constituting an experimentally realizable sensing function. Our results suggest that chiral ligand-protected gold nanoclusters could be used for noninvasive chiral sensing, creating a tunable toolbox where the nanocluster size and chiral ligand type could be varied for optimizing the sensing activity for specific targets. Nanoclusters ; Biomolecule ; van der Waals force ; Analyte ; Biosensor ; Enantiomer ; Ligand (biochemistry) ; Hydrogen bond ; Circular dichroism Nanocluster Synthesis and Applications ; Gold and Silver Nanoparticles Synthesis and Applications ; Molecular spectroscopy and chirality
Authors: Daoxin Luo | Chunyan Jia | Yuchao Lin | Jin Zhou | Congrui Ren | Xixian Xie | Tong Chen | Zhiping Zeng | Weifeng Li | Yuguang Mu | Changliang Ren
Source: Angewandte Chemie International Edition (article, 2026-01-28)
Field: Chemistry — Organic Chemistry

Proton‐Activated Artificial Channels for pH‐Selective Cancer Therapy Proton-activated ion channels mediate ion transport in response to extracellular acidification, enabling cellular adaptation to acidic microenvironments. Despite their biological importance, mimicking proton-activated functionality in artificial ion channels remains a significant challenge. Here, we present a novel class of proton-activated artificial ion channels built from self-assembled peptide chains integrated into a pH-responsive 2,2'-bipyridine scaffold. Protonation induces a conformational switch in the channel-forming units, promoting one-dimensional self-assembly and subsequent hydrophobic packing into functional channels capable of transporting small molecules. As extracellular pH decreases from 7.4 to 6.5, C-FF exhibits a 10.3-fold enhancement in cytotoxicity against human colorectal carcinoma cells, boosting an IC<sub>50</sub> of 2.8 µM, mediated through apoptosis induction and cell cycle arrest resulting from disruption of the autophagic process. Significantly, C-FF demonstrates exceptional selectivity for cancer cells, achieving a selectivity index of 8.5, surpassing that of doxorubicin by one order of magnitude while maintaining comparable potency, highlighting its potential as a pH-responsive platform for selective anticancer therapy in acidic tumor microenvironments. Extracellular ; Doxorubicin ; Ion channel ; Cancer therapy ; Cytotoxicity ; Selectivity ; Peptide ; Cancer cell Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials ; Molecular Sensors and Ion Detection
Authors: Panagiota Karampa | Konstantinos Makryniotis | Theofani-Iosifina Sousani | Evangelos Topakas | Vangelis Daskalakis | Maria Dimarogona
Source: bioRxiv (Cold Spring Harbor Laboratory) (article, 2026-01-06)
Field: Materials Science — Biomaterials

Structural investigation of an engineered feruloyl esterase with improved MHET degrading properties Abstract MHETases are enzymes implicated in polyethylene terephthalate (PET) biodegradation. The present study elucidates the structural determinants that result in increased mono(2-hydroxyethyl) terephthalate (MHET) degradation by a feruloyl esterase, which has been engineered to resemble MHETase active site. The crystal structures of the variant in apo and benzoic acid bound state reveal the changes induced by the introduced mutation, specifically the formation of a hydrogen bond and a trans to cis isomerization of a peptide bond in the vicinity of the catalytic site. Molecular dynamics simulations demonstrate the stabilization of the loop harboring the engineered residue, as well as an expansion of the substrate binding cleft, which would facilitate accommodation of a broader variety of substrates, indicative of a promiscuous biocatalyst. Enzyme Enzyme Commission Number: EC 3.1.1.73; Uniprot accession code: A0A1D3S5H0_FUSOX Substrate (aquarium) ; Enzyme ; Hydrogen bond ; Isomerization ; Benzoic acid ; Esterase ; Catalysis ; Molecular dynamics ; Homolysis biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Enzyme Production and Characterization
Authors: Rafaela Eliasquevici | Kalil Bernardino
Source: ChemRxiv (article, 2026-03-02)
Field: Chemistry — Organic Chemistry

Stability of SDS Micelles in Reline / Water Mixtures: a Molecular Dynamics Study Deep eutectic solvents of type III, like reline, a mixture of choline chloride and urea, are a relatively new class of green solvent that displays physical properties intermediate between conventional salt solutions and ionic liquids. Well-established models used to explain colloidal stability and macromolecules conformation in usual solvents, including the hydrophobic effect and the formation of the electrical double layer, are not applicable to these solvents and their mixtures with water due to the strong non-ideal behavior and the high ionic strength of those solvents. Hence, in this work, molecular dynamics simulations were performed to evaluate the thermodynamic stability of sodium dodecyl sulfate (SDS) micelles in reline/water mixtures with different compositions by potential of mean force (PMF) calculations and splitting the contributions from the surfactant ionic head and the hydrophobic tail. The presence of reline reduces the favorable contribution of the surfactant alkyl tail to aggregation, indicating a reduction of the hydrophobic effect, which is explained by a smaller amount of water molecules in contact with the surfactant. However, the high adsorption of choline cations over the micelle surface also reduces the repulsion between charged heads. The second effect dominates over the reduction of the hydrophobic effect, resulting in more negative aggregation free energy and improved micelle stability in the presence of reline. The electrostatic potential from the anionic micelle was also computed and does not follow the usual double layer profile, presenting oscillations away from the micelle surface in mixtures with high reline concentration and with the choline cations almost completely replacing the sodium counterions from the micelle surface. Micelle ; Pulmonary surfactant ; Molecular dynamics ; Solvation ; Counterion ; Hydrophobic effect ; Sodium dodecyl sulfate ; Colloid ; Solvent ; Ionic strength Surfactants and Colloidal Systems ; Ionic liquids properties and applications ; Electrostatics and Colloid Interactions
Authors: Sirine Nouri | Akseli Niemelä | Ricky Nencini | Georgios Kolypetris | Tuomas Niemi-Aro | Salla Virtanen | O. H. Samuli Ollila | Artturi Koivuniemi
Source: Communications Chemistry (article, 2026-04-09)
Field: Materials Science — Biomaterials

Exploring the structure and dynamics of peptide nanodiscs through a synergistic approach with NMR spectroscopy, SAS and MD simulations Peptide nanodiscs are promising anti-atherosclerosis therapeutics, drug delivery particles and structural biology tools. However, the lack of experimental methods for structural and dynamical characterization of these particles hinders their further development. Here, we integrated nuclear magnetic resonance (NMR), small-angle x-ray scattering, and small-angle neutron scattering experiments with molecular dynamics (MD) simulations to investigate the structure and dynamics of peptide nanodiscs stabilized by the apolipoprotein A-I mimetic peptide 22 A with therapeutic activity against atherosclerosis. This multi-technique approach takes advantage of combining average size and shape information from small-angle scattering, peptide site-specific information from NMR spectroscopy, and interpretative power of MD simulations. Our results reveal the intrinsic polydispersity in the size of peptide nanodiscs. Our consensus model suggests that 22 A peptides are predominantly in α-helical configuration with a disordered inter-helical orientation around the lipid matrix. The terminal regions of the peptides display greater flexibility relative to the peptide core and an enhanced C-terminal exposure to solvent, which could facilitate interaction with the enzyme LCAT. The methodological approach described in this paper paves the way for the design of more stable and effective therapeutic nanodiscs and for the characterization of other biomolecular aggregates that are beyond the scope of current structural biology techniques. Peptide ; Molecular dynamics ; Neutron scattering ; Structural biology ; Rational design ; Dynamics (music) ; Characterization (materials science) ; Nuclear magnetic resonance spectroscopy ; Flexibility (engineering) Supramolecular Self-Assembly in Materials ; Lipid Membrane Structure and Behavior ; Dendrimers and Hyperbranched Polymers
Authors: Fumihiko Aiga | Hayato Goto
Source: ArXiv.org (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Quantum chemistry based on classical mechanics inspired by simulated bifurcation Accurate quantum chemical calculations are critical for understanding molecular properties, yet their computational cost remains a major challenge. Full Configuration Interaction (FCI) provides exact solutions but is prohibitively expensive for large systems. To address this, quantum computers are expected to be useful, but developing practical quantum computers is still ongoing. Here we introduce an efficient Configuration Interaction (CI) computation algorithm based on classical mechanics, which we call Simulated Bifurcation-based CI (SBCI), because we derive this algorithm from a quantum inspired algorithm for combinatorial optimization called Simulated Bifurcation. Applying it to FCI computations of representative molecular systems and comparing the results with those by a standard method, we demonstrate that SBCI can reduce computation costs such as computation times and/or required memory sizes, while keeping high accuracy comparable to the standard method. Thus, SBCI will be promising for accelerating high-precision electronic structure calculations without compromising reliability. Computation ; Quantum computer ; Quantum ; Quantum algorithm ; Quantum chemistry ; Bifurcation ; Electronic structure Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Protein Structure and Dynamics
Authors: D. A. Tumakov | Daria Popova-Gorelova
Source: ArXiv.org (article, 2026-03-06)
Field: Materials Science — Materials Chemistry

Real-time exciton dynamics in two-dimensional materials under ultrashort laser pulses The optical response of two-dimensional materials is often significantly impacted by excitonic effects due to the reduced screening of attractive Coulomb interactions in low-dimensional systems. Accurate modeling of exciton formation and real-time dynamics is essential to understanding their ultrafast optical properties. In this study, we theoretically investigate the exciton dynamics in a two-dimensional hexagonal boron nitride (h-BN) and a germanium sulfide (GeS) monolayers exposed to an ultrashort laser pulse. We analyze the system's response to the external field in one- and two-photon excitation regimes. For our calculations, we combine a state-of-the-art ab initio approach to study exciton dynamics with a highly precise numerical scheme. We incorporate electron-hole interactions through a non-local self-energy operator derived from the many-body perturbation theory (MBPT) within the time-dependent adiabatic $GW$ (TD-a$GW$) approximation. We implement this approach using the full-electron LAPW+lo method in the all-electron exciting package. Our results elucidate the role of many-body effects in shaping ultrafast excitonic processes in two-dimensional materials, contributing to the fundamental understanding necessary for optoelectronic and photonic applications. Exciton ; Ultrashort pulse ; Adiabatic process ; Excitation ; Perturbation theory (quantum mechanics) ; Biexciton ; Laser 2D Materials and Applications ; Strong Light-Matter Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Mukesh Kumar Sharma
Source: ArXiv.org (article, 2026-03-08)
Field: Materials Science — Materials Chemistry

ETHER: An Efficient Tool for Monte Carlo Simulations of Magnetic Systems Monte Carlo (MC) simulations are powerful computational tools for investigating thermodynamic behavior and validating analytical approaches in complex physical systems. Here we present ETHER (Efficient Tool for THermodynamics Exploration via Relaxations), an open-source MC simulation package, developed for studying temperature-dependent magnetic properties in spin systems. ETHER enables large-scale MC simulations of various spin systems to analyze phase transitions, critical behavior, and complex magnetic structures. The package constructs spin-lattice networks from standard structural input files and supports exchange interaction definitions through user-specified neighbor lists. It also provides tools for easy visualization and post-processing of simulation outputs. Our code has been benchmarked thoroughly against results reported in the literature for common representative magnetic systems. This user-friendly code offers researchers a versatile platform for exploring thermodynamic properties of complex magnetic systems. Monte Carlo method ; Visualization ; Complex system ; Spin (aerodynamics) ; Code (set theory) ; Monte Carlo molecular modeling Machine Learning in Materials Science ; Theoretical and Computational Physics ; Magnetism in coordination complexes
Authors: T. M. Weigand
Source: The Journal of Open Source Software (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

PMMoTo: A Porous Media Morphology and Topology Toolkit The Porous Media Morphology and Topology Toolkit (PMMoTo) is a software tool designed to help researchers analyze and understand porous structures and how their features influence behavior at larger scales. A porous medium is any solid material that contains pores, for example, soil, membranes, skin, and many other natural and engineered materials. In fact, porosity is often a function of spatial scale; all materials have some degree of porosity, you just need to zoom in. The morphology (i.e. the shape and structure) and topology (i.e., the connectivity and spatial relations) of a porous structure govern larger-scale behavior. By characterizing these structures, PMMoTo enables researchers to better understand emergent behaviors and develop more predictive models of porous systems. Porous medium ; Topology (electrical circuits) ; Morphology (biology) ; Network topology ; Mathematical morphology Anodic Oxide Films and Nanostructures ; Pickering emulsions and particle stabilization ; Block Copolymer Self-Assembly