jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maria Politi | Fábio Baum | Kiran Vaddi | Sonya Vasquez | Edwin Antonio | Brittany P. Bishop | Nadya Peek | Vincent C. Holmberg | Lilo D. Pozzo
Source: ChemRxiv (preprint, 2023-03-01)
Field: Materials Science — Materials Chemistry

High-Throughput Workflow for the Synthesis of CdSe Nanocrystals Using a Sonochemical Materials Acceleration Platform We present a complete open-hardware and software Materials Acceleration Platform (MAP) for sonochemical synthesis of nanocrystals using a versatile tool-changing platform (Jubilee) configured for automated ultrasound application, a liquid-handling robot (Opentrons OT2) and a well-plate spectrometer. An automated high-throughput protocol was developed demonstrating the synthesis of CdSe nanocrystals using sonochemistry and different combinations of sample conditions, including precursor and ligand compositions and concentrations. Cavitation caused by ultrasound fields cause local and transient increases in temperature and pressure sufficient to drive the decomposition of organometalic precursors to drive the chemical reaction leading to nanocrystal formation. A total of 625 unique sample conditions were prepared and analyzed in triplicate with an individual sample volume of as little as 0.5 mL, which drastically reduced chemical waste and experimental times. The rapid onset of cavitation and quick dissipation of energy results in fast nucleation with little nanocrystal growth leading to the formation of small nanocrystals or magic-size clusters (MSCs) depending on composition. Using the effective mass approximation, the calculated QDs diameters obtained in all our experimental conditions ranged between 1.3 and 2.1 nm, which was also validated with small angle xray scattering (SAXS). Polydispersity, QD shape and optical properties largely varied depending on the concentration of ligands present in solution. Statistical analysis of the spectroscopic data corroborates the qualitative relationships observed from the optical characterization of the samples with the model-agnostic SHAP analysis. The complete workflow relies on relatively low-cost and open-source systems (< $15k for both robotic platforms). Automation and the reduced volumes also allows for cost-efficient experimentation, increasing the accessibility of this MAP. The high-throughput capabilities of the automated sonication platform, the extensible nature of the Jubilee system, and the modular nature of the protocol, make the workflow adaptable to a variety of future studies, including other nanocrystal design spaces, emulsification processes, and nanoparticle re-dispersion or exfoliation. Nanocrystal ; Materials science ; Dispersity ; Nucleation ; Nanomaterials ; Throughput ; Nanotechnology ; Nanoparticle ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Chromatography ; Organic chemistry Quantum Dots Synthesis And Properties
Authors: Daniel Wigh
Source:  (preprint, 2023-08-10)
Field: Chemistry — Organic Chemistry

Quantitative in silico prediction of the rate of protodeboronation by a mechanistic density functional theory-aided algorithm Computational reaction prediction has become a ubiquitous task in chemistry due to the potential value accurate predictions can bring to chemists. Boronic acids are widely used in industry; however, understanding how to avoid the protodeboronation side reaction remains a challenge. We have developed an algorithm for in silico prediction of the rate of protodeboronation of boronic acids. A general mechanistic model devised through kinetic studies of protodeboronation was found in the literature and forms the foundation on which the algorithm presented in this work is built. Protodeboronation proceeds through 7 distinct pathways, though for any particular boronic acid, only a subset of mechanistic pathways are active. The rate of each active mechanistic pathway is linearly correlated with its characteristic energy difference, which in turn can be determined using Density Functional Theory. We validated the algorithm using leave-one-out cross-validation on a data set of 50 boronic acids and made a further 50 rate predictions on academically and industrially important boronic acids out of sample. We believe this work will provide great assistance to chemists performing reactions that feature boronic acids, such as Suzuki-Miyaura and Chan-Evans-Lam couplings. In silico ; Computer science ; Algorithm ; Computational biology ; Chemistry ; Biology Organoboron and organosilicon chemistry ; Chemical Synthesis and Characterization ; Boron Compounds in Chemistry
Authors: Arpan Biswas | Yongtao Liu | Maxim Ziatdinov | Yuchen Liu | Stephen Jesse | Jan‐Chi Yang | Sergei V. Kalinin | Rama K. Vasudevan
Source:  (article, 2023-08-20)
Field: Materials Science — Materials Chemistry

A Multi-Objective Bayesian Optimized Human Assessed Multi-Target Generated Spectral Recommender System for Rapid Pareto Discoveries of Material Properties Abstract Optimization for different tasks like material characterization, synthesis, and functional properties for desired applications over multi-dimensional control parameter and function spaces need a rapid strategic search through active learning. However, in all cases prior to optimization, the target material properties are assumed known and fixed, which mostly deviates from real-world scenarios in material synthesis. This can be critical for running expensive experiments on new materials, when the experimental results are fuzzy for any scientific outcomes due to improper target setting, ultimately wasting time and cost. The failure rate and cost are even higher over exploring on multi-target space, where we want to learn the pareto among multiple properties, to jointly optimize during material synthesis for desired applications. To address the challenge, here we introduce the human-operator attempt flexibility in the active learning based automated experiment framework, with generating multiple human assessed targets through a voting-based recommender system during real-time microscope measurements over the large material image space, sequentially learn/update multiple desired targets through a weighting system, and adaptively search in multiple material properties functional space for non-dominated pareto discoveries to maximize the custom structural similarity based acquisition function. We term this a multi-objective Bayesian optimized human assessed multi-target generated spectral recommender systems (MOBO-HAM-SRS). The approach has been demonstrated to peizoresponse force spectroscopy of a ferroelectric thin film, exploring with different kernels and acquisition functions. This work shows an advancement towards human-AI collaborated automated experiments, steering optimization trajectories through human overpowering AI at the early stage when uncertainty is high and AI overpowering human at the later stage with rapid exploration towards optimal goal, following human-assessed multiple targets properties. Bayesian optimization ; Computer science ; Recommender system ; Pareto principle ; Weighting ; Flexibility (engineering) ; Multi-objective optimization ; Artificial intelligence ; Machine learning ; Mathematical optimization ; Mathematics Machine Learning in Materials Science ; Advanced Neural Network Applications
Authors: Johannes T. Margraf
Source: Angewandte Chemie (article, 2023-03-10)
Field: Materials Science — Materials Chemistry

Science‐Driven Atomistic Machine Learning Abstract Machine learning (ML) algorithms are currently emerging as powerful tools in all areas of science. Conventionally, ML is understood as a fundamentally data‐driven endeavour. Unfortunately, large well‐curated databases are sparse in chemistry. In this contribution, I therefore review science‐driven ML approaches which do not rely on “big data”, focusing on the atomistic modelling of materials and molecules. In this context, the term science‐driven refers to approaches that begin with a scientific question and then ask what training data and model design choices are appropriate. As key features of science‐driven ML, the automated and purpose‐driven collection of data and the use of chemical and physical priors to achieve high data‐efficiency are discussed. Furthermore, the importance of appropriate model evaluation and error estimation is emphasized. Computer science ; Big data ; Data science ; Context (archaeology) ; Key (lock) ; Data-driven ; Artificial intelligence ; Machine learning ; Data mining Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Derek van Tilborg | Francesca Grisoni
Source: ChemRxiv (preprint, 2023-11-23)
Field: Materials Science — Materials Chemistry

Traversing Chemical Space with Active Deep Learning Deep learning is accelerating drug discovery. However, current approaches are often affected by limitations in the available data, e.g., in terms of size or molecular diversity. Active learning is poised to be a solution for drug discovery in low-data regimes. In active learning, a model is updated iteratively by taking multiple smaller screening steps, instead of suggesting many molecules at once with a single model for traditional ‘one-shot’ screening. This iterative approach aims to improve models during the screening process and can adjust course along the way. However, active learning remains still relatively underexplored in the molecular sciences. It is currently unclear how active learning holds up to traditional approaches and what the best strategies are for prospective drug discovery applications. In this study, we lay the first foundations for the prospective use of active deep learning in low-data scenarios where only dozens of training molecules are available to screen hundreds of thousands of molecules. Our systematic study combines six active learning strategies, two deep learning architectures, and three large-scale molecular libraries. We highlight that active learning can achieve up to a six-fold improvement in hit discovery compared to traditional methods. How molecules are chosen for the next iteration proved to be the primary driver of performance – it is more important than the chosen network architecture in determining the ‘molecular journey’ in the chemical space. Remarkably, active learning showed to quickly compensate for a lack of molecular diversity in the starting set, allowing to efficiently chart unexplored structural motifs. These results set the basis for the adoption of active deep learning to accelerate drug discovery in low-data regimes. Chemical space ; Active learning (machine learning) ; Drug discovery ; Computer science ; Artificial intelligence ; Machine learning ; Deep learning ; Set (abstract data type) ; Process (computing) ; Traverse ; Data science ; Bioinformatics ; Biology ; Geography Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning and Algorithms
Authors: Clara Fannjiang | Jennifer Listgarten
Source: arXiv (Cornell University) (preprint, 2023-06-01)
Field: Materials Science — Materials Chemistry

Is novelty predictable? Machine learning-based design has gained traction in the sciences, most notably in the design of small molecules, materials, and proteins, with societal implications spanning drug development and manufacturing, plastic degradation, and carbon sequestration. When designing objects to achieve novel property values with machine learning, one faces a fundamental challenge: how to push past the frontier of current knowledge, distilled from the training data into the model, in a manner that rationally controls the risk of failure. If one trusts learned models too much in extrapolation, one is likely to design rubbish. In contrast, if one does not extrapolate, one cannot find novelty. Herein, we ponder how one might strike a useful balance between these two extremes. We focus in particular on designing proteins with novel property values, although much of our discussion addresses machine learning-based design more broadly. Novelty ; Computer science ; Extrapolation ; Viewpoints ; Property (philosophy) ; Artificial intelligence ; Machine learning ; Psychology ; Epistemology ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Miguel Steiner | Markus Reiher
Source: arXiv (Cornell University) (preprint, 2023-08-31)
Field: Materials Science — Materials Chemistry

Navigating chemical reaction space with a steering wheel Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a Steering Wheel to guide an otherwise unbiased automated exploration. The Steering Wheel algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the Steering Wheel for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates. Computer science ; Mechanism (biology) ; Throughput ; Context (archaeology) ; Key (lock) ; Chemical space ; Focus (optics) ; Biochemical engineering ; Chemistry ; Engineering ; Drug discovery ; Physics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Namkyeong Lee | Heewoong Noh | Sungwon Kim | Dongmin Hyun | Gyoung S. Na | Chanyoung Park
Source: arXiv (Cornell University) (preprint, 2023-10-24)
Field: Materials Science — Materials Chemistry

Density of States Prediction of Crystalline Materials via Prompt-guided Multi-Modal Transformer The density of states (DOS) is a spectral property of crystalline materials, which provides fundamental insights into various characteristics of the materials. While previous works mainly focus on obtaining high-quality representations of crystalline materials for DOS prediction, we focus on predicting the DOS from the obtained representations by reflecting the nature of DOS: DOS determines the general distribution of states as a function of energy. That is, DOS is not solely determined by the crystalline material but also by the energy levels, which has been neglected in previous works. In this paper, we propose to integrate heterogeneous information obtained from the crystalline materials and the energies via a multi-modal transformer, thereby modeling the complex relationships between the atoms in the crystalline materials and various energy levels for DOS prediction. Moreover, we propose to utilize prompts to guide the model to learn the crystal structural system-specific interactions between crystalline materials and energies. Extensive experiments on two types of DOS, i.e., Phonon DOS and Electron DOS, with various real-world scenarios demonstrate the superiority of DOSTransformer. The source code for DOSTransformer is available at https://github.com/HeewoongNoh/DOSTransformer. Modal ; Density of states ; Materials science ; Computer science ; Crystal (programming language) ; Focus (optics) ; Phonon ; Transformer ; Statistical physics ; Crystal structure ; Physics ; Crystallography ; Condensed matter physics ; Chemistry ; Optics ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Yuanqi Du | Yingheng Wang | Yining Huang | Jianan Canal Li | Yanqiao Zhu | Tian Xie | Chenru Duan | John M. Gregoire | Carla P. Gomes
Source: arXiv (Cornell University) (preprint, 2023-06-14)
Field: Materials Science — Materials Chemistry

M$^2$Hub: Unlocking the Potential of Machine Learning for Materials Discovery We introduce M$^2$Hub, a toolkit for advancing machine learning in materials discovery. Machine learning has achieved remarkable progress in modeling molecular structures, especially biomolecules for drug discovery. However, the development of machine learning approaches for modeling materials structures lag behind, which is partly due to the lack of an integrated platform that enables access to diverse tasks for materials discovery. To bridge this gap, M$^2$Hub will enable easy access to materials discovery tasks, datasets, machine learning methods, evaluations, and benchmark results that cover the entire workflow. Specifically, the first release of M$^2$Hub focuses on three key stages in materials discovery: virtual screening, inverse design, and molecular simulation, including 9 datasets that covers 6 types of materials with 56 tasks across 8 types of material properties. We further provide 2 synthetic datasets for the purpose of generative tasks on materials. In addition to random data splits, we also provide 3 additional data partitions to reflect the real-world materials discovery scenarios. State-of-the-art machine learning methods (including those are suitable for materials structures but never compared in the literature) are benchmarked on representative tasks. Our codes and library are publicly available at https://github.com/yuanqidu/M2Hub. Workflow ; Computer science ; Drug discovery ; Machine learning ; Benchmark (surveying) ; Key (lock) ; Artificial intelligence ; Bridge (graph theory) ; Knowledge extraction ; Generative grammar ; Data science ; Database ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Henry W. Sprueill | Carl N. Edwards | Mariefel V. Olarte | Udishnu Sanyal | Heng Ji | Sutanay Choudhury
Source: arXiv (Cornell University) (preprint, 2023-10-22)
Field: Materials Science — Materials Chemistry

Monte Carlo Thought Search: Large Language Model Querying for Complex Scientific Reasoning in Catalyst Design Discovering novel catalysts requires complex reasoning involving multiple chemical properties and resultant trade-offs, leading to a combinatorial growth in the search space. While large language models (LLM) have demonstrated novel capabilities for chemistry through complex instruction following capabilities and high quality reasoning, a goal-driven combinatorial search using LLMs has not been explored in detail. In this work, we present a Monte Carlo Tree Search-based approach that improves beyond state-of-the-art chain-of-thought prompting variants to augment scientific reasoning. We introduce two new reasoning datasets: 1) a curation of computational chemistry simulations, and 2) diverse questions written by catalysis researchers for reasoning about novel chemical conversion processes. We improve over the best baseline by 25.8\% and find that our approach can augment scientist's reasoning and discovery process with novel insights. Computer science ; Process (computing) ; Space (punctuation) ; Artificial intelligence ; Theoretical computer science ; Data science ; Programming language Machine Learning in Materials Science ; Topic Modeling
Authors: Abdulelah S. Alshehri | Franklin L. Lee | Shihu Wang
Source: arXiv (Cornell University) (preprint, 2023-09-21)
Field: Materials Science — Materials Chemistry

Multimodal Deep Learning for Scientific Imaging Interpretation In the domain of scientific imaging, interpreting visual data often demands an intricate combination of human expertise and deep comprehension of the subject materials. This study presents a novel methodology to linguistically emulate and subsequently evaluate human-like interactions with Scanning Electron Microscopy (SEM) images, specifically of glass materials. Leveraging a multimodal deep learning framework, our approach distills insights from both textual and visual data harvested from peer-reviewed articles, further augmented by the capabilities of GPT-4 for refined data synthesis and evaluation. Despite inherent challenges--such as nuanced interpretations and the limited availability of specialized datasets--our model (GlassLLaVA) excels in crafting accurate interpretations, identifying key features, and detecting defects in previously unseen SEM images. Moreover, we introduce versatile evaluation metrics, suitable for an array of scientific imaging applications, which allows for benchmarking against research-grounded answers. Benefiting from the robustness of contemporary Large Language Models, our model adeptly aligns with insights from research papers. This advancement not only underscores considerable progress in bridging the gap between human and machine interpretation in scientific imaging, but also hints at expansive avenues for future research and broader application. Computer science ; Bridging (networking) ; Benchmarking ; Expansive ; Deep learning ; Data science ; Artificial intelligence ; Robustness (evolution) ; Visualization ; Comprehension ; Interpretation (philosophy) ; Human–computer interaction Machine Learning in Materials Science ; Multimodal Machine Learning Applications ; Cell Image Analysis Techniques
Authors: Vadim Korolev | Pavel Protsenko
Source: arXiv (Cornell University) (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

Toward Accurate Interpretable Predictions of Materials Properties within Transformer Language Models Property prediction accuracy has long been a key parameter of machine learning in materials informatics. Accordingly, advanced models showing state-of-the-art performance turn into highly parameterized black boxes missing interpretability. Here, we present an elegant way to make their reasoning transparent. Human-readable text-based descriptions automatically generated within a suite of open-source tools are proposed as materials representation. Transformer language models pretrained on 2 million peer-reviewed articles take as input well-known terms, e.g., chemical composition, crystal symmetry, and site geometry. Our approach outperforms crystal graph networks by classifying four out of five analyzed properties if one considers all available reference data. Moreover, fine-tuned text-based models show high accuracy in the ultra-small data limit. Explanations of their internal machinery are produced using local interpretability techniques and are faithful and consistent with domain expert rationales. This language-centric framework makes accurate property predictions accessible to people without artificial-intelligence expertise. Interpretability ; Computer science ; Parameterized complexity ; Suite ; Artificial intelligence ; Language model ; Transformer ; Graph ; Representation (politics) ; Property (philosophy) ; Natural language processing ; Machine learning ; Theoretical computer science ; Algorithm Machine Learning in Materials Science ; Topic Modeling