jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ignacio Pickering | Jiangeng Xue | Kate Huddleston | Nicholas Terrel | Adrián E. Roitberg
Source: ChemRxiv (preprint, 2025-08-07)
Field: Materials Science — Materials Chemistry

TorchANI 2.0: An extensible, high performance library for the design, training, and use of NN-IPs In this work we introduce TorchANI 2.0, a significantly improved version of the free and open source TorchANI software package for training and evaluation of ANI (ANAKIN-ME) deep learning models. TorchANI 2.0 builds upon the foundation of its predecessor, while addressing its limitations and introducing new features. These changes greatly enhance its extensibility, performance, and suitability as a framework for developing models ready for molecular dynamics applications. These improvements include the introduction of a modular system to add arbitrary pairwise potentials to models, CUDA-accelerated optimization for faster and more memory-efficient calculation of local atomic features, and a batched system for better performance of network ensembles, among others. Our benchmarks demonstrate that TorchANI 2.0 achieves significant speedup over previous versions in both training and inference, and the library enhancements allow users to train physically constrained models that better represent important qualities of chemical systems. We demonstrate this by introducing three new ANI models that incorporate these features and evaluating their capabilities Computer science ; Modular design ; Speedup ; Extensibility ; Pairwise comparison ; Inference ; Computer architecture ; Granularity ; Software ; Supercomputer ; Artificial intelligence ; Machine learning ; Software engineering ; Parallel computing ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Fuel Cells and Related Materials
Authors: Huihui Liu | Bingjie Zhu | Shuyang Nie | Haoran Li | Yugang Lin | Tianyi Ma | Xin Shao | Chen Qian | Minjie Shen | Yanrong Zheng | Xiaohui Fan | Jie Liao
Source: Briefings in Bioinformatics (article, 2025-08-31)
Field: Materials Science — Materials Chemistry

Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are critical determinants of the pharmacokinetic and safety profiles of drug candidates. Accurate and early-stage prediction of ADMET characteristics is essential for reducing late-stage attrition rates, lowering development costs, and accelerating the drug discovery process. Recent advances in deep learning have shown great promise in molecular property prediction, especially with the emergence of Transformer-based architectures that can effectively model long-range dependencies in molecular representations. However, most existing methods rely heavily on atom-level encodings (e.g. smiles or molecular graphs), which often lack structural interpretability and generalization across heterogeneous tasks. Previously, we developed a de novo and flexible molecular representation framework named MSformer (available at https://github.com/ZJUFanLab/MSformer), which demonstrated success in bioactivity prediction. We have now adapted and specialized this architecture for ADMET property prediction. This adapted implementation, designated as MSformer-ADMET, extends the framework's capabilities to pharmacokinetic and toxicity endpoints while maintaining its flexible, fragmentation-based approach to molecular representation learning. MSformer-ADMET is fine-tuned on 22 tasks collected from the Therapeutics Data Commons (TDC), covering both classification and regression settings. Results demonstrate that MSformer-ADMET achieves superior performance across a wide range of ADMET endpoints, consistently outperforming conventional smiles-based and graph-based models. Notably, we further conducted interpretability analyses by leveraging the model's attention distributions and fragment-to-atom mappings, allowing the identification of key structural fragments that are highly associated with molecular properties. This post hoc interpretability provides more transparent insights into the structure-property relationship. Collectively, results demonstrate that MSformer-ADMET is a highly effective and broadly applicable model for ADMET prediction. Interpretability ; Identification (biology) ; Representation (politics) ; Generalization ; Property (philosophy) ; Drug discovery ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Taruna Likhariya | Pragnesh N. Dave
Source: Next research. (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Prediction of activation energy and TG curves of AP using artificial neural networks Artificial neural network ; Biological system ; Activation energy ; Materials science ; Computer science ; Artificial intelligence ; Chemistry ; Biology ; Physical chemistry Thermal and Kinetic Analysis ; Advanced Materials and Semiconductor Technologies ; Manufacturing Process and Optimization
Authors: Nawaf Alampara | Anagha Aneesh | Martiño Ríos-García | Adrian Mirza | Mara Schilling-Wilhelmi | Ali Asghar Aghajani | Meiling Sun | Gordan Prastalo | Kevin Maik Jablonka
Source: ArXiv.org (preprint, 2025-07-10)
Field: Materials Science — Materials Chemistry

General-Purpose Models for the Chemical Sciences: LLMs and Beyond Data-driven techniques have a large potential to transform and accelerate the chemical sciences. However, chemical sciences also pose the unique challenge of very diverse, small, fuzzy datasets that are difficult to leverage in conventional machine learning approaches. A new class of models, which can be summarized under the term general-purpose models (GPMs) such as large language models, has shown the ability to solve tasks they have not been directly trained on, and to flexibly operate with low amounts of data in different formats. In this review, we discuss fundamental building principles of GPMs and review recent and emerging applications of those models in the chemical sciences across the entire scientific process. While many of these applications are still in the prototype phase, we expect that the increasing interest in GPMs will make many of them mature in the coming years. Leverage (statistics) ; Class (philosophy) ; Fuzzy logic ; Chemical products ; Term (time) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Ahmad Altarabsheh | Ibrahim Altarabsheh | Xiang Chen
Source: International Journal of Solids and Structures (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Predicting nanoscale stress-strain curves: A Gaussian processes within a Bayesian framework Nanoscopic scale ; Gaussian ; Materials science ; Bayesian probability ; Strain (injury) ; Gaussian process ; Stress (linguistics) ; Stress–strain curve ; Mathematics ; Statistical physics ; Composite material ; Physics ; Deformation (meteorology) ; Nanotechnology ; Statistics Microstructure and mechanical properties ; Force Microscopy Techniques and Applications ; Machine Learning in Materials Science
Authors: Sofia Zinzani | Francesca Baletto | Kevin Rossi
Source: ArXiv.org (preprint, 2025-09-13)
Field: Materials Science — Materials Chemistry

Bridging Structure and Activity in Nanocatalysts via Machine Learning and Global Structure Representations Establishing a mapping between nanocatalysts structure and their catalytic properties is essential for efficient design. To this end, we demonstrate the accuracy of a general machine learning framework on a representative and challenging application: predicting the mass activity of Pt nanoparticles for the electrochemical oxygen reduction reaction, estimated via a microkinetic model. Accurate models are obtained when leveraging either a nanocatalyst's structure representation accessible at the computational level, namely the surface site generalized coordination number distributions, or one accessible experimentally, namely the nanoparticle's pair distance distribution function. Building on this result, we demonstrate that our machine learning model, in tandem with Bayesian optimization, efficiently identifies the Top-10 and Top-100 most active structures out of a large pool of candidates comprising more than 50000 different structures, after probing the activity only of a few thousand structures. These findings provide a robust blueprint for accelerated theoretical and experimental identification of active nanocatalysts. Nanomaterial-based catalyst ; Bridging (networking) ; Bayesian probability ; Representation (politics) ; Identification (biology) ; Active learning (machine learning) ; Feature learning ; Support vector machine Machine Learning in Materials Science
Authors: Radion A. Zaripov | Roman A. Eremin | Innokentiy S. Humonen | Aliaksei V. Krautsou | В. В. Кузнецов | Konstantin E. German | Semen A. Budennyy | Sergey V. Levchenko
Source: Acta Materialia (article, 2025-11-05)
Field: Materials Science — Materials Chemistry

First-principles data-driven approach for assessment of stability of Tc-C systems Density functional theory ; Chemical stability ; Carbide ; Structural stability ; Stability (learning theory) ; Carbon fibers ; Hexagonal crystal system ; Entropy (arrow of time) ; Phase (matter) Machine Learning in Materials Science ; Nuclear Materials and Properties ; Boron and Carbon Nanomaterials Research
Authors: Pengxiang Wang | Zhuoran Li | Limin Cao | Jianxin Sui | Hong Lin | Xiudan Wang | Kaiqiang Wang
Source: Microchemical Journal (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Machine learning-assisted early evaluation of aquatic products freshness based on UV-enhanced multi-enzymatic cascade Cascade ; Chemistry ; Enzyme ; Biochemical engineering ; Computer science ; Chromatography ; Biochemistry ; Engineering Advanced Nanomaterials in Catalysis ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Xinyu Zhao | Yanjie Lv | Lijie Chen | Le Li | Xin Zhang | Zhongran Dai
Source: Biomass and Bioenergy (article, 2025-08-05)
Field: Chemistry — Inorganic Chemistry

Optimization of slaughter sludge-derived biochar via response surface methodology and backpropagation neural network for efficient uranium extraction from radioactive wastewater Biochar ; Uranium ; Backpropagation ; Wastewater ; Extraction (chemistry) ; Response surface methodology ; Artificial neural network ; Waste management ; Radioactive waste ; Environmental science ; Radiochemistry ; Materials science ; Chemistry ; Computer science ; Chromatography ; Engineering ; Metallurgy ; Pyrolysis ; Artificial intelligence Radioactive element chemistry and processing ; Adsorption and biosorption for pollutant removal
Authors: Juncheng Hu | Wenjie Nie | Shengfeng Zhao | Yong Liu | Hengyi Zhu | Shuibin Tu | Jiawei Zhang | K. Yang | Ning Xue | Justin Z. Lian | Bin Dong | Stefano Cucurachi | Yuan Gao
Source: Ultrasonics Sonochemistry (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Eco-friendly production of AgNPs by ultrasound-intensified continuous method, and process evaluation via life cycle assessment and machine learning Owing to their excellent biocompatibility and antibacterial properties, the global annual production of silver nanoparticles (AgNPs) is estimated at 400-800 tons. Therefore, developing a green and safe approach for AgNPs synthesis is urgently required. However, the conventional AgNPs manufacturing methods required toxic reductant and higher energy consumption, which makes the reaction system harmful to humans and environment. In this study, an eco-friendly ultrasound-intensified continuous system employing minimal toxic reagents was developed. The AgNPs formation process was intensified by cavitation instead of using toxic and strong reductants. Several analytical methods were conducted to systematically investigate the mechanism and performance of as-synthesized AgNPs, such as particle size distribution, zeta potential, UV-visible absorption, morphology, yield calculation, as well as in vitro photothermal, and antibacterial. The results clearly indicated that the physicochemical properties of AgNPs could be tuned by adjusting process parameters, including temperature, feeding rate, and ultrasonic power. Specifically, the AgNPs prepared at 90 ℃, 600 W and 50 µL/min exhibited smaller size (2-5 nm) with monodisperse, spherical shape, higher yield, enhanced photothermal, and antibacterial activities. Furthermore, life-cycle assessment (LCA) and machine learning (ML) were employed to evaluate the process sustainability and identify the key influencing parameters. Comprehensive consideration of safety, experimental results, and LCA outcomes demonstrated that the ultrasound-intensified continuous method proposed in this study is superior to the traditional NaBH<sub>4</sub>-based batch synthesis at the laboratory scale. Silver nanoparticle ; Biocompatibility ; Process (computing) ; Zeta potential ; Reagent ; Green chemistry ; Yield (engineering) ; Nanoparticle Nanoparticles: synthesis and applications ; Ultrasound and Cavitation Phenomena ; Nanomaterials for catalytic reactions
Authors: Lijun Shang | Yongli Yang | Yadong Yu | Xiang Pan | Li Ma | Zhonglu Guo | Mengyan Dai
Source: Journal of Materials Informatics (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

Stacked machine learning for accurate and interpretable prediction of MXenes’ work function MXenes, with tunable compositions and rich surface chemistry, enable precise control of electronic, optical, and mechanical properties, making them promising materials in electronics and energy-related applications. In particular, the work function plays a critical role in determining their physicochemical properties. However, the accurate prediction of the work function of MXenes with machine learning (ML) remains challenging due to the lack of robust models with high accuracy and interpretability. To this end, we propose a stacked model and introduce high-quality descriptors constructed via Sure Independence Screening and Sparsifying Operator method to improve the prediction accuracy of the work function of MXenes in this work. The stacked model initially generates predictions from multiple base models, and then employs these predictions as inputs to a meta-model for secondary learning, thereby enhancing both predictive performance and generalization capability. The results show that by integrating the high-quality descriptors, the model’s performance improves significantly, yielding a coefficient of determination of 0.95 and mean absolute error of 0.2, respectively. Last but not least, we demonstrate that MXenes’ work functions are predominantly governed by their surface functional groups, where SHapley Additive exPlanations value analysis quantitatively resolves the structure–property relationship between surface functional groups and the work function of MXenes. Specifically, O terminations can lead to the highest work functions, while OH terminations result in the lowest value (over 50% reduction), and transition metals or C/N elements have a relatively smaller effect. This work achieves an optimal balance between accuracy and interpretability in ML predictions of MXenes’ work functions, providing both fundamental insights and practical tools for materials discovery. Interpretability ; MXenes ; Function (biology) ; Work (physics) ; Generalization ; Operator (biology) ; Independence (probability theory) ; Surface (topology) MXene and MAX Phase Materials ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Anna Katharina Picha | Marcus Wieder | Stefan Boresch
Source: ChemRxiv (preprint, 2025-04-24)
Field: Materials Science — Materials Chemistry

Condensed phase properties and transferable neural network potentials Transferable neural network potentials (NNP) are undergoing rapid development. Many practical applications of NNPs focus on single molecules; e.g., using NNPs as a fast replacement for quantum chemical methods for dihedral angle scans in force field development. Similarly, the reference data on which most portable NNPs have been trained are single molecules. As NNPs are beginning to be used to simulate more complex systems, such as solute-solvent simulations, the question arises whether the current generation of transferable NNPs is accurate enough to reproduce condensed phase properties, which in most cases are outside the training domain of the models. Here we present a first analysis of how well two transferable NNPs (ANI-2x, MACE-OFF23-(S/M)) perform in predicting properties such as density, heat of vaporization, heat capacity, and isothermal compressibility of several pure liquids (water, methanol, acetone, benzene, n-hexane at room temperature, and N-methylacetamide at 100C). In addition, we look at selected pair correlation functions and the mean square displacement. Currently, each of the models has specific weaknesses, and seemingly small flaws lead to poor performance when applied to condensed phase simulations. This suggests that the reproduction of condensed phase properties should be considered in the training/validation of transferable NNPs, as is standard in the development of classical mechanical force fields. Phase (matter) ; Artificial neural network ; Chemical physics ; Chemistry ; Statistical physics ; Materials science ; Biological system ; Physics ; Computer science ; Artificial intelligence ; Biology ; Quantum mechanics Machine Learning in Materials Science
Authors: Emmanuel G. Ikenga | Chidozie Chukwuemeka Nwobi-Okoye | Remy Uche
Source: Discover Artificial Intelligence (article, 2025-11-21)
Field: Materials Science — Polymers and Plastics

Multi-objective optimization of plantain/coconut fibres hybrid reinforced polymer composite using ANN, GRA and genetic algorithm This study optimized the properties of a novel hybrid composite made from plantain and coconut fibers within a polyester resin matrix using artificial intelligence techniques. Three key variables—hybridization ratio (plantain to coconut fibers), total fiber mass fraction, and fiber length—were used as design inputs. The outputs were composite density (g/cm3) and three mechanical properties: tensile strength (MPa), impact strength (J), and flexural strength (MPa). To handle this multi-objective problem, Grey Relational Analysis (GRA) was first used to combine the mechanical properties into a single Grey Relational Grade (GRG), representing overall performance. An Artificial Neural Network (ANN) was then trained to predict GRG and density based on the input variables, showing high predictive accuracy. These ANN models were embedded within the Non-Dominated Sorting Genetic Algorithm II (NSGA II) framework to simultaneously maximize GRG and minimize density. From the resulting Pareto front, the optimal solution was selected using the minimum-distance method, yielding a composite with a density of 0.7841 g/cm3, tensile strength of 48.93 MPa, impact strength of 60.09 J, and flexural strength of 26.69 MPa at a GRG value of 0.6494. These properties corresponded to a hybridization ratio of 1.67, a fiber mass fraction of 4.25 wt%, and a fiber length of 40.7 mm. The validation experiment yielded the following values: 0.8142 g/cm3, 47.1000 MPa, 59.5000 J, and 25.6000 MPa for density, tensile strength, impact strength, and flexural strength respectively, with relative errors of 3.8388%, 3.7471%, 0.9794 and 4.0992% respectively. The integrated GRA–ANN–NSGA II approach effectively tailored lightweight, high-performance biocomposites and offers a generalizable tool for sustainable material design. This result represents an important advancement of the role of artificial intelligence in advanced materials engineering and sustainable manufacturing. Ultimate tensile strength ; Flexural strength ; Composite number ; Grey relational analysis ; Fiber ; Genetic algorithm ; Artificial neural network ; Compressive strength Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Aluminum Alloys Composites Properties
Authors: Ahmed Ould Boudia
Source: Plasmonics (article, 2025-03-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Enhancing Single-Molecule SERS Analysis of Amino Acids Using AI-Driven Hyperspectral Imaging and Gold Nanostars Hyperspectral imaging ; Materials science ; Nanotechnology ; Molecule ; Chemistry ; Computer science ; Artificial intelligence ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Wenjie Zhou | Changming Ke | Shi Liu
Source: Physical review. B./Physical review. B (article, 2025-10-15)
Field: Materials Science — Materials Chemistry

Two-dimensional ferroelectric crystal with temperature-invariant ultralow thermal conductivity We report the discovery of temperature-invariant ultralow thermal conductivity ($\ensuremath{\kappa}$) in monolayer ${\ensuremath{\beta}}^{\ensuremath{'}}\text{\ensuremath{-}}{\mathrm{In}}_{2}{\mathrm{Se}}_{3}$, a two-dimensional ferroelectric crystal with in-plane polarization. Using a combination of generalized Wigner transport equation theory and machine-learning-assisted molecular dynamics simulations, we reveal that the balance between particlelike phonon propagating and wavelike tunneling transport mechanisms results in a propagating-tunneling-invariant ultralow thermal conductivity of approximately 0.6 W/mK (comparable to that of glass) over a broad temperature range ($150&lt;T&lt;800$ K). This behavior stems from intrinsic strong lattice anharmonicity driven by ferroelectric dipolar fluctuations, eliminating the need for extrinsic structural modifications. In contrast, the $\ensuremath{\alpha}\text{\ensuremath{-}}{\mathrm{In}}_{2}{\mathrm{Se}}_{3}$ monolayer, which shares the same stoichiometry, exhibits a temperature-dependent thermal conductivity typical of simple crystals. We show that the anharmonicity in ${\ensuremath{\beta}}^{\ensuremath{'}}\text{\ensuremath{-}}{\mathrm{In}}_{2}{\mathrm{Se}}_{3}$ can be precisely modulated by an external electric field, enabling on-demand control of the temperature scaling behavior of heat conductivity. Furthermore, an electric-field-driven motion of the $\ensuremath{\alpha}/{\ensuremath{\beta}}^{\ensuremath{'}}$ phase interface is demonstrated, supporting a nonvolatile, large thermal switching ratio of $&gt;3$. These findings provide fundamental insights into the interplay between field-tunable lattice anharmonicity, phonon dynamics, and thermal transport mechanisms. Anharmonicity ; Thermal conductivity ; Phonon ; Ferroelectricity ; Quantum tunnelling ; Dipole ; Lattice (music) ; Scaling ; Thermal conduction Ferroelectric and Piezoelectric Materials ; Material Dynamics and Properties ; Glass properties and applications