jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qian Chen | Valdas Vitartas | James R. Kermode | Reinhard J. Maurer
Source: npj Computational Materials (preprint, 2026-03-11)
Field: Materials Science — Materials Chemistry

Equivariant electronic Hamiltonian prediction with many-body message passing Abstract Machine learning surrogate models of Kohn-Sham Density Functional Theory Hamiltonians provide a powerful tool for accelerating the prediction of electronic properties of materials, such as electronic band structures and density of states. For large-scale applications, an ideal model would exhibit high generalization ability and computational efficiency. Here, we introduce the MACE-H graph neural network, which combines high body-order message passing with a node-order expansion to efficiently obtain all relevant O (3) irreducible representations. The model achieves high accuracy and computational efficiency and captures the full local chemical environment features of, currently, up to f orbital matrix interaction blocks. We demonstrate the model’s accuracy and transferability on several open materials benchmark datasets of two-dimensional materials and a new dataset for bulk gold, achieving sub-meV prediction errors on matrix elements and high accuracy on eigenvalues across all systems. We further analyze the interplay of high-body-order message passing and locality that makes this model a good candidate for high-throughput material screening. Locality ; Message passing ; Eigenvalues and eigenvectors ; Generalization ; Hamiltonian (control theory) ; Artificial neural network ; Diagonalizable matrix ; Computation ; Benchmark (surveying) Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Varsha Gautam
Source: International Journal of Physics and Applications (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Future directions in theoretical and computational spectroscopy of liquid crystals Liquid crystals have unique optical properties and molecular arrangements, making them suitable for applications involving photonics, display technology, and sensors. Thus, computational spectroscopy has become an important method for understanding the molecular interactions that contribute to the electrical and vibrational properties of both inorganic and organic LCs. Therefore, the current book presents advances in YH computational modelling, while also identifying and discussing challenges with computational methods. At the same time, the book discusses how high-throughput computational frameworks, hybrid quantum-classical methods, and machine learning help to predict and account for the spectroscopic properties of future generations of liquid crystalline materials with defined attributes. Ultimately, this discussion was created to assist in directing future efforts toward developing liquid crystalline materials with specified properties. Spectroscopy ; Liquid crystal ; Computational model ; Current (fluid) ; Infrared spectroscopy ; Computational simulation ; Molecular spectroscopy Liquid Crystal Research Advancements ; Molecular spectroscopy and chirality ; Crystallography and molecular interactions
Authors: Yuliang Guo | Xiaobo Sun | Xiaoli Xi | Zuoren Nie
Source: Computational Materials Science (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

The spontaneous internalization of electrostatic interactions by large atomic models in molten salts Machine learning interatomic potentials (MLIPs) have revolutionized molecular dynamics by combining deep learning efficiency with quantum mechanical accuracy. However, the “black-box” nature of neural networks often obscures the physical mechanisms by which these models fit complex potential energy surfaces (PES). This study investigates the correlation between local atomic environments and energy prediction behaviors using the pre-trained DPA2 model applied to NaCl-KCl and Na 2 WO 4 molten salt systems. Utilizing ab initio molecular dynamics (AIMD) data for fine-tuning, we demonstrate that the model spontaneously internalizes electrostatic laws despite the absence of explicit charge or oxidation state inputs. Statistical analysis reveals that for cations, higher energy contributions correlate with cation-dense regions (repulsion), while anion-dense regions lead to stabilization (attraction); the inverse is observed for anions. Furthermore, a strong correlation was found between model-predicted atomic energies and local formal charges calculated via Gaussian expansion. Our findings substantiate the physical interpretability of DPA2, showing that it transcends mathematical interpolation to extract the physical essence of interatomic interactions, providing critical validation for the development of advanced MLIPs. Can machine learning potentials capture electrostatic laws without explicit supervision? This study demonstrates that the pre-trained DPA2 model spontaneously internalizes Coulomb's Law from geometric topology in molten salts. By quantifying correlations between atomic energy contributions and ionic neighborhoods, we prove that deep learning transcends mathematical interpolation to extract fundamental physical essence with high fidelity. Internalization ; Molecular dynamics ; Electrostatics ; Electrostatic interaction ; Atomic force microscopy Material Dynamics and Properties ; Theoretical and Computational Physics ; Solidification and crystal growth phenomena
Authors: Anton Bochkarev | Yury Lysogorskiy | Aparna P. A. Subramanyam | Ralf Drautz | Danny Perez
Source: ArXiv.org (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Exploring the extremes: atomic basis for multi-elemental materials science under complex thermodynamic conditions Modern materials science has historically been founded on combining restricted subsets of the periodic table, favoring high-purity, few-element systems. However, the demands of an emerging circular economy, together with the need to understand materials behavior under planetary and industrial extremes, increasingly require mastering Mendeleev materials - chemically and structurally complex systems that span large portions of the periodic table. In these regimes, current universal machine-learning interatomic potentials often fail, largely due to systematic gaps in traditional training datasets that heavily emphasize low-energy, near-equilibrium structures. We address this limitation by introducing a chemistry-agnostic, information-entropy-maximization protocol for data generation. By decoupling structural sampling from thermodynamic bias, our approach provides a robust physical prior for atomic interactions across the entire periodic table, including regimes far from equilibrium and under extreme conditions. Training a Graph Atomic Cluster Expansion (GRACE) model on the resulting statistically maximized entropy (SMAX) dataset yields markedly improved robustness across a range of stringent benchmarks. These include large-strain phase transformations in tin, defect evolution in tungsten-based alloys, and catalytic reaction barrier prediction. More broadly, our approach establishes a scalable and principled methodology for navigating the vast chemical and configurational space relevant to future materials design. It enables a paradigm of discovery by simulation in which unbiased sampling protocols autonomously resolve emergent structures in multi-elemental mixtures-such as systems containing the nine most abundant elements in the Earth's crust-without reliance on a priori chemical assumptions. Scalability ; Chemical space ; A priori and a posteriori ; Robustness (evolution) ; Decoupling (probability) ; Complex system ; Entropy (arrow of time) ; Physical system Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Nikita I. Kolomoets | Daniil A. Boiko | Leonid V. Romashov | Kirill S. Kozlov | Evgeniy G. Gordeev | Alexey S. Galushko | Valentine P. Ananikov
Source: Angewandte Chemie (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Reaction Discovery Involving Digital co‐Expert with a Practical Application in Atom‐Economic Cycloaddition Abstract The discovery of new chemical transformations is central to advancing modern chemistry, yet conventional approaches often require months or years of extensive experimental screening. Here, we present a machine‐learning‐assisted and expert‐guided pipeline for reaction discovery applied to the search for atom‐economic cycloaddition reactions. Candidate reactions were generated from publicly available quantum chemical data, filtered through unsupervised machine learning, and clustered to reduce redundancy. A digital co‐expert then enabled rapid prioritization, after which human expertise provided final selection and experimental validation. This hybrid workflow is fully compatible with current laboratory infrastructure and addresses the most time‐consuming stage of reaction discovery, accelerating the expert screening bottleneck by approximately 180‐fold (from &gt; 1200 days to 7 days). Within ∼1 week, two novel cycloaddition reactions were identified and experimentally confirmed, yielding previously undescribed products. While fully autonomous robotic platforms represent a long‐term vision, their high cost and limited availability restrict immediate application. In contrast, our approach demonstrates the practicality of human‐AI collaboration for reaction discovery, combining computational screening, machine learning and expert knowledge to efficiently expand the accessible chemical space. Cycloaddition ; Bottleneck ; Workflow ; Pipeline (software) ; Quantum chemical ; Cheminformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Chuanlong Zeng | Z. Zhang | Yuqi Shi | Yufan Tang | Wei Zhang | Mingan Chen | Zhehuan Fan | Zunyun Fu | Zijian Han | Ruitong Luo | Jinghua Yu | Xinyu Jiang | Xia Sheng | Yufan Li | Jingtong Wang | Rui Zeng | Duanhua Cao | Xutong Li | Jiacheng Xiong | Hanyang Zhou | Mingyue Zheng
Source: ChemRxiv (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Structure-free generation of transition state conformations with Bayesian flow networks Transition state (TS) conformation prediction is crucial for understanding reaction mechanisms and guiding catalyst or enzyme design yet remains challenging due to the intrinsic instability and structural sensitivity of TSs. Conventional determination relies heavily on expert intuition and computationally intensive quantum chemistry methods. Existing 3D-input search approaches are sensitive to initial conformations, while other methods tend to yield high-variance and imprecise results, limiting their practical application. Here, we introduce TSBFN, a structurefree transition state conformation generator based on Bayesian flow networks, directly from reaction SMILES and graphs. TSBFN achieves state-of-the-art performance, outperforming other machine learning methods with a median structural deviation of 0.064 Å and a 93.4% intrinsic reaction coordinate (IRC) validation rate. Notably, TSBFN generates inherently compact conformational distributions that naturally coalesce into a handful of high-quality candidates per reaction, substantially mitigating the challenge of TS selection. Furthermore, the structure-free design demonstrates enhanced robustness and efficacy in challenging multi-component systems. In an out-of-distribution Claisen rearrangement case, TSBFN efficiently generated two distinct classes of mechanistically relevant and valid TSs at millisecond speed, achieving 100% IRC validation and underscoring its potential for automated reaction exploration. Intuition ; Robustness (evolution) ; Millisecond ; Limiting ; Bayesian probability ; Quantum ; Bayesian network ; Reaction coordinate Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Qingya Zhu | Shufan Feng | Yuan Feng | Chi Ma | Zijie Chu | Katherine Tang | Chun Mao | Ya Guan | Sharon Gerecht | Mimi Wan
Source: Chemical Engineering Journal (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Application of machine learning in the design of biomedical nanomaterials Interpretability ; Process (computing) ; Field (mathematics) ; Deep learning ; Translation (biology) Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Computational Drug Discovery Methods
Authors: Vladimir Paunović | Georg Liesche | Kai Sundmacher | Jeroen A. van Bokhoven
Source: Nature Catalysis (article, 2026-03-23)
Field: Chemistry — Inorganic Chemistry

Exploring the landscape of methanol-to-hydrocarbons conversion catalysts Context (archaeology) ; Leverage (statistics) ; Commercialization ; Catalysis ; Fossil fuel ; Renewable energy ; Production (economics) Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Ivan A. Bespalov | Ivan Zlobin | Mark Yu. Schneider | Gleb M. Averochkin | Eugeny Alexandrov
Source: ChemRxiv (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Ensuring Physicochemical Fidelity of Generated Polymers with PoGE The computational design of polymers with targeted macroscopic properties is frequently limited by the tendency of generative models to produce structures that deviate from experimental reality. Standard evaluation metrics, originally derived for small drug-like molecules, often do not adequately capture the physicochemical constraints unique to polymeric materials. To address this, we introduce PoGE (Polymer Generation and Evaluation), a transformer-based framework trained on a hybrid corpus of generated and experimentally validated polymer representations. We propose a physics-driven evaluation suite that utilizes Wasserstein distance to quantify the alignment between generated and experimental property distributions, replacing traditional fragment-based scoring. PoGE demonstrates superior fidelity to the physicochemical attributes of real-world polymers-specifically regarding molecular weight, topological polar surface area, and chain flexibility-when compared to existing recurrent neural network and conditional generation baselines. Notably, this alignment is achieved through unconditional sequence modeling, indicating that the architecture implicitly captures the complex structural rules governing polymer synthesis without requiring explicit descriptor conditioning. By providing a rigorously validated pre-training corpus and a physics-informed benchmarking framework, this work establishes a reliable foundation for the on-demand inverse design of synthesizable materials for advanced applications. Fidelity ; Benchmarking ; Suite ; Polymer ; Inverse ; Artificial neural network ; Sequence (biology) ; High fidelity Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Omkar Shashank Sathe | Shreyas Bhat Brahmavar | Mrunmay Mohan Shelar | Raviprasad Aduri
Source: Journal of Chemical Information and Modeling (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

QPred: A Quantum Mechanical Property Predictor for Small Molecules Predicting the physicochemical properties of molecules is a cornerstone of computational chemistry and drug discovery. While quantum mechanical methods provide high accuracy, their computational expense limits their use in high-throughput screening. To bridge this gap, we introduce a novel, disentangled deep learning architecture that adaptively predicts molecular properties by leveraging either 2D topological graphs or 3D geometric information. Our framework consists of two specialized models: a Message Passing Neural Network (MPNN) enhanced with cycle-based semimaster nodes for robust 2D feature extraction, and an equivariant network with a disentangled update mechanism for high-fidelity 3D-aware predictions. The architecture's built-in hierarchical attention mechanism provides interpretability by highlighting salient atomic and substructure features. This adaptive, high-performance, and interpretable framework offers a versatile solution for accelerating molecular discovery, irrespective of the data dimensionality. Interpretability ; Substructure ; Salient ; Quantum ; Feature (linguistics) ; Property (philosophy) ; Mechanism (biology) ; Equivariant map ; Bridge (graph theory) ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Divya Bajpai Tripathy | Subhalaxmi Pradhan | Pooja Agarwal
Source: Inorganica Chimica Acta (article, 2026-02-14)
Field: Chemistry — Inorganic Chemistry

Heterometallic coordination clusters and frameworks incorporating 3d–4f, 4d–4f, 5d–4f, and f-block metal combinations: Structures, properties, and applications Magnetism ; Quantum dot ; Metal-organic framework ; Quantum ; Ligand (biochemistry) ; Function (biology) ; Catalysis ; Work (physics) Metal-Organic Frameworks: Synthesis and Applications ; Magnetism in coordination complexes ; Inorganic Chemistry and Materials
Authors: Ivan A. Bespalov | Ivan Zlobin | Mark Yu. Schneider | Gleb M. Averochkin | Eugeny Alexandrov
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Ensuring Physicochemical Fidelity of Generated Polymers with PoGE Flexibility (engineering) ; Fidelity ; Benchmarking ; Polymer ; Suite ; Sequence (biology) ; Artificial neural network ; Inverse ; Representation (politics) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Marcello Sega | Matteo Salvalaglio
Source: Annual Review of Chemical and Biomolecular Engineering (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Molecular Understanding of Free-Energy Landscapes Free-energy surfaces (FESs) offer a unifying framework for understanding molecular-level structures, transformations, and thermodynamic stability. They distill the complexity of atomistic simulations into interpretable landscapes of metastable states, bridging molecular-level detail with macroscopic observables. This review provides researchers in molecular simulations, computational physical chemistry, and chemical engineering with a conceptual and practical guide to computing and interpreting FESs, from their statistical-mechanical foundations to modern machine learning approaches that are transforming the sampling, representation, and analysis of molecular systems. Bridging (networking) ; Metastability ; Molecular dynamics ; Computational model Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Matthew Ball | Felix A. Faber | Cassie Pratley | Thierry Kogej | Dragos Horvath | Alexandre Varnek | Giulia Bergonzini | Christof M. Jäger | Mikhail Kabeshov
Source: ChemRxiv (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Exploring Reaction Condition Space with Expert-Informed Representation Learning The selection of appropriate reaction conditions is paramount in chemical synthesis and can greatly impact the success of experimental campaigns. Machine learning models for reaction outcome prediction have focused primarily on reactant representations, whereas the representation of reaction conditions (the chemical components defining the reaction environment, e.g. activators, bases or solvents) has received comparatively less attention. Here, we present a novel machine learning framework designed to generate chemically relevant representations for sets of conditions. We evaluate these representations using literature Amide Coupling High-Throughput Experimentation (HTE) datasets, testing whether distances in embedding space reflect local yield similarity, physical-property trends, and experimentally useful condition diversity. Our results demonstrate that the learnt representations capture reactivity-relevant trends and support downstream tasks including condition visualization, diversity-aware HTE plate design, and selection of alternative conditions. Embedding ; Representation (politics) ; Outcome (game theory) ; Space (punctuation) ; Selection (genetic algorithm) ; Yield (engineering) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Adarsh V. Kalikadien | Evgeny A. Pidko
Source: The Journal of Physical Chemistry A (article, 2026-02-18)
Field: Materials Science — Materials Chemistry

Performance of Meta’s Universal Model for Atoms across the Conformational and Configurational Space of Diverse Transition-Metal Catalysts Machine Learning Interatomic Potentials (MLIPs) promise to transform computational catalysis by delivering near-density functional theory (DFT) accuracy at a fraction of the computational cost. Here, we evaluate the Universal Machine Learning Potential for Atoms (UMA) on two data sets of transition-metal complexes. UMA enables high-throughput evaluations in seconds per structure on consumer-grade GPUs. Analysis of per-ligand Spearman rank correlations (ρ > 0.6, <i>p</i> < 0.05) reveals variability in ranking reliability that is not captured by aggregate metrics such as <i>R</i><sup>2</sup> or RMSE. However, these inaccuracies are shown to mainly occur in the near-DFT accuracy regime where these complexes are practically indistinguishable. For square-planar Ni complexes, reliable rankings are obtained for 84% of ligands in rigid Ni-Cl<sub>2</sub> complexes and drop to 53% for flexible asymmetric coordination environments, particularly only when conformers differ by <2 kJ/mol. Data set 2 shows a similar trend, with 61% and 44% reliability for Ru(II) and Mn(I) complexes, respectively, and, as expected, challenges for fluxional systems with small (<5 kJ/mol) relative energy gaps. These findings highlight the promise of MLIPs for both rigid, well-defined systems and highly flexible or fluxional catalysts, while underscoring the need to combine the speed of ML with validation and domain expertise to ensure robust and meaningful chemical insights. Reliability (semiconductor) ; Ranking (information retrieval) ; Domain (mathematical analysis) ; Set (abstract data type) ; Space (punctuation) ; Rank (graph theory) ; Aggregate (composite) ; Conformational isomerism ; Fraction (chemistry) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; CO2 Reduction Techniques and Catalysts
Authors: Abdulkarem H.M. Almawgani | Sultan Alkhteeb | Abdulrhman Alshaabani | Jacob Wekalao | Affaq Qamar | Amuthakkannan Rajakannu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2026-04-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A multilayer 2D material plasmonic metasurface terahertz biosensor with machine learning optimization for high-sensitivity tuberculosis detection Terahertz radiation ; Plasmon ; Biosensor ; Mycobacterium tuberculosis ; Nanoparticle Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research