jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lingyu Kong | Nima Shoghi | Guoxiang Hu | Pan Li | Victor Fung
Source: ArXiv.org (preprint, 2025-04-14)
Field: Materials Science — Materials Chemistry

MatterTune: An Integrated, User-Friendly Platform for Fine-Tuning Atomistic Foundation Models to Accelerate Materials Simulation and Discovery Geometric machine learning models such as graph neural networks have achieved remarkable success in recent years in chemical and materials science research for applications such as high-throughput virtual screening and atomistic simulations. The success of these models can be attributed to their ability to effectively learn latent representations of atomic structures directly from the training data. Conversely, this also results in high data requirements for these models, hindering their application to problems which are data sparse which are common in this domain. To address this limitation, there is a growing development in the area of pre-trained machine learning models which have learned general, fundamental, geometric relationships in atomistic data, and which can then be fine-tuned to much smaller application-specific datasets. In particular, models which are pre-trained on diverse, large-scale atomistic datasets have shown impressive generalizability and flexibility to downstream applications, and are increasingly referred to as atomistic foundation models. To leverage the untapped potential of these foundation models, we introduce MatterTune, a modular and extensible framework that provides advanced fine-tuning capabilities and seamless integration of atomistic foundation models into downstream materials informatics and simulation workflows, thereby lowering the barriers to adoption and facilitating diverse applications in materials science. In its current state, MatterTune supports a number of state-of-the-art foundation models such as ORB, MatterSim, JMP, and EquformerV2, and hosts a wide range of features including a modular and flexible design, distributed and customizable fine-tuning, broad support for downstream informatics tasks, and more. Modular design ; Flexibility (engineering) ; Leverage (statistics) ; Foundation (evidence) ; Informatics ; Materials informatics ; Generalizability theory Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: Jonathon E. Vandezande | Corin Wagen
Source: ChemRxiv (preprint, 2025-07-21)
Field: Materials Science — Materials Chemistry

ExpBDE54: A Slim Experimental Benchmark for Exploring the Pareto Frontier of Bond-Dissociation-Enthalpy-Prediction Methods ExpBDE54 is a benchmark dataset of experimental homolytic bond-dissociation enthalpies (BDEs) for 54 small molecules. Single-point energies were computed using density-functional theory, semiempirical methods, and neural network potentials; linear regression corrections were applied to capture enthalpic effects. g-xTB//GFN2-xTB and OMol25's eSEN Conserving Small define the Pareto frontier, yielding root-mean-square errors of 4.7 and 3.6 kcal⋅mol¯¹. These results demonstrate that suitably corrected semiempirical and machine-learning approaches can enable rapid, accurate BDE predictions. Benchmark (surveying) ; Frontier ; Pareto principle ; Bond ; Dissociation (chemistry) ; Enthalpy ; Bond-dissociation energy ; Thermodynamics ; Economics ; Econometrics ; Computer science ; Chemistry ; Physical chemistry ; Physics ; Geography ; Operations management ; Cartography Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; Chemical Thermodynamics and Molecular Structure
Authors: T. SEO | Donghun Kim | Shinwon Ham | You Kyoung Chung | Inho Jeong | Joonsuk Huh | Hyunwoo Kim | Do Hyun Ryu
Source: Angewandte Chemie International Edition (article, 2025-11-02)
Field: Chemistry — Inorganic Chemistry

Generality‐Driven Optimization of Enantio‐ and Regioselective Mono‐Reduction of 1,2‐Dicarbonyls by High‐Throughput Experimentation and Machine Learning The longstanding quest for substrate generality stems from the unpredictability of single-model optimization. Leveraging high-throughput experimentation (HTE), we present a practical multi-substrate screening strategy for the general asymmetric mono-reduction of 1,2-dicarbonyls. Quantitative <sup>1</sup>H NMR spectroscopy combined with simultaneous chiral analysis by <sup>19</sup>F NMR for pooled crude mixtures accelerated the workflow eightfold. Robust screening of 31 chiral oxazaborolidinium ion (COBI) variants across eight substrates tackled even ethyl/methyl differentiation. HTE data were utilized in a machine learning (ML) model with CGR (Condensed Graphs of Reaction)-based descriptors, identifying catalysts for target substrates without quantum chemical calculations. The ARMS (Automated Reaction Mapping for various Substituents) system was introduced to streamline SMILES (Simplified Molecular Input Line Entry System) preprocessing for multi-substrate datasets. The resulting chiral α-silyloxy ketones, obtained in excellent yields (up to >99%) and selectivities (up to >99% ee, >20:1 r.r.), could be readily transformed into high-value compounds, such as (S)-bupropion. Workflow ; Preprocessor ; Regioselectivity ; Troubleshooting ; Substrate (aquarium) ; Generality ; Nuclear magnetic resonance spectroscopy ; Feature (linguistics) Asymmetric Hydrogenation and Catalysis ; Machine Learning in Materials Science ; Asymmetric Synthesis and Catalysis
Authors: Lisa Hamada | Akihiro Kishimoto | Kohei Miyaguchi | Masataka Hirose | Junta Fuchiwaki | Indra Priyadarsini | Seiji Takeda
Source: Research Square (preprint, 2025-04-07)
Field: Chemistry — Spectroscopy

Revisiting Molecular Descriptors with TDiMS for Interpretable Intramolecular Interactions Based on Substructure Pairs Substructure ; Intramolecular force ; Computer science ; Artificial intelligence ; Psychology ; Chemistry ; Stereochemistry ; Engineering ; Structural engineering Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Computational Drug Discovery Methods
Authors: Brian S. Cahill | Lin Wang | Bin Ouyang
Source: MRS Communications (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Specialized modeling, database, and AI for Li-ion and Na-ion battery materials Materials science ; Ion ; Battery (electricity) ; Database ; Engineering physics ; Computer science ; Thermodynamics ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Technologies Research
Authors: Samad Hajinazar | Eva Zurek
Source: Computer Physics Communications (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

XtalOpt version 14: Variable-composition crystal structure search for functional materials through Pareto optimization Pareto principle ; Multi-objective optimization ; Crossover ; Evolutionary algorithm ; Set (abstract data type) ; Local search (optimization) ; Search algorithm ; Variable (mathematics) ; Feature (linguistics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Ossi Laurila | Tiia Jacklin | Ouail Zakary | Perttu Lantto
Source: ChemRxiv (article, 2025-11-05)
Field: Chemistry — Organic Chemistry

Machine Learning-Accelerated Path Integral Molecular Dynamics and 13C NMR Simulations Unlock New Insights Into Quantum Effects in C60 Fullerene Molecular dynamics ; Isotropy ; Dispersion (optics) ; Fullerene ; Ab initio ; Carbon-13 NMR ; Limiting ; Path (computing) Fullerene Chemistry and Applications ; Thermal Expansion and Ionic Conductivity ; Thermal properties of materials
Authors: Patrick Kimber (8504748) | James Mattock (22136370) | Sophia Wheeler (22136373) | John Mullaney (22136376) | Alison Beardah (22136379) | Justin Fellows (15143520) | Kenny Jolley (7125533) | Felix Plasser (1293858)
Source: Figshare (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Machine LearningDensities, Detonation Velocities,and Formation Enthalpies of Energetic Materials Using Quantum ChemistryDescriptors The prediction of detonation parameters is a challenging task requiring to bridge the gap between microscopic molecular features and macroscopic materials properties. Whereas traditional routes are concerned with empirical equations, we present a machine learning approach to this task here. Our approach capitalizes on molecular descriptors from high-level quantum chemistry as input to produce models fitted against experimental reference data to model three key quantities: the crystalline density, the detonation velocity, and the heat of formation. To determine the detonation products, we use a new nonempirical product optimization scheme, maximizing the heat release, which is extensible to any molecular composition. We find, for all three properties, that the machine-learned results significantly surpass standard rule-based schemes. Finally, we present an all <i>in silico</i> scheme for predicting detonation velocities, highlighting that this is almost as good as when experimental densities are used as input. In summary, we believe that this work is a major step toward the goal of accurately predicting detonation parameters by showing how to leverage the power of quantum chemistry for this task. Detonation ; Leverage (statistics) ; Experimental data ; Quantum ; Quantum chemistry ; Work (physics) ; Task (project management) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Ossi Laurila | Tiia Jacklin | Ouail Zakary | Perttu Lantto
Source: ChemRxiv (preprint, 2025-11-28)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Path Integral Molecular Dynamics and 13C NMR Simulations Unlock New Insights Into Quantum Effects in C60 Fullerene van der Waals force ; Molecular dynamics ; Ab initio ; Dispersion (optics) ; Fullerene ; Isotropy ; Carbon-13 NMR ; Quantum Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Thermal properties of materials
Authors: Ghanasham C. Sarode | Arvind M. Jagtap | Truti Pawase | Swati Jaiswal | Neha S. Joshi | Kasiprasad Mannepalli | Anant Sidhappa Kurhade
Source: Journal of Mines Metals and Fuels (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Predicting Performance of Sustainable Marine Materials The increasing demand for ecological materials in the marine sector has increased the quest for new materials which are environmental, economic, and durable. However, it is still challenging to predict long-term performances of those materials in complex ocean and corrosive environment based only on the conventional experimental methods. AI has become a revolutionizing factor in speeding up the material discovery and performance rating, life cycle optimization in marine applications. This review looks at the range of AI techniques—machine learning, deep learning, natural language processing and hybrid AI models—used to forecast the performance of sustainable marine materials: bio-composites, corrosion-resistant alloys, nanomaterials, and smart self-healing coatings. Supervised and unsupervised learning algorithms are employed to predict tensile strength and to study corrosion behavior for different environmental conditions. Deep learning models (e.g., CNNs, RNNs) are used to detect microstructural defects and used for time-series monitoring of degradation of protective coatings. Hybrid AI and physics models including FEA and CFD, combined with IoT sensors realize real-time structural health monitoring and the predictive maintenance for maritime assets. In addition, AI enables data-inspired decisions for material selection, design optimization, and environmental benefit. AI-based approaches and methods applied to the field of sustainable marine materials, have not only increased prediction efficiency, but also reduced development times; decreased testing costs, and are in line with the principles for a circular economy. In this review, state-of-the-art and limitations, e.g., data scarcity and interpretability, of AI-based technologies for accelerating marine material innovation are addressed and the future roadmap for exploiting the full potential of AI in marine material innovation and green maritime infrastructure is discussed. Major Findings: AI methodologies such as the development of ML and DL models, further improve the predictability of the corrosion immunity, mechanical strength, and long-term durability of sustainable marine materials. AI systems combining. Scarcity ; Artificial neural network ; Field (mathematics) ; Marine industry ; Sustainable development ; Sustainability ; Natural resource ; Deep learning ; Applications of artificial intelligence Machine Learning in Materials Science ; Fault Detection and Control Systems ; Maritime Transport Emissions and Efficiency
Authors: Stella F. Valle | Andrey Coatrini Soares | Mário Popolin Neto | Cauê Ribeiro | L. H. C. Mattoso | Osvaldo N. Oliveira
Source: Journal of Applied Polymer Science (article, 2025-07-09)
Field: Materials Science — Polymers and Plastics

Polysulfides From Inverse Vulcanization Used in Electronic Tongues for Heavy Metal Sensing ABSTRACT Polysulfides (PS) are versatile sulfur‐rich polymers synthesized via the inverse vulcanization of elemental sulfur, a simple one‐pot method that transforms an abundant, low‐cost residue into a functional material. In this work, we employed different PS structures as sensing units in an electronic tongue (e‐tongue) to detect the metal ions Hg 2+ , Ag + , and Fe 3+ . Detection was based on impedance spectroscopy, with capacitance data from three sensors processed using a multidimensional projection technique and machine learning. The e‐tongue distinguished the three metal ions at various concentrations, down to 1 nM for Hg 2+ , with a silhouette coefficient of 0.95. This high efficiency may be attributed to sulfur domains in PS acting as multiple active sites for metal sorption and binding. Using machine learning within the framework of multidimensional calibration space (MCS), the accuracy in distinguishing metal ion concentrations reached ~99%. Vulcanization ; Inverse ; Metal ; Polymer science ; Materials science ; Polymer chemistry ; Composite material ; Metallurgy ; Natural rubber ; Mathematics Synthesis and properties of polymers ; Conducting polymers and applications ; Epoxy Resin Curing Processes
Authors: Hiroaki Matsukawa | Katsuyuki Koizumi | Katsuto Otake
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of pure-component parameters of perturbed chain-statistical associating fluid theory equations of state using graph convolutional neural network-based modeling Representation (politics) ; Graph ; Molecular graph ; Convolutional neural network ; Property (philosophy) ; Range (aeronautics) ; Artificial neural network ; Physical system Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Material Dynamics and Properties
Authors: Mitsugu Araki | Shigeyuki Matsumoto | Biao Ma | Kei Terayama | Yasushi Okuno
Source: Biophysical Journal (article, 2025-02-01)
Field: Chemistry — Spectroscopy

BPS2025 - Deep learning approaches for protein dynamics extraction from cryo-EM maps and structure-based de novo molecular generation Molecular dynamics ; Extraction (chemistry) ; Dynamics (music) ; Deep learning ; Cryo-electron microscopy ; Computational biology ; Chemistry ; Artificial intelligence ; Biological system ; Computer science ; Biophysics ; Biology ; Physics ; Chromatography ; Computational chemistry Advanced NMR Techniques and Applications
Authors: Tae-Il Ri | Suk-Gyong Hwang | Jin-Song Kim | Kum-Chol Ri | Chol‐Jun Yu
Source: Applied Physics Letters (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Fast ionic conductivity by thermal treatment with ultralow electronic transport in solid-state electrolyte Na3YCl6 Improving the ionic and electronic conductivities of solid-state electrolytes (SSEs) is urgently needed to develop commercially viable all-solid-state batteries. Here, we provide atomistic insights into the electronic transport properties and Na ionic conductivity of the halide-based SSE Na3YCl6 (NYC) with a trigonal structure and propose a way for improving ionic conductivity by amorphization. Our ab initio calculations, employing a highly accurate hybrid functional and many-body method, reveal high electric and thermal insulating behavior of crystalline NYC. Using machine learning interatomic potential-based molecular dynamics simulations, we demonstrate low ionic conductivity at room temperature in the crystalline phase, but a significantly higher value of 0.29 mS/cm in amorphous NYC simulated by thermal treatment, highlighting that amorphization is an effective way for improving ionic conductivity. Ionic conductivity ; Ionic bonding ; Electrolyte ; Thermal conductivity ; Amorphous solid ; Fast ion conductor ; Conductivity ; Ab initio quantum chemistry methods Solid-state spectroscopy and crystallography ; Advanced Battery Materials and Technologies ; Advancements in Solid Oxide Fuel Cells
Authors: Nolan
M. Green (22572962) | Jamie L. B. Putnam (22673051) | Rachel I. Hammond (10682037) | Nicholas H. Angello (12213512) | James Planey (22572965) | Dylan Brandt (22673054) | José Andino Martinez (22673057) | Thomas Hummel (176177) | Bhagya Gunasekera (22572977) | Rachel Switzky (9928765) | Serenity Desmond (22572983) | Martin D. Burke (1280013)
Source: Figshare (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Illuminating the Interface of Blocc Chemistry andData Science: An Introduction to K‑Nearest Neighbor Analysisand K‑Medoids Clustering The discovery of new chemical functions is being accelerated by blocc chemistry for iterative carbon–carbon bond formation coupled to artificial intelligence (AI) and machine learning (ML) techniques; yet, these technologies are largely absent from introductory undergraduate courses. This lab emphasizes the utility of data science tools through the context of discovering chemical function. We propose that teaching blocc chemistry provides student scientists the ability to explore functional data from frontier research generated for the purpose of this labeven with minimal knowledge of organic chemistry. Here, we report the first laboratory experiment in a sequence designed to illuminate the AI-Chemistry interface. This first experiment teaches students the fundamental principles of K-Nearest Neighbor (KNN) analysis and K-Medoids clustering to predict unknown chemical functions through the lens of blocc chemistry. Cluster analysis ; Context (archaeology) ; Interface (matter) ; Sequence (biology) ; Frontier ; Chemistry education Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Nolan M. Green | Jamie L. B. Putnam | Rachel I. Hammond | Nicholas H. Angello | James Planey | Dylan Brandt | José M. Martínez | Thomas Hummel | Bhagya Gunasekera | Rachel Switzky | Serenity Desmond | Martin D. Burke
Source: Journal of Chemical Education (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Illuminating the Interface of Blocc Chemistry and Data Science: An Introduction to K-Nearest Neighbor Analysis and K-Medoids Clustering The discovery of new chemical functions is being accelerated by blocc chemistry for iterative carbon-carbon bond formation coupled to artificial intelligence (AI) and machine learning (ML) techniques; yet, these technologies are largely absent from introductory undergraduate courses. This lab emphasizes the utility of data science tools through the context of discovering chemical function. We propose that teaching blocc chemistry provides student scientists the ability to explore functional data from frontier research generated for the purpose of this labeven with minimal knowledge of organic chemistry. Here, we report the first laboratory experiment in a sequence designed to illuminate the AI-Chemistry interface. This first experiment teaches students the fundamental principles of K-Nearest Neighbor (KNN) analysis and K-Medoids clustering to predict unknown chemical functions through the lens of blocc chemistry. Cluster analysis ; Interface (matter) ; Context (archaeology) ; Chemistry education ; Frontier ; Sequence (biology) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Lukas Boden | Faras Brumand‐Poor | Linda Pleninger | Katharina Schmitz
Source: Physchem (article, 2025-04-08)
Field: Materials Science — Materials Chemistry

Enhancing Predictive Accuracy Under Data Scarcity: Modeling Molecular Interactions to Describe Sealing Material Compatibility with Bio-Hybrid Fuels Bio-hybrid fuels, chemically derived from sustainable raw materials and green energies, offer significant potential to reduce carbon dioxide emissions in the transport sector. However, when these fuels are used as drop-in replacements in internal combustion engines, material compatibility with common sealing materials is not always given. Within the cluster of excellence, “The Fuel Science Center (FSC)” at RWTH Aachen, experimental immersion tests were conducted on a limited set of fuel and sealing material combinations. Given the extensive range of possible fuel and sealing combinations, a data-based machine learning prediction framework was developed and validated to pre-select promising fuel candidates. Due to the limited number of samples, preliminary results indicate a need to expand the database. Since experimental investigations are time-consuming and costly, this work explores faster physics-motivated data generation approaches by modeling molecular interactions between fuel and sealing materials. Two modeling scales are employed. One calculates the intermolecular distance using density functional theory. The other uses Hansen solubility parameters, representing an abstract modeling of intermolecular forces. Both approaches are compared, and their limitations are assessed. Including the generated data in the prediction framework improves its accuracy. Compatibility (geochemistry) ; Scarcity ; Biochemical engineering ; Process engineering ; Environmental science ; Computer science ; Materials science ; Engineering ; Composite material Thermal and Kinetic Analysis ; Heat transfer and supercritical fluids ; Nuclear Materials and Properties