jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenhao Gao | Priyanka Raghavan | Ron Shprints | Connor W. Coley
Source: Journal of the American Chemical Society (article, 2025-03-02)
Field: Materials Science — Materials Chemistry

Revealing the Relationship between Publication Bias and Chemical Reactivity with Contrastive Learning A synthetic method's substrate tolerance and generality are often showcased in a "substrate scope" table. However, substrate selection exhibits a frequently discussed publication bias: unsuccessful experiments or low-yielding results are rarely reported. In this work, we explore more deeply the relationship between such a publication bias and chemical reactivity beyond the simple analysis of yield distributions using a novel neural network training strategy, <i>substrate scope contrastive learning</i>. By treating reported substrates as positive samples and nonreported substrates as negative samples, our contrastive learning strategy teaches a model to group molecules within a numerical embedding space, based on historical trends in published substrate scope tables. Training on 20,798 aryl halides in the CAS Content Collection<sup>TM</sup>, spanning thousands of publications from 2010 to 2015, we demonstrate that the learned embeddings exhibit a correlation with physical organic reactivity descriptors through both intuitive visualizations and quantitative regression analyses. Additionally, these embeddings are applicable to various reaction modeling tasks like yield prediction and regioselectivity prediction, underscoring the potential to use historical reaction data as a pretraining task. This work not only presents a chemistry-specific machine learning training strategy to learn from literature data in a new way but also represents a unique approach to uncover trends in chemical reactivity reflected by trends in substrate selection in publications. Chemistry ; Reactivity (psychology) ; Psychology Machine Learning in Materials Science ; Educational Strategies and Epistemologies ; Computational Drug Discovery Methods
Authors: Omar Abou El Kheir | Marco Bernasconi
Source: Advanced Electronic Materials (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Million‐Atom Simulation of the Set Process in Phase Change Memories at the Real Device Scale Abstract Phase change materials are exploited in several enabling technologies such as storage class memories, neuromorphic devices and memories embedded in microcontrollers. A key functional property for these applications is the fast crystal nucleation and growth in the supercool liquid phase. Over the last decade, atomistic simulations based on density functional theory (DFT) have provided crucial insights on the early stage of this process. These simulations are, however, restricted to a few hundred atoms for at most a few ns. More recently, the scope of the DFT simulations is greatly extended by leveraging on machine learning techniques. In this study, it is showed that the exploitation of a recently devised neural network potential for the prototypical phase change compound Ge 2 Sb 2 Te 5 , allows simulating the crystallization process in a multimillion atom model at the length and time scales of the real memory devices. The simulations provide a vivid atomistic picture of the subtle interplay between crystal nucleation and crystal growth from the crystal/amorphous rim. Moreover, the simulations have allowed quantifying the distribution of point defects that controls electronic transport, in a very large crystallite grown at the real conditions of the set process of the device. Materials science ; Scale (ratio) ; Process (computing) ; Atom (system on chip) ; Set (abstract data type) ; Phase (matter) ; Phase change ; Optoelectronics ; Engineering physics ; Computer science ; Embedded system ; Operating system ; Engineering ; Quantum mechanics ; Physics Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Phase-change materials and chalcogenides
Authors: Kanika Raj | Ramdevsinh Jhala | S. Sujai | Binayak Pattanayak | Jitendra Singh | Parveen Kumar | M. Kaladhar | Rahul Singh | Yashwant Singh Bisht | Anurag Gautam
Source: Journal of Molecular Structure (review, 2025-08-05)
Field: Materials Science — Materials Chemistry

A comprehensive review on the MXene-based catalysts for hydrogen generation via water splitting processes Chemistry ; Water splitting ; Catalysis ; Hydrogen ; Nanotechnology ; Hydrogen production ; Photocatalysis ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Thomas Michael Pruyn | Amro Aswad | Sartaaj Takrim Khan | Ju Huang | Robert Black | Seyed Mohamad Moosavi
Source: Journal of the American Chemical Society (article, 2025-11-10)
Field: Chemistry — Inorganic Chemistry

MOF-ChemUnity: Literature-Informed Large Language Models for Metal–Organic Framework Research Artificial intelligence (AI) is transforming research in metal-organic frameworks (MOFs), where models trained on structured computational data routinely predict new materials and optimize their properties. This raises a central question: What if we could leverage the full breadth of MOF knowledge, not just structured data sets, but also the scientific literature? For researchers, the literature remains the primary source of knowledge, yet much of its content, including experimental data and expert insight, remains underutilized by AI systems. We introduce MOF-ChemUnity, a structured, extensible, and scalable knowledge graph that unifies MOF data by linking literature-derived insights to crystal structures and computational data sets. By disambiguating MOF names in the literature and connecting them to crystal structures in the Cambridge Structural Database, MOF-ChemUnity unifies experimental and computational sources and enables cross-document knowledge extraction and linking. We showcase how this enables multiproperty machine learning across simulated and experimental data, compilation of complete synthesis records for individual compounds by aggregating information across multiple publications, and expert-guided materials recommendations via structure-based machine learning descriptors for pore geometry and chemistry. When used as a knowledge source to augment large language models (LLMs), MOF-ChemUnity enables a literature-informed AI assistant that operates over the full scope of MOF knowledge. Expert evaluations show improved accuracy, interpretability, and trustworthiness across tasks such as retrieval, inference of structure-property relationships, and materials recommendation, outperforming standard LLMs. This work lays the foundation for literature-informed materials discovery, enabling both scientists and AI systems to reason over the full existing knowledge in a new way. Leverage (statistics) ; Inference ; Knowledge graph ; Scalability ; Trustworthiness ; Applications of artificial intelligence ; Knowledge base ; Scope (computer science) ; Information extraction ; Graph Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Juan Santiago Grassano | Ignacio Pickering | Adrián E. Roitberg | Darío A. Estrı́n | Jonathan A. Semelak
Source: Chemical Physics Reviews (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

From QM/MM to ML/MM: A new era in multiscale modeling Hybrid machine-learning/molecular-mechanics (ML/MM) methods extend the classical QM/MM paradigm by replacing the quantum description with neural network interatomic potentials trained to reproduce accurately quantum-mechanical (QM) results. By describing only the chemically active region with ML and the surrounding environment with molecular mechanics (MM), ML/MM models achieve near-QM/MM fidelity at a fraction of the computational cost, enabling routine simulation of reaction mechanisms, vibrational spectra, and binding free energies in complex biological or condensed-phase environments. The key challenge lies in coupling the ML and MM regions, a task addressed through three main strategies: (1) mechanical embedding (ME), where ML regions interact with fixed MM charges via classical electrostatics; (2) polarization-corrected mechanical embedding (PCME), where a vacuum-trained ML potential is supplemented post hoc with electrostatic corrections; and (3) environment-integrated embedding (EIE), where ML potentials are trained with explicit inclusion of MM-derived fields, enhancing accuracy but requiring specialized data. Since ML/MM builds on the scaffolding of QM/MM, most proposed coupling strategies rely heavily on electrostatics, polarization, and other physicochemical concepts, and the development and analysis of ML/MM schemes sits naturally at the intersection of physical chemistry and modern data science. This review surveys the conceptual foundations of ML/MM schemes, classifies existing implementations, and highlights key applications and open challenges, providing a critical snapshot of the current state-of-the-art and positioning ML/MM not merely as a computational alternative but as the natural evolution of QM/MM toward data-driven, scalable multiscale modeling. Multiscale modeling ; Embedding ; Scalability ; Key (lock) ; Molecular dynamics ; Computational model ; Snapshot (computer storage) ; Artificial neural network ; Representation (politics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Burcu Oral | Rüveyda Karakoyun | Ecem Temelkaya Bilgin | Ramazan Yıldırım
Source: International Journal of Hydrogen Energy (article, 2025-04-06)
Field: Materials Science — Materials Chemistry

Machine learning analysis of photocatalytic glycerol reforming for hydrogen production Hydrogen production ; Photocatalysis ; Glycerol ; Production (economics) ; Hydrogen ; Chemical engineering ; Environmental science ; Process engineering ; Pulp and paper industry ; Materials science ; Chemistry ; Catalysis ; Economics ; Engineering ; Organic chemistry ; Macroeconomics Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Advanced Photocatalysis Techniques
Authors: Xiaohua Tian | Yulin Pan | Jian Li | Xiangyu Tong | Hong-Xing Li | Wenbin Zhao | Haipo Zhang | Changlong Tan
Source: Journal of Alloys and Compounds (article, 2025-02-21)
Field: Materials Science — Materials Chemistry

Accelerated design of a novel wide thermal hysteresis NiTi-based shape memory alloy based on interpretable information machine learning Shape-memory alloy ; Nickel titanium ; Hysteresis ; Materials science ; Alloy ; Thermal ; Computer science ; Metallurgy ; Artificial intelligence ; Thermodynamics ; Physics ; Condensed matter physics Shape Memory Alloy Transformations ; Creativity in Education and Neuroscience
Authors: Xiaoye Zhou | Hong‐Hui Wu | Meisa Zhou | Lifei Wang | Turab Lookman | Xinping Mao
Source: Acta Materialia (article, 2025-06-04)
Field: Materials Science — Metals and Alloys

Enhanced hydrogen embrittlement resistance of FeCoNiCrMn multi-principal element alloys via local chemical ordering and grain boundary segregation Materials science ; Grain boundary ; Hydrogen embrittlement ; Embrittlement ; Metallurgy ; Hydrogen ; Microstructure ; Grain boundary strengthening ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; High Temperature Alloys and Creep
Authors: Ping Gao | Xin Jiang | Jinyu Li | Julien Nicolas | Tâp Ha‐Duong
Source: Advanced Healthcare Materials (review, 2025-09-26)
Field: Materials Science — Biomaterials

Molecular Simulations of Polymer‐based Drug Nanocarriers: From Physical and Structural Properties to Controlled Release Polymer-based drug delivery systems have been extensively studied to overcome the limitations of free drug administration (e.g., poor solubility and stability, rapid degradation and early metabolization, short plasma half-life, low therapeutic efficacy, and occurrence of side effects). Although the vast majority of these drug delivery systems are developed using the traditional time- and resource-intensive trial-and-error method, computational techniques have received considerable attention in order to facilitate and accelerate their understanding and development. In this review, several computational techniques is presented that are commonly used to study polymer-based drug delivery systems. Then, this is discussed several computational investigations of the self-assembly and supramolecular organization of polymer nanocarriers for drug delivery applications, including drug-loaded polymer micelles and polymer prodrug nanoparticles. How modeling approaches can rationalize the drug loading and release from polymer drug delivery systems is further examined, including studies which aim to better understand how physical, chemical, or biological stimuli can trigger the drug release. Machine learning possibilities for extending physics-based molecular simulation efficiency and predictive power have also been briefly discussed. Nanocarriers ; Drug delivery ; Drug ; Controlled release ; Prodrug ; Micelle ; Polymer Nanoparticle-Based Drug Delivery ; Advanced Polymer Synthesis and Characterization ; Polymer Nanocomposites and Properties
Authors: Shubhang Goswami | Scott Jensen | Yubo Yang | Markus Holzmann | Carlo Pierleoni | David M. Ceperley
Source: The Journal of Chemical Physics (article, 2025-02-05)
Field: Materials Science — Materials Chemistry

High temperature melting of dense molecular hydrogen from machine-learning interatomic potentials trained on quantum Monte Carlo We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this newly trained model, we emphasize the importance of accurate total energies in the training. We integrate a two phase method for estimating the melting temperature with estimates from the Clausius-Clapeyron relation to provide a more accurate melting curve from the model. We make detailed predictions of the melting temperature, solid and liquid volumes, latent heat, and internal energy from 50 to 180 GPa for both classical hydrogen and quantum hydrogen. At pressures of roughly 173 GPa and 1635 K, we observe molecular dissociation in the liquid phase. We compare with previous simulations and experimental measurements. Monte Carlo method ; Dissociation (chemistry) ; Latent heat ; Thermodynamics ; Hydrogen ; Melting temperature ; Quantum ; Quantum Monte Carlo ; Melting point ; Melting curve analysis ; Internal energy ; Materials science ; Phase transition ; Statistical physics ; Liquid hydrogen ; Chemistry ; Physics ; Physical chemistry ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Phase Equilibria and Thermodynamics
Authors: Jiayin Li | Jing Xu | Evgeny V. Rebrov | Annemie Bogaerts
Source: Chemical Engineering Journal (article, 2025-01-30)
Field: Materials Science — Materials Chemistry

Machine learning-based prediction and optimization of plasma-catalytic dry reforming of methane in a dielectric barrier discharge reactor Dielectric barrier discharge ; Methane ; Carbon dioxide reforming ; Plasma ; Catalysis ; Chemical engineering ; Dielectric ; Materials science ; Methane reformer ; Nuclear engineering ; Environmental science ; Chemistry ; Waste management ; Engineering ; Organic chemistry ; Optoelectronics ; Syngas ; Physics ; Steam reforming Catalytic Processes in Materials Science ; Catalysts for Methane Reforming ; Catalysis and Oxidation Reactions
Authors: Chengwei Yang | Chao Yang | Yunxia Liang | Hongxia Yan | Aodi Zhang | Guixian Ge | Wentao Wang | Pengfei Ou
Source: Materials Chemistry Frontiers (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine-learning enables nitrogen reduction reaction on transition metal doped C<sub>3</sub>B by controlling the charge states The charge transfer and d-band center are key factors affecting the NRR performance of TM@C 3 B, both of which can be modulated by charge states. Thus, use of charge states in modeling electrochemical reactions represents a new design method. Doping ; Materials science ; Nitrogen ; Charge (physics) ; State (computer science) ; Reduction (mathematics) ; Transition metal ; Engineering physics ; Inorganic chemistry ; Nanotechnology ; Chemistry ; Computer science ; Optoelectronics ; Catalysis ; Physics ; Organic chemistry ; Mathematics ; Algorithm ; Quantum mechanics Machine Learning in Materials Science
Authors: Kartikeya Sharma | Antoine Loew | Haiyuan Wang | Fredrik Nilsson | Manjari Jain | Miguel A. L. Marques | Kristian S. Thygesen
Source: npj Computational Materials (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

Accelerating point defect photo-emission calculations with machine learning interatomic potentials Abstract We introduce a computational framework leveraging universal machine learning interatomic potentials (MLIPs) to dramatically accelerate the calculation of photoluminescence (PL) spectra of atomic or molecular emitters with ab initio accuracy. By replacing the costly density functional theory (DFT) computation of phonon modes with much faster MLIP phonon mode calculations, our approach achieves speed improvements exceeding an order of magnitude with minimal precision loss. We benchmark the approach using a dataset comprising ab initio emission spectra of 791 color centers spanning various types of crystal point defects in different charge and magnetic states. The method is also applied to a molecular emitter adsorbed on a hexagonal boron nitride surface. Across all the systems, we find excellent agreement for both the Huang-Rhys factor and the PL lineshapes. This application of universal MLIPs bridges the gap between computational efficiency and spectroscopic fidelity, opening pathways to high-throughput screening of defect-engineered materials. Our work not only demonstrates accelerated calculation of PL spectra with DFT accuracy, but also makes such calculations tractable for more complex materials. Phonon ; Ab initio ; Interatomic potential ; Spectral line ; Density functional theory ; Ab initio quantum chemistry methods ; Computation ; Benchmark (surveying) Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Luminescence and Fluorescent Materials
Authors: Afshin Farahbakhsh | Javad Mohebbi Najm Abad | Amin Hekmatmanesh | Heikki Handroos
Source: Battery energy (article, 2025-01-08)
Field: Chemistry — Electrochemistry

Evaluating the Performance of Ethanol Electrochemical Nanobiosensor Through Machine for Predictive Analysis of Electric Current in Self‐Powered Biosensors ABSTRACT In this study, the focus is on ethanol nano biosensors based on alcohol oxidase (AOX) enzymatic reactions and the feasibility of generating electric current for biobatteries. The aim is to convert the latent energy in ethanol into electrical energy through the enzymatic oxidation process in the presence of an AOX enzyme. The release of electrons and the creation of a potential difference make the use of ethanol as a biofuel cell/self‐power biosensor in biologically sensitive systems feasible. To achieve this, glassy carbon electrodes were modified with gold nanoparticles to enhance conductivity, and the AOX enzyme was immobilized on the working electrode. The current generated through the enzymatic process was measured in various pH and analyte concentration conditions. Afterward, machine‐learning models, including multilayer perceptron (MLP), deep neural network, decision tree, and random forest, were employed to assess the impact of parameters on electric current generation, evaluate the error rate, and compare the results. The results indicated that the MLP model was the most suitable method for predicting the electric current produced under different pH, temperature, and ethanol concentration values. These findings can be utilized to identify optimal conditions and increase the current output for use as a reliable energy source in self‐powered biosensors. In conclusion, this study suggests a promising way to generate electricity by oxidizing ethanol with the AOX enzyme. The use of machine learning to analyze experimental data has provided insight into optimal conditions for maximizing electric current output for developing sustainable energy sources in biologically sensitive systems and biobattery technology. Current (fluid) ; Biosensor ; Nanotechnology ; Materials science ; Engineering ; Electrical engineering Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Neuroscience and Neural Engineering
Authors: Jiu-Ning Wang | Wei Xu | Hao Ma | Qasim Qasim | Wanglai Cen
Source: International Journal of Hydrogen Energy (article, 2025-02-02)
Field: Materials Science — Materials Chemistry

Screened (Co, Rh, Ir)–N4 graphene single atom catalysts for hydrogen production via tunable surrounding coordination shell: Computation and machine learning Theoretical calculation is an efficient and cost-effective method to design single-atom catalysts (SACs), which can provide an important guidance for experimental synthesis. Although the usage of machine learning (ML) can effectively reduce the computational costs, obtaining suitable ML algorithms and input features for design of the SACs doped with non-metal atoms remains a challenge. Herein, The hydrogen evolution reaction (HER) activity of nitrogen-doped (Co,Rh,Ir)–N4-graphene has been thoroughly determined by the calculation and ML methods . The calculation results reveal that Ir–N4 SACs doped with either one or two nitrogen atoms show a higher proportion (75% and 78%) that exhibits the high HER activity (|△G ∗H | ≤ 0.1 eV) in acidic media. This is attributed to the stronger interaction between hydrogen atoms and Ir atoms than Co and Rh atoms, as revealed by the electronic structure analysis. In addition, the optimal ML algorithm and input features, selected from six machine algorithm and two types of features, are used to predict the HER activity of Ir–N4–N3 using a dataset calculated from 20% of the total 645 possible structures. The remaining 80% of the data are successfully predicted using the ML method, saving approximately 200,000 times compared to the theoretical calculations. Finally, the results indicate that 53% of Ir–N4–N3 SACs exhibit |△G ∗H | ≤ 0.1 eV, indicating the N-doped Ir–N4 SACs have great application potential as HER catalysts. This work not only provides a method for rapidly designing metal-N4-graphene-based SACs but also offers strong guidance for their experimental synthesis. • Nitrogen-doped (Co,Rh,Ir)–N4 single-atom catalysts were studied for H 2 evolution. • Optimal machine learning algorithm and input features were screened. • Hydrogen evolution activities of 516 Ir–N4–N3 catalysts were predicted. • Ir–N4–N(0–3) with high hydrogen evolution activity was proved to be at least 53%. Graphene ; Catalysis ; Computation ; Hydrogen production ; Shell (structure) ; Hydrogen atom ; Hydrogen ; Atom (system on chip) ; Materials science ; Chemistry ; Nanotechnology ; Computer science ; Parallel computing ; Organic chemistry ; Composite material ; Algorithm Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies