jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cohen, Jaron | Hasson, Alexander G. | Tanovic, Sara
Source: ArXiv.org (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Unveiling Latent Knowledge in Chemistry Language Models through Sparse Autoencoders Since the advent of machine learning, interpretability has remained a persistent challenge, becoming increasingly urgent as generative models support high-stakes applications in drug and material discovery. Recent advances in large language model (LLM) architectures have yielded chemistry language models (CLMs) with impressive capabilities in molecular property prediction and molecular generation. However, how these models internally represent chemical knowledge remains poorly understood. In this work, we extend sparse autoencoder techniques to uncover and examine interpretable features within CLMs. Applying our methodology to the Foundation Models for Materials (FM4M) SMI-TED chemistry foundation model, we extract semantically meaningful latent features and analyse their activation patterns across diverse molecular datasets. Our findings reveal that these models encode a rich landscape of chemical concepts. We identify correlations between specific latent features and distinct domains of chemical knowledge, including structural motifs, physicochemical properties, and pharmacological drug classes. Our approach provides a generalisable framework for uncovering latent knowledge in chemistry-focused AI systems. This work has implications for both foundational understanding and practical deployment; with the potential to accelerate computational chemistry research. Interpretability ; Autoencoder ; Foundation (evidence) ; Generative grammar ; Property (philosophy) ; Computational model ; ENCODE ; Language model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Shengguang Wang (626910) | Han Chau (21353106) | Stephen Kristy (21156928) | Brooklyne Ariana Thompson (21353109) | Jason P. Malizia (13805466) | Rebecca Fushimi (1889575)
Source: Figshare (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

MachineLearning-Assisted Recovery of Delicate KineticInformation from Transient Reactor Experiments Identifying active sites and their roles in chemical reaction steps remains a vital challenge in heterogeneous catalysis. Transient experiments offer a unique way to probe active sites and distinguish subtle kinetic features. Although physics-based analysis methods may be well-developed, they can be highly susceptible to experimental noise, and smoothing methods may erase or even distort important features; a smooth curve is not always the best curve. We demonstrate a new workflow for the direct interpretation of intrinsic kinetic information from exit flux curves measured in transient reactor experiments. This workflow contains three artificial neural networks (ANNs), including a noise reducer, a concentration predictor, and a rate predictor to analyze experimental data, followed by the virtual TAP (VTAP) physics-based reactor model and density functional theory (DFT) calculations of adsorption energies on specific sites. We use this workflow to analyze the data from experiments titrating Pt/Al<sub>2</sub>O<sub>3</sub> and Pt/SiO<sub>2</sub> catalysts with carbon monoxide (CO) in the temporal analysis of products (TAP) reactor. Our workflow separates the time-evolving chemical reaction and mass transfer information contained in the TAP pulse response. The existence of strong- and weak-binding sites on the Pt/Al<sub>2</sub>O<sub>3</sub> catalyst is observed in the catalyst titration experiment in the transient reactor. The structures of the strong- and weak-binding sites are then identified by using DFT calculations. We find that the Pt/SiO<sub>2</sub> catalyst has only strong-binding sites, which aligns with the inactive support effect of SiO<sub>2</sub>. We demonstrate how machine learning methods provide unique insights with high-resolution data analysis that cannot be achieved by using state-of-the-art physics-based methods. Transient (computer programming) ; Workflow ; Smoothing ; Kinetic energy ; Catalysis ; Steady state (chemistry) ; Carbon monoxide ; Noise (video) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Raúl Pérez‐Soto | Mihai V. Popescu | Sabari Kumar | Leticia Adao Gomes | Changyeob Lee | Elijah Shore | Steven A. Lopez | Robert S. Paton | Seonah Kim
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A fragment based approach towards curating, comparing and developing machine learning models applied in photochemistry The development of graph neural networks for predicting molecular properties has garnered significant attention, as it enables the correlation of quickly computable atomic and bond descriptors with overall molecular properties. With the rising interest in photochemistry and photocatalysis as sustainable alternatives to thermal reactions, curation of virtual databases of computed photophysical properties for training of machine learning models has become popular. Unfortunately, current efforts fail to consider the exciton localization onto different chromophores of the same molecule, leading to potentially large prediction errors. Here we describe a molecular fragmentation strategy that can be used to overcome this limitation, while also providing a way to compare structural diversity between different libraries. Using a newly generated database of 46 432 adiabatic S<sub>0</sub>-T<sub>1</sub> energy gaps (ALFAST-DB), we compare its diversity with two datasets from the literature and demonstrate that a fragment-based delta learning approach improves model generalizability while achieving accuracies comparable to those of traditional message passing graph neural network architectures (MPGNN). Artificial neural network ; Generalizability theory ; Chromophore ; Graph ; Fragmentation (computing) ; Adiabatic process ; Categorization ; Deep learning ; Virtual screening Machine Learning in Materials Science
Authors: Ailian Wang | Xiaoyan Fu | Ping Ji | Jianhui Ma | Zhongwei Zhang | P. Hu
Source: Journal of Materials Chemistry A (article, 2025-12-06)
Field: Materials Science — Materials Chemistry

Accelerating solid-state battery design: predicting ionic conductivity with machine learning potentials Achieving high ionic conductivity in solid-state electrolytes (SSEs) is critical for next-generation batteries, yet exploring the vast chemical space is hindered by the computational expense of first-principles methods. Ionic conductivity ; Conductivity ; Work (physics) ; Battery (electricity) ; Artificial neural network ; Electrolyte Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Eric Kolor | Edoardo Magnone | M.M. Harussani | Mirza H. K. Rubel | Sasipa Boonyubol | Köichi Mikami | Jeffrey S. Cross
Source: ArXiv.org (preprint, 2025-11-12)
Field: Materials Science — Materials Chemistry

Hydrogen permeability prediction in palladium alloys and virtual screening of B2-phase stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning We present a forward prediction material screening framework designed to discover Pd-Cu alloys with improved B2 phase stability, thereby unlocking simultaneous $H_2$ generation and utilization. First, we trained CatBoost models with literature-derived Pd alloy data to predict $H_2$ permeability from composition and testing conditions. We evaluated fractional, composition-based, and physics-informed descriptors, individually and in combination, and showed that sequential Pearson filtering and fold-wise SHAP-based recursive feature elimination with cross-fold aggregation reduced errors while controlling complexity. Guided by the one-SE rule, a narrower domain-informed set of 13 features provided the best accuracy parsimony trade-off ($R^2=0.81$), only 0.01 below the max. $R^2$ achievable with 3x the number of features. SHAP analysis indicated that high permeability is promoted by elevated temperature, lattice expansion relative to Pd, atomic size mismatch, and favorable mixing tendencies. Second, the selected model was applied to screen $Pd_{(100-x-y)}Cu_{x}M_{y}$ spanning 16 co-dopants M for B2 stabilization. For each M system, we obtained the Pareto set of compositions that minimize Pd content and Miedema heat of formation and maximize the permeability, then picked three compounds, including that with the highest predicted permeability, the lowest Miedema heat of formation, and the lowest Pd content. With a final filter considering M concentration for single-phase Pd-M solution formation, we recommend Pd48.48Cu43.00Y8.52, Pd49.08Cu42.45Sc8.47, Pd56.09Cu33.70La10.21, and Pd52.68Cu40.44Mg6.88 for experimental validation. We predict those alloys to exhibit permeabilities 1.7 to 1.9 higher than B2 Pd60Cu40. Our framework provides plausible experimental targets and a scalable pathway for designing stable, high-temperature, H2-selective Pd-alloy membranes. Ternary operation ; Alloy ; Permeability (electromagnetism) ; Relative permeability ; Scalability ; Filter (signal processing) ; Lattice (music) ; Intermetallic Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysts for Methane Reforming
Authors: Connor G. Bischak
Source:  (article, 2025-09-15)
Field: Materials Science — Polymers and Plastics

Optimizing the performance of organic electrochemical transistors (OECTs) through high-throughput electrochemistry guided by machine learning Organic electrochemical transistors (OECTs) have recently gained attention for a diverse range of applications, including bioelectronics, neuromorphic computing, and biosensing. Their performance is strongly influenced by the properties of the active layer, which typically consists of a conjugated polymer organic mixed ionic-electronic conductor (OMIEC). Factors such as polymer chemical structure, processing conditions, and electrolyte selection all play critical roles in determining device performance. Because of the large number of variables, finding optimal OECT configurations can be challenging. To address this, we have developed an automated robotic system—guided by machine learning—that fabricates and characterizes OMIEC thin films in a high-throughput and reproducible manner. By systematically investigating how molecular ion identity affects OECT performance, we are able to predict and discover new high-performance ions. Overall, our work reveals which chemical properties of electrolytes are most important for realizing OECTs with superior performance. Transistor ; Electrochemistry ; Electrolyte ; Polymer ; Neuromorphic engineering ; Conductive polymer ; Bioelectronics ; Thin-film transistor Conducting polymers and applications ; Analytical Chemistry and Sensors ; Electrochemical Analysis and Applications
Authors: Lieihn Tsaur | Luis A. Nieves-Rosado | B.P. Prajwal | Qi Zhang | Yuk Mun Li | Fernando A. Escobedo | Ulrich Wiesner
Source: Nature Communications (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Film surface assemblies from chemically distinct block copolymer micelles Block copolymer (BCP) self-assembly - neat, or in a homopolymer/second BCP containing molecular blend - enables complex nanostructures including spherical micelles, co-continuous cubic gyroids and Frank-Kasper phases. Emergent properties in metallic and high entropy alloys inspire a paradigm shift: treating chemically distinct BCP micelles as functional building blocks to pave a path for multicomponent assemblies with molecularly engineered property profiles. Slowing current progress are challenges associated with the structural characterization of such BCP assemblies with poor atomic contrast. Here, we demonstrate how film surface self-assemblies of binary and ternary BCP micelle alloys can be phase inverted to generate porosity. This, combined with machine-learning assisted image segmentation, allows component classification via routine scanning electron microscopy. Results are supported by mechanistic insights from Voronoi analysis, cluster analysis and Monte Carlo/Brownian Dynamics simulations, revealing rich and controllable non-equilibrium surface structural behavior. Such multicomponent materials are expected to enable emergent properties for a range of applications. Copolymer ; Micelle ; Nanostructure ; Ternary operation ; Characterization (materials science) ; Self-assembly ; Voronoi diagram ; Component (thermodynamics) ; Molecular dynamics ; Surface (topology) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization
Authors: Ştefan Ţălu | Long V. Truong
Source:  (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

The power of simulation: Exploring binary alloys for next-generation applications This review provides an updated perspective on the transformative role of computational simulation in the design and discovery of binary alloys for advanced technologies. Unlike traditional trial and error methods, molecular dynamics (MD) and density functional theory (DFT) simulations now deliver atomistic insights into structure property relationships, enabling more predictive materials design. Recent developments demonstrate that hybrid strategies integrating DFT, MD, machine learning (ML), and multiscale modeling are accelerating the discovery of high performance alloys. The article emphasizes the novelty of simulation-driven design frameworks while identifying critical research challenges, including scalability, force-field accuracy, and the integration of simulation with digital twin concepts. Through selected case studies ranging from semiconductors and biocompatible biomedical alloys to energy materials and emerging 2D binary systems this review argues that computational simulation is shifting from a supplementary role to a central driver of innovation in modern materials science Binary number ; Transformative learning ; Novelty ; Power (physics) ; Perspective (graphical) ; Binary Independence Model ; Molecular dynamics ; Ranging Machine Learning in Materials Science ; High Entropy Alloys Studies ; Nanoporous metals and alloys
Authors: Abhinav Sai Chalasani | S. Deb | Avishek Anand | Yuhan Li | R. Downing | Edward Njoo
Source: ChemRxiv (preprint, 2025-10-15)
Field: Materials Science — Materials Chemistry

Evaluation of machine learning models for condition optimization in diverse amide coupling reactions Context (archaeology) ; Support vector machine ; Coupling (piping) ; Feature (linguistics) ; Kernel (algebra) ; Substrate (aquarium) ; Yield (engineering) Machine Learning in Materials Science
Authors: Baobin Xie | Chen Yang | Weizheng Lu | Li Jia | Qihong Fang
Source: International Conference on Computational & Experimental Engineering and Sciences (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Rational Design and Cross-Scale Simulation in Multi-Principal Element Alloys Multi-principal element alloys have aroused extensive attention due to their outstanding mechanical, physical, and chemical performances. To achieve performance-orientated design with high efficiency and low cost and further ... | Find, read and cite all the research you need on Tech Science Press Element (criminal law) ; Finite element method ; Rational design ; Machine design Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; High Entropy Alloys Studies
Authors: Ravuri Krishna
Source: American Journal of Physical Chemistry (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

Prediction of Molecular Structures and Properties by Using Quantum Technology Accurate prediction of molecular structures and properties is vital for chemistry; materials science, and drug discovery, yet classical electronic-structure methods often fail for strongly correlated systems, large basis sets, and complex potential-energy landscapes. Quantum technology encompassing quantum computing, quantum machine learning, and hybrid quantum classical strategies offers a fundamentally new paradigm by encoding many-electron wave functions directly on qubits and exploiting superposition and entanglement to explore exponentially large Hilbert spaces. This review synthesizes recent algorithmic and hardware advances relevant to molecular modelling, including the Variational Quantum Eigensolver (VQE), Quantum Phase Estimation (QPE), quantum unitary coupled-cluster (q-UCC) families, equation-of-motion and subspace methods for excited states, tensor-network hybrids, and quantum kernel and variational QML approaches. We examine noise-aware hybrid workflows, error-mitigation techniques, symmetry-preserving ansätze, and operator-factorization methods that reduce measurement and gate overhead. Representative applications are discussed: ground- and excited-state energy prediction, potential-energy surface mapping, geometry and transition-state optimization, spectroscopic property estimation (IR, UV–Vis, NMR, EPR), and reaction-dynamics scenarios where non-adiabatic effects and conical intersections dominate. Resource estimates and scaling analyses clarify current NISQ limitations qubit counts, circuit depth, shot complexity and delineate the roadmap to fault-tolerant QPE for chemical accuracy. We compare quantum approaches with classical baselines (DFT, CCSD (T), multireference methods), identifying domains where quantum methods already show promise (strong correlation, multi-reference dissociation, spin-state ordering) and where classical methods remain competitive. Finally, we highlight near-term industrial opportunities in drug design, catalysis, CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; capture, and energy materials, and outline critical research directions: algorithmic reductions in measurement/precision cost, hardware improvements in fidelity and connectivity, scalable ansatz design, and integrated software stacks for reproducible hybrid simulations. Together, these developments indicate that while practical, large-scale quantum advantage for general chemistry remains future work, quantum technologies are rapidly maturing into powerful tools for targeted molecular problems that are intractable with existing classical techniques. Quantum algorithm ; Quantum computer ; Quantum ; Quantum entanglement ; Quantum technology ; Qubit ; Ansatz ; Quantum phase estimation algorithm ; Overhead (engineering) ; Quantum dynamics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Advanced Chemical Physics Studies
Authors: Guangshuai Han | Tianhao Li | Xu Xiao | Jaehyung Lee | Guotao Qiu | Sabrina Sequeira | Akshaya Ajith | Corey Oses
Source: Nano Futures (article, 2025-10-30)
Field: Materials Science — Materials Chemistry

The search for high-entropy fuel-cell catalysts using disorder descriptors Abstract The transition to a hydrogen economy depends on efficient, affordable catalysts for fuel cells. Platinum—the industry standard for fuel-cell electrodes—is costly and scarce, highlighting the need for practical alternatives. High-entropy alloys offer vast compositional diversity and tunable properties that can mitigate these issues, yet their chemical complexity and configurational disorder have hindered rational discovery. Here, we introduce a data-driven framework that couples machine learning with first-principles disorder descriptors—including the entropy forming ability, disordered enthalpy-entropy descriptor, and electronic-structure similarity metrics to platinum—to predict alloy synthesizability and catalytic performance. These descriptors are applied for the first time in the context of fuel-cell catalyst discovery. The workflow rapidly screens more than 20 000 compositions and identifies several platinum-free candidates that are economically viable, readily scalable, and exhibit promising predicted activity. These results demonstrate that disorder descriptors are reliably predicted by machine learning models and can be effectively integrated into materials-discovery pipelines, accelerating innovation across complex compositional spaces. Workflow ; Catalysis ; Context (archaeology) ; Similarity (geometry) ; Entropy (arrow of time) ; Training set Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; High Entropy Alloys Studies
Authors: Haipeng Gong | Haoyu Wang | Z. L. Zhang
Source: Research Square (preprint, 2025-12-29)
Field: Materials Science — Materials Chemistry

qcMol: a large-scale dataset of 1.2 million molecules with high-quality quantum chemical annotations for molecular representation learning Quantum chemical ; Representation (politics) ; Property (philosophy) ; Benchmark (surveying) ; External Data Representation ; Set (abstract data type) ; Function (biology) ; Data set ; Feature learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Murtaza Zohair | Vidushi Sharma | Eduardo Soares | Khanh Nguyen | Maxwell J. Giammona | Linda K. Sundberg | Andy Tek | Emilio A. V. Vital | Young‐Hye La
Source: ArXiv.org (preprint, 2025-03-19)
Field: Materials Science — Materials Chemistry

Chemical Foundation Model Guided Design of High Ionic Conductivity Electrolyte Formulations Designing optimal formulations is a major challenge in developing electrolytes for the next generation of rechargeable batteries due to the vast combinatorial design space and complex interplay between multiple constituents. Machine learning (ML) offers a powerful tool to uncover underlying chemical design rules and accelerate the process of formulation discovery. In this work, we present an approach to design new formulations that can achieve target performance, using a generalizable chemical foundation model. The chemical foundation model is fine-tuned on an experimental dataset of 13,666 ionic conductivity values curated from the lithium-ion battery literature. The fine-tuned model is used to discover 7 novel high conductivity electrolyte formulations through generative screening, improving the conductivity of LiFSI and LiDFOB based electrolytes by 82% and 172%, respectively. These findings highlight a generalizable workflow that is highly adaptable to the discovery of chemical mixtures with tailored properties to address challenges in energy storage and beyond. Foundation (evidence) ; Electrolyte ; Workflow ; Conductivity ; Battery (electricity) ; Process (computing) ; Chemical space ; Ionic conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials