jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mood Mohan | Sreelekha Guggilam | Debsindhu Bhowmik | Michelle K. Kidder | Jeremy C. Smith
Source: ACS Sustainable Chemistry & Engineering (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents. Artificial neural network ; Enthalpy of vaporization ; Generative grammar ; Quantitative structure–activity relationship ; Correlation coefficient ; Molecular descriptor ; Predictive modelling ; Computational model ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Bing Yan | Shijia Wang | Gui Xiao | Xinpeng Xing | Qishun Guo | Chengang Cao | Jian Li | Tao Jiang
Source: Journal of Catalysis (article, 2025-09-17)
Field: Chemistry — Organic Chemistry

QSAR-driven digital catalyst models: high-throughput design of polyolefin elastomers Polyolefin ; Metallocene ; Catalysis ; Comonomer ; Elastomer ; Post-metallocene catalyst ; Copolymer Organometallic Complex Synthesis and Catalysis ; Catalysis and Oxidation Reactions ; Machine Learning in Materials Science
Authors: Haorui Wang | Jeff Guo | Lingkai Kong | Rampi Ramprasad | Philippe Schwaller | Yuanqi Du | Chao Zhang
Source: ArXiv.org (preprint, 2025-05-11)
Field: Materials Science — Materials Chemistry

LLM-Augmented Chemical Synthesis and Design Decision Programs Retrosynthesis, the process of breaking down a target molecule into simpler precursors through a series of valid reactions, stands at the core of organic chemistry and drug development. Although recent machine learning (ML) research has advanced single-step retrosynthetic modeling and subsequent route searches, these solutions remain restricted by the extensive combinatorial space of possible pathways. Concurrently, large language models (LLMs) have exhibited remarkable chemical knowledge, hinting at their potential to tackle complex decision-making tasks in chemistry. In this work, we explore whether LLMs can successfully navigate the highly constrained, multi-step retrosynthesis planning problem. We introduce an efficient scheme for encoding reaction pathways and present a new route-level search strategy, moving beyond the conventional step-by-step reactant prediction. Through comprehensive evaluations, we show that our LLM-augmented approach excels at retrosynthesis planning and extends naturally to the broader challenge of synthesizable molecular design. Retrosynthetic analysis ; Chemical space ; Process (computing) ; Scheme (mathematics) ; Encoding (memory) ; Core (optical fiber) ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Alexandre Boucher | Cameron Beevers | Bertrand Gauthier | Alberto Roldán
Source: Journal of Chemical Theory and Computation (article, 2025-02-25)
Field: Materials Science — Materials Chemistry

Machine Learning Force Field for Optimization of Isolated and Supported Transition Metal Particles Computational modeling is an integral part of catalysis research. With it, new methodologies are being developed and implemented to improve the accuracy of simulations while reducing the computational cost. In particular, specific machine-learning techniques have been applied to build interatomic potential from ab initio results. Here, we report an energy-free machine-learning calculator that combines three individually trained neural networks to predict the energy and atomic forces of metallic particles. The investigated structures were a monometallic Pd nanoparticle, a bimetallic AuPd nanoalloy, and supported Pd metal crystallites on silica. Atomic energies were predicted via a graph neural network, leading to a mean absolute error (MAE) within 0.004 eV from density functional theory (DFT) calculations. The task of predicting atomic forces was split over two feed-forward networks, one predicting the force norm and another its direction. The force prediction resulted in a MAE within 0.080 eV/Å against DFT results. The interpretability of the graph neural network predictions was demonstrated by underlying the physics of the monometallic particle in the form of cohesion energy. Force field (fiction) ; Field (mathematics) ; Computer science ; Nanotechnology ; Data science ; Chemical physics ; Materials science ; Artificial intelligence ; Chemistry ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Advanced Materials Characterization Techniques
Authors: Xiaoang Yuan | Bo Zhu | Chunbo Zhang | Qifan Zheng | Enlai Gao | Qian Shao
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated discovery of ultraincompressible, superhard materials <i>via</i> physics-enhanced active learning The discovery of ultraincompressible, superhard materials is constrained by the high computational cost of screening vast chemical spaces using density functional theory (DFT). To address this, we introduced a machine learning framework that integrates crystal graph convolutional neural networks (CGCNN) with physics-based atomic stiffness descriptors derived from bond-level mechanical models. This physics-enhanced approach achieves superior accuracy in predicting bulk and shear moduli while maintaining interpretability. By employing active learning to efficiently prioritize candidates from a pool of over 2.7 million inorganic crystals, we reduced the reliance on DFT validation and identified 632 ultraincompressible materials (bulk modulus > 400 GPa) and 15 superhard crystals (Vickers hardness > 40 GPa). Remarkably, over 90% of these ultraincompressible candidates and over 60% of these superhard materials are previously uncharacterized. Structural analysis revealed that ultraincompressibility predominantly emerges in low-symmetry intermetallic compounds showing strong Os/Re/Ir compositional preference, whereas superhard characteristics primarily associate with ceramic-based systems, including covalent compounds formed by light elements and transition metal-light element compounds exhibiting partial covalent bonding. Our methodology not only expands the known family of ultraincompressible, superhard materials but also overcomes a major scalability barrier in computational material discovery. These predictions, rigorously validated through DFT, establish a physics-grounded, data-driven pipeline to accelerate the exploration of extreme-performance materials for industrial applications. Materials science ; Nanotechnology Diamond and Carbon-based Materials Research ; Boron and Carbon Nanomaterials Research ; High-pressure geophysics and materials
Authors: Richard Murdoch Montgomery
Source: ChemRxiv (preprint, 2025-06-27)
Field: Materials Science — Materials Chemistry

Bridging Scales in Heterogeneous Catalysis: A Multiscale Computational Framework for Operando Conditions The accurate modelling of heterogeneous catalytic processes under operando con- ditions remains one of the most formidable challenges in computational chemistry and materials science. Traditional approaches employing static surface models fail to capture the dynamic nature of active sites, adsorbate-induced reconstruc- tions, and temperature-dependent phenomena that characterise real catalytic sys- tems. This work presents a comprehensive multiscale computational framework that bridges density functional theory (DFT) calculations with kinetic Monte Carlo (kMC) simulations, enhanced by a high-fidelity machine learning potential (MLP), to achieve realistic timescales commensurate with experimental turnover frequen- cies. We demonstrate the methodology through the exemplar case of CO oxidation on Pt(111). The framework reveals previously unidentified dynamic phenomena, including a non-monotonic dependence of activation energy on coverage driven by dynamic site creation and temperature-induced surface restructuring. The frame- work successfully predicts turnover frequencies within an order of magnitude of experimental values across a wide temperature range, a significant improvement over static models. Our results indicate that incorporating surface dynamics and realistic reaction conditions fundamentally alters predicted catalytic behaviour, sug- gesting that static models may systematically misrepresent catalytic mechanisms. This methodology opens new avenues for rational catalyst design based on operando behaviour rather than idealised surface structures. Bridging (networking) ; Multiscale modeling ; Catalysis ; Computer science ; Scale (ratio) ; Nanotechnology ; Environmental science ; Materials science ; Chemistry ; Computational chemistry ; Geography ; Cartography Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalytic Processes in Materials Science
Authors: Jothi Arunachalam Solairaju | Nagaraj Ashok
Source: Discover Applied Sciences (article, 2025-12-01)
Field: Materials Science — Polymers and Plastics

Prediction of the mechanical strength of modified kenaf fiber reinforced polymer incorporating nanographene using ANN Artificial Intelligence (AI) methods, such as artificial neural networks (ANN) and machine learning, have found a common use in solving numerous challenges in engineering. The present research work consisted of the making of a hybrid polymer nanocomposite through reinforcing a polymer composite with natural fibers of kenaf (Hibiscus cannabinus) fiber (KF) and Nanographene. A chemical treatment to help in changing the surface property of the fibres facilitate its adhesion and interaction with the polymeric matrix was conducted using a potassium permanganate (KMnO4) solution in acetone (C3H6O). Originally, the ANN model was applied and trained to predict and optimize the tensile strength (TS) of the resultant KF/nanographene hybrid nanocomposite (KFN). The utilized model was the architecture of a single-layer perceptron with the configuration 3-5-1, and the hidden layer with five neurons. The Central Composite Design (CCD) was used to design the experiments in such a way that it gave a systematic approach towards knowing the impact of the specific variables on the tensile strength. Scanning electron microscopy (SEM) studies supported the great influence of the modification of KMnO4 on the fiber-matrix interface and hence on the mechanical properties of the composite. The outcomes of the mechanical testing carried out using analysis of variance (ANOVA) proved that the major factors had a significant impact on the tensile strength, whilst the model fitted excellently with a value of coefficient of determination R2 = 0.9805. By using the ANN-CCD model, the optimum tensile strength value of 46 MPa was predicted; this value was simply the nearest match to the experimental validation test method’s experimentally obtained value of 45.4 MPa, which translates to a near 98.45% accuracy in predicting the model’s result. This paper brings out the usefulness of using ANN with CCD to quickly get reliable estimations of mechanical properties and therefore save experimental design time and cost of production and resources of developing composite materials. The use of natural resources such as kenaf fiber combined with nanographene promotes a sustainable approach by enhancing material performance while supporting sustainable development goals in materials engineering. Ultimate tensile strength ; Composite number ; Natural fiber ; Kenaf ; Fiber ; Polymer ; Synthetic fiber ; Nanocomposite Natural Fiber Reinforced Composites ; Carbon Nanotubes in Composites ; Scientific and Engineering Research Topics
Authors: Michiel Jacobs | Lise Vermeersch | Freija De Vleeschouwer
Source: Journal of Computational Chemistry (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Conceptual <scp>DFT</scp> Meets Machine Learning: A New Route to Enhanced Diels–Alder Reactivity Conceptual Density Functional Theory (CDFT) offers a rigorous framework for understanding chemical reactivity through energy-response descriptors. The global electrophilicity index (ω) is particularly useful for rationalizing trends in reactions such as Diels-Alder (DA) cycloadditions. In this work, we present a deep-learning model predicting ω from randomized Coulomb matrices derived from force-field geometries. This enables high-throughput screening with accuracy comparable to DFT but at a fraction of the cost. Validation shows prediction errors below 0.1 eV for nucleophiles and ~0.3 eV for less abundant electrophiles from our PubChem and GDB13 datasets. Diels-Alder activation barriers of a curated set of dienophiles with furan and fulvene correlated well with ω, especially when considering the transition-state interaction energy. Of value for applications in bioconjugation and self-healing polymers, several candidates were identified as more reactive than maleimide (ω = 1.63 eV). Machine-learning models targeting CDFT descriptors thus provide scalable, interpretable tools for automated reaction-space exploration. Electrophile ; PubChem ; Reactivity (psychology) ; Nucleophile ; Set (abstract data type) ; Bioconjugation ; Furan ; Density functional theory Machine Learning in Materials Science ; Organic Chemistry Cycloaddition Reactions ; Cyclization and Aryne Chemistry
Authors: Rashid R. Valiev | Rinat T. Nasibullin | Hilda Sandström | Patrick Rinke | Kai Puolamäki | Theo Kurtén
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting intersystem crossing rate constants of alkoxy-radical pairs with structure-based descriptors and machine learning Peroxy radicals (RO<sub>2</sub>) are ubiquitous intermediates in many oxidation processes, especially in the atmospheric gas phase. The recombination reaction of two peroxy radicals (RO<sub>2</sub> + R'O<sub>2</sub>) has been demonstrated to lead, <i>via</i> several steps, to a triplet complex of two alkoxy radicals: <sup>3</sup>(RO˙⋯R'O˙). The different product channels of RO<sub>2</sub> + R'O<sub>2</sub> reactions thus correspond to different reactions of this triplet complex. Of particular interest to atmospheric chemistry is the intersystem crossing (ISC) to the singlet state, which enables the recombination of the two radicals to an ROOR' peroxide with considerably lower volatility than the original precursors. These peroxides are believed to be key contributors to the formation of secondary organic aerosol (SOA) particles, which in turn contribute to both air pollution and radiative forcing uncertainties. Developing reliable computational models for, <i>e.g.</i>, RO<sub>2</sub> + R'O<sub>2</sub> branching ratios requires accurate estimates of the ISC rate constants, which can currently be obtained only from computationally expensive quantum chemistry calculations. By contrast, machine learning (ML) methods offer a faster alternative for estimating ISC rate constants. In the present work, we create a dataset with 98 082 conformations of radical pairs and their corresponding rate constants. We apply three ML models-random forest (RF), CatBoost (CB), and a neural network (NN)-to predict ISC rate constants from triplet to singlet states. Specifically, the models predict <i>k</i><sub>ISC</sub>(T<sub>1</sub> → S<sub>i</sub>) for <i>i</i> = 1-4 and the cumulative <i>k</i><sub>ISC</sub>(T<sub>1</sub> → S<sub><i>n</i></sub>), in alkoxy radical pairs, using only molecular geometry descriptors as inputs. All ML models achieved a mean absolute error (MAE) on our test set within one order of magnitude and a coefficient of determination <i>R</i><sup>2</sup> > 0.82 for all rate constants. Overall, the ML prediction matches the quantum chemical calculations within 1-2 orders of magnitude, providing a fast and scalable alternative for quantum chemical methods for ISC rate estimation. Intersystem crossing ; Alkoxy group ; Reaction rate constant ; Chemistry ; Computational chemistry ; Photochemistry ; Chemical physics ; Physics ; Atomic physics ; Organic chemistry ; Excited state ; Classical mechanics ; Kinetics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Miaomiao Zhang | Yuming Su | Tianyi Du | Shihao Ding | Jieyu Dai | Cheng Wang | Yun Liu
Source: Angewandte Chemie (article, 2025-03-27)
Field: Chemistry — Organic Chemistry

Revealing Transition State Stabilization in Organocatalytic Ring‐Opening Polymerization Using Data Science Abstract In nature, enzymes leverage constituent amino acid residues to create catalytically active sites to effect high reactivity and selectivity. Multicomponent host–guest assemblies have been exploited to mimic enzymatic microenvironments by pre‐organizing a network of noncovalent interactions. While organocatalysts such as thioureas have gained widespread success in organic transformation and controlled polymerization, evaluation of the participating structural features in the transition state (TS) remains challenging. Herein, we report the use of data science tools, i.e., a decision‐tree‐based machine‐learning algorithm and Shapley additive explanations (SHAP) analysis, to model reactivity and regioselectivity in a thiourea‐catalyzed ring‐opening polymerization of 1,2‐dithiolanes. Variation of aryl substituent position and electronic characteristics reveals key catalyst features involved in the TS. The analysis of feature importance helps explain the reason behind the optimal performance of (pseudo)halogen‐substituted catalysts. Furthermore, the structural basis for the unveiled reactivity‐regioselectivity trade‐off in the catalysis are established. Polymerization ; Ring (chemistry) ; State (computer science) ; Chemistry ; Organocatalysis ; Ring-opening polymerization ; Polymer chemistry ; Materials science ; Nanotechnology ; Chemical engineering ; Photochemistry ; Combinatorial chemistry ; Catalysis ; Enantioselective synthesis ; Polymer ; Organic chemistry ; Computer science ; Engineering Advanced Polymer Synthesis and Characterization
Authors: Anonymous | Matthias Schmidt
Source: Physical Review Letters (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Determining the Chemical Potential via Universal Density Functional Learning We demonstrate that the machine learning of density functionals allows one to determine simultaneously the equilibrium chemical potential across simulation datasets of inhomogeneous classical fluids. Minimization of a loss function based on an Euler-Lagrange equation yields both the universal one-body direct correlation functional, which is represented locally by a neural network, as well as the system-specific unknown chemical potential values. The method can serve as an efficient alternative to conventional computational techniques of measuring the chemical potential. It also facilitates using canonical data from Brownian dynamics, molecular dynamics, or Monte Carlo simulations as a basis for constructing neural density functionals, which are fit for accurate multiscale prediction of soft matter systems in equilibrium. Minification ; Density functional theory ; Basis (linear algebra) ; Function (biology) ; Monte Carlo method ; Probability density function ; Artificial neural network ; Basis function Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Hakan Demir | İlknur Eruçar
Source: Advanced Theory and Simulations (article, 2025-05-25)
Field: Chemistry — Inorganic Chemistry

Effect of Atomic Charges on C<sub>2</sub>H<sub>2</sub>/CO<sub>2</sub>/CH<sub>4</sub> Separation Performances of Covalent‐Organic Framework Adsorbents Abstract A critical factor for the accuracy of computational screening studies is the method employed to assign atomic charges. While chemically meaningful atomic charges can be obtained using a quantum chemistry method‐based charge assignment technique (density‐derived electrostatic and chemical method (DDEC6)), its application to large material datasets remains computationally demanding. As an alternative, machine‐learning (ML) models can offer the ability to determine atomic charges with high accuracy and speed. Herein, two ML models, Partial Atomic Charge Predicter for Porous Materials based on Graph Convolutional Neural Network (PACMAN) and Partial Atomic Charges in Metal‐Organic Frameworks (PACMOF), are utilized to predict atomic charges in Clean, Uniform, Refined with Automatic Tracking from Experimental Database (CURATED) covalent‐organic frameworks (COFs). The predicted atomic charges are used in simulations to assess COFs' C 2 H 2 /CO 2 /CH 4 separation performances in comparison with reference DDEC6‐based performances. Results show PACMAN charges can more effectively reproduce DDEC6‐based charges and corresponding separation performance metrics, underscoring their suitability for high‐throughput material screening. Additionally, the proportions of Coulombic interactions to van der Waals interactions are systematically analyzed, revealing substantial variation across both narrow and wide pores. This study highlights that ML models can be applied to obtain atomic charges that could enable attaining accurate material performance evaluations. Adsorption ; Covalent bond ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Physical chemistry ; Environmental chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies