jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guoqiang Wang | Xuefei Wu | Bo Xin | Xu Gu | Gaobo Wang | Yong Zhang | Jiabao Zhao | Xu Cheng | Chunlin Chen | Jing Ma
Source: Molecules (review, 2023-02-27)
Field: Materials Science — Materials Chemistry

Machine Learning in Unmanned Systems for Chemical Synthesis Chemical synthesis is state-of-the-art, and, therefore, it is generally based on chemical intuition or experience of researchers. The upgraded paradigm that incorporates automation technology and machine learning (ML) algorithms has recently been merged into almost every subdiscipline of chemical science, from material discovery to catalyst/reaction design to synthetic route planning, which often takes the form of unmanned systems. The ML algorithms and their application scenarios in unmanned systems for chemical synthesis were presented. The prospects for strengthening the connection between reaction pathway exploration and the existing automatic reaction platform and solutions for improving autonomation through information extraction, robots, computer vision, and intelligent scheduling were proposed. Automation ; Computer science ; Intuition ; Scheduling (production processes) ; Robot ; Artificial intelligence ; Chemical technology ; Machine learning ; Biochemical engineering ; Engineering ; Mechanical engineering Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Modular Robots and Swarm Intelligence
Authors: Nicholas H. Angello | David Friday | Changhyun Hwang | Seungjoo Yi | Austin Cheng | Tiara Torres-Flores | Edward R. Jira | Wesley Wang | Alán Aspuru‐Guzik | Martin D. Burke | Charles M. Schroeder | Ying Diao | Nicholas E. Jackson
Source: ChemRxiv (preprint, 2023-09-20)
Field: Materials Science — Materials Chemistry

Closed-Loop Transfer Enables AI to Yield Chemical Knowledge AI-guided closed-loop experimentation has recently emerged as a promising method to optimize objective functions,1,2 but the substantial potential of this traditionally black-box approach to reveal new scientific knowledge has remained largely untapped. Here, we report a new AI-guided approach, dubbed Closed-Loop Transfer (CLT), that integrates closed-loop experiments with physics-based feature selection and supervised learning to yield new scientific knowledge in parallel with optimization of objective functions. CLT surprisingly revealed that high-energy regions of the triplet state manifold are paramount in dictating molecular photostability in solution across a diverse chemical library of light-harvesting donor-bridge-acceptor oligomers. Remarkably, this insight emerged after automated modular synthesis and experimental characterization of only ~1.5% of the theoretical chemical space. Supervised learning models considering millions of combinations of 100+ physics-based descriptors further showed that high energy triplet states most strongly correlate with photostability, while excluding more commonly considered predictors such as the lowest energy triplet state. The physics-informed model for photostability was even further confirmed and then strengthened using an explicit experimental test set, validating the substantial power of the CLT method. Broadly, these findings show that interfacing physics-based modeling with closed-loop discovery campaigns unimpeded by synthesis bottlenecks can rapidly illuminate fundamental chemical insights and guide more rational pursuit of frontier molecular functions. Interfacing ; Chemical space ; Modular design ; Artificial intelligence ; Computer science ; Yield (engineering) ; Set (abstract data type) ; Machine learning ; Biochemical engineering ; Physics ; Engineering ; Biology ; Drug discovery ; Bioinformatics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Arpan Biswas | Yongtao Liu | Nicole Creange | Yuchen Liu | Stephen Jesse | Jan‐Chi Yang | Sergei V. Kalinin | Maxim Ziatdinov | Rama K. Vasudevan
Source: arXiv (Cornell University) (preprint, 2023-04-05)
Field: Materials Science — Materials Chemistry

A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments Optimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined apriori with limited human feedback during operation. In contrast, here we present the development of a new type of human in the loop experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly, employing human feedback. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and then implement this in real time on an atomic force microscope, where the optimization proceeds to find symmetric piezoresponse amplitude hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human-augmented machine learning approaches for curiosity-driven exploration of systems across experimental domains. The analysis reported here is summarized in Colab Notebook for the purpose of tutorial and application to other data: https://github.com/arpanbiswas52/varTBO Bayesian optimization ; Computer science ; Workflow ; Domain (mathematical analysis) ; Recommender system ; Piezoresponse force microscopy ; Artificial intelligence ; Ranging ; Human-in-the-loop ; Curiosity ; Machine learning ; Ferroelectricity ; Materials science ; Optoelectronics ; Database ; Mathematics Machine Learning in Materials Science
Authors: Hannes Kneiding | Ainara Nova | David Balcells
Source: ChemRxiv (preprint, 2023-09-25)
Field: Materials Science — Materials Chemistry

Directional Multiobjective Optimization of Metal Complexes at the Billion-Scale with the tmQMg-L Dataset and PL-MOGA Algorithm Transition metal complexes (TMCs) play a key role in several areas of high interest, including medicinal chemistry, renewable energies, and nanoporous materials. The development of TMCs enabling these technologies remains challenged by the need to optimize multiple properties within very large chemical spaces, in which the thirty transition metals can be combined with a virtually infinite number of ligands. In this work, we provide the open tmQMg-L dataset including 30K TMC ligands, which combines large chemical diversity with synthesizability. The charge and metal-coordination mode of the ligands were robustly defined with a novel algorithm based on graph and natural bond orbital theories. The tmQMg-L dataset was leveraged in the automated generation of 1.37M TMCs resulting from all possible combinations between a square planar palladium(II) scaffold and a pool of 50 different ligands. This TMC space was used to benchmark a multiobjective genetic algorithm (MOGA) that optimized two properties over a Pareto front; namely the polarizability (alpha) and the HOMO-LUMO gap (epsilon). The MOGA evolved 130 TMC hits with maximal (alpha, epsilon) values in a way that could be easily rationalized by analyzing the nature of the ligands selected. Instead of the traditional mutation and crossover of fragments within a single ligand, this MOGA implemented full-ligand genetic operations acting on all coordination sites, maximizing chemical diversity. Further, we extended this MOGA algorithm with the Pareto-Lighthouse functionality (PL-MOGA), which allows for controlling both the aim and scope of the multiobjective optimization over the Pareto front. In explicit spaces containing billions of TMCs, the PL-MOGA enabled the explainable generation of thousands of novel and highly diverse TMC hits. We believe that the combined use of the tmQMg-L dataset and PL-MOGA algorithm will facilitate the discovery of TMCs with optimal properties within untapped chemical spaces. Computer science ; Multi-objective optimization ; Algorithm ; Benchmark (surveying) ; Genetic algorithm ; Chemical space ; Pareto principle ; Mathematical optimization ; Chemistry ; Mathematics ; Drug discovery ; Machine learning Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Zhenze Yang | Weike Ye | X. L. Lei | Daniel Schweigert | Ha-Kyung Kwon | Arash Khajeh
Source: arXiv (Cornell University) (preprint, 2023-12-11)
Field: Materials Science — Materials Chemistry

De novo Design of Polymer Electrolytes with High Conductivity using GPT-based and Diffusion-based Generative Models Solid polymer electrolytes hold significant promise as materials for next-generation batteries due to their superior safety performance, enhanced specific energy, and extended lifespans compared to liquid electrolytes. However, the material's low ionic conductivity impedes its commercialization, and the vast polymer space poses significant challenges for the screening and design. In this study, we assess the capabilities of generative artificial intelligence (AI) for the de novo design of polymer electrolytes. To optimize the generation, we compare different deep learning architectures, including both GPT-based and diffusion-based models, and benchmark the results with hyperparameter tuning. We further employ various evaluation metrics and full-atom molecular dynamics simulations to assess the performance of different generative model architectures and to validate the top candidates produced by each model. Out of only 45 candidates being tested, we discovered 17 polymers that achieve superior ionic conductivity better than any other polymers in our database, with some of them doubling the conductivity value. In addition, by adopting a pretraining and fine-tuning methodology, we significantly improve the efficacy of our generative models, achieving quicker convergence, enhanced performance with limited data, and greater diversity. Using the proposed method, we can easily generate a large number of novel, diverse, and valid polymers, with a chance of synthesizability, enabling us to identify promising candidates with markedly improved efficiency. Benchmark (surveying) ; Computer science ; Conductivity ; Polymer ; Generative model ; Generative grammar ; Materials science ; Hyperparameter ; Electrolyte ; Commercialization ; Diffusion ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Zongtao Lin | Joanna Gongora | Xingyu Liu | Yixuan Xie | Chenfeng Zhao | Dongwen Lv | Benjamin A. García
Source: Journal of Proteome Research (review, 2023-11-02)
Field: Chemistry — Spectroscopy

Automation to Enable High-Throughput Chemical Proteomics Chemical proteomics utilizes small-molecule probes to covalently engage with their interacting proteins. Since chemical probes are tagged to the active or binding sites of functional proteins, chemical proteomics can be used to profile protein targets, reveal precise binding sites/mechanisms, and screen inhibitors competing with probes in a biological context. These capabilities of chemical proteomics have great potential to enable discoveries of both drug targets and lead compounds. However, chemical proteomics is limited by the time-consuming bottleneck of sample preparations, which are processed manually. With the advancement of robotics and artificial intelligence, it is now possible to automate workflows to make chemical proteomics sample preparation a high-throughput process. An automated robotic system represents a major technological opportunity to speed up advances in proteomics, open new frontiers in drug target discovery, and broaden the future of chemical biology. Proteomics ; Chemical genetics ; Context (archaeology) ; Chemical biology ; Drug discovery ; Bottleneck ; Computer science ; Workflow ; Automation ; Computational biology ; Nanotechnology ; Chemistry ; Data science ; Small molecule ; Bioinformatics ; Biology ; Engineering ; Biochemistry ; Materials science Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Monoclonal and Polyclonal Antibodies Research
Authors: Kangming Li | Daniel Persaud | Kamal Choudhary | Brian DeCost | Michael Greenwood | Jason Hattrick‐Simpers
Source: arXiv (Cornell University) (preprint, 2023-04-25)
Field: Materials Science — Materials Chemistry

On the redundancy in large material datasets: efficient and robust learning with less data Extensive efforts to gather materials data have largely overlooked potential data redundancy. In this study, we present evidence of a significant degree of redundancy across multiple large datasets for various material properties, by revealing that up to 95 % of data can be safely removed from machine learning training with little impact on in-distribution prediction performance. The redundant data is related to over-represented material types and does not mitigate the severe performance degradation on out-of-distribution samples. In addition, we show that uncertainty-based active learning algorithms can construct much smaller but equally informative datasets. We discuss the effectiveness of informative data in improving prediction performance and robustness and provide insights into efficient data acquisition and machine learning training. This work challenges the "bigger is better" mentality and calls for attention to the information richness of materials data rather than a narrow emphasis on data volume. Redundancy (engineering) ; Computer science ; Robustness (evolution) ; Machine learning ; Training set ; Artificial intelligence ; Data mining Machine Learning in Materials Science ; Machine Learning and Algorithms ; Machine Learning and Data Classification
Authors: Sarah R. Moor | James R. Howard | Brenden T. Herrera | Matthew McVeigh | Federico Marini | Adrian T. Keatinge‐Clay | Eric V. Anslyn
Source: Organic Chemistry Frontiers (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Determination of enantiomeric excess and diastereomeric excess <i>via</i> optical methods. Application to α-methyl-β-hydroxy-carboxylic acids Characterization of chiral molecules in solution is paramount for measuring reaction success. However, techniques to distinguish between chiral molecules containing more than one stereocenter through the use of optical techniques remains a challenge. Herein, we report a techique using a series of circular dichroism spectra to train multivariate regression models that are capable of predicting the complete speciation of 3-hydroxy-2-methylbutanoic acid stereoisomers. From this, it is possible to rapidly and accurately determine the enantiomeric excess and diastereomeric excess of the solution without the need for chiral chromatography. Chemistry ; Diastereomer ; Enantiomeric excess ; Enantiomer ; Molecule ; Organic chemistry ; Carboxylic acid ; Combinatorial chemistry ; Enantioselective synthesis ; Catalysis Analytical Chemistry and Chromatography ; Mass Spectrometry Techniques and Applications ; Molecular spectroscopy and chirality
Authors: Arash Khajeh | X. L. Lei | Weike Ye | Zhenze Yang | Daniel Schweigert | Ha-Kyung Kwon
Source: arXiv (Cornell University) (preprint, 2023-12-07)
Field: Materials Science — Materials Chemistry

A Self-Improvable Polymer Discovery Framework Based on Conditional Generative Model In this work, we introduce a polymer discovery platform to efficiently design polymers with tailored properties, exemplified by the discovery of high-performance polymer electrolytes. The platform integrates three core components: a conditioned generative model, a computational evaluation module, and a feedback mechanism, creating a self-improving system for material innovation. To demonstrate the efficacy of this platform, it is used to design polymer electrolyte materials with high ionic conductivity. A simple conditional generative model, based on the minGPT architecture, can effectively generate candidate polymers that exhibit a mean ionic conductivity that is significantly greater than those in the original training set. This approach, coupled with molecular dynamics simulations (MD) for testing and a specifically planned acquisition mechanism, allows the platform to refine its output iteratively. Notably, after the first iteration, we observed an increase in both the mean and the lower bound of the ionic conductivity of the new polymer candidates. The platform's effectiveness is underscored by the identification of 14 polymer repeating units, each displaying a computed ionic conductivity surpassing that of Polyethylene Oxide (PEO). The performance of these polymers in MD simulations verifies the platform's efficacy in generating potential polymer candidate materials. Acknowledging current limitations, future work will focus on enhancing modeling techniques, evaluation processes, and acquisition strategies, aiming for broader applicability in polymer science and machine learning. Polymer ; Computer science ; Generative grammar ; Polyethylene oxide ; Generative model ; Work (physics) ; Polymer architecture ; Ionic bonding ; Materials science ; Nanotechnology ; Artificial intelligence ; Chemistry ; Mechanical engineering ; Engineering ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Block Copolymer Self-Assembly
Authors: Kin Long Kelvin Lee | Carmelo Gonzales | Marcel Nassar | Matthew Spellings | Mikhail Galkin | Santiago Miret
Source: arXiv (Cornell University) (preprint, 2023-09-12)
Field: Materials Science — Materials Chemistry

MatSciML: A Broad, Multi-Task Benchmark for Solid-State Materials Modeling We propose MatSci ML, a novel benchmark for modeling MATerials SCIence using Machine Learning (MatSci ML) methods focused on solid-state materials with periodic crystal structures. Applying machine learning methods to solid-state materials is a nascent field with substantial fragmentation largely driven by the great variety of datasets used to develop machine learning models. This fragmentation makes comparing the performance and generalizability of different methods difficult, thereby hindering overall research progress in the field. Building on top of open-source datasets, including large-scale datasets like the OpenCatalyst, OQMD, NOMAD, the Carolina Materials Database, and Materials Project, the MatSci ML benchmark provides a diverse set of materials systems and properties data for model training and evaluation, including simulated energies, atomic forces, material bandgaps, as well as classification data for crystal symmetries via space groups. The diversity of properties in MatSci ML makes the implementation and evaluation of multi-task learning algorithms for solid-state materials possible, while the diversity of datasets facilitates the development of new, more generalized algorithms and methods across multiple datasets. In the multi-dataset learning setting, MatSci ML enables researchers to combine observations from multiple datasets to perform joint prediction of common properties, such as energy and forces. Using MatSci ML, we evaluate the performance of different graph neural networks and equivariant point cloud networks on several benchmark tasks spanning single task, multitask, and multi-data learning scenarios. Our open-source code is available at https://github.com/IntelLabs/matsciml. Computer science ; Machine learning ; Benchmark (surveying) ; Artificial intelligence ; Field (mathematics) ; Scalability ; Data mining ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Tianfan Jin | Qiyuan Zhao | Andrew B. Schofield | Brett M. Savoie
Source: ChemRxiv (preprint, 2023-06-14)
Field: Materials Science — Materials Chemistry

Machine Learning Models Capable of Chemical Deduction for Identifying Reaction Products Deductive solution strategies are required in prediction scenarios that are under determined, when contradictory information is available, or more generally wherever one-to-many non-functional mappings occur. In contrast, most contemporary machine learning (ML) in the chemical sciences is inductive learning from example, with a fixed set of features. Chemical workflows are replete with situations requiring deduction, including many aspects of lab automation and spectral interpretation. Here, a general strategy is described for designing and training machine learning models capable of deduction that consists of combining individual inductive models into a larger deductive network. The training and testing of these models is demonstrated on the task of deducing reaction products from a mixture of spectral sources. The resulting models are capable of distinguishing between intended and unintended reaction outcomes and identifying starting material based on a mixture of spectral sources. The models are also capable of performing well on tasks that they were not directly trained on, like predicting minor products from named organic chemistry reactions, identifying reagents and isomers as plausible impurities, and handling missing or conflicting information. A new dataset of 1,124,043 simulated spectra that were generated to train these models is also distributed with this work. These findings demonstrate that deductive bottlenecks for chemical problems are not fundamentally insuperable for ML models. Workflow ; Computer science ; Artificial intelligence ; Interpretation (philosophy) ; Set (abstract data type) ; Machine learning ; Automation ; Inductive reasoning ; Task (project management) ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Hiroki Umemoto | Shigeo Arai | Hirohide Otobe | Shunsuke Muto
Source: Polymer Journal (article, 2023-05-17)
Field: Materials Science — Surfaces, Coatings and Films

Stain-free mapping of polymer-blend morphologies via application of high-voltage STEM-EELS hyperspectral imaging to low-loss spectra Abstract Polymer blends composed of multiple types of polymers are used for various industrial applications; therefore, their morphologies must be understood to predict and improve their physical properties. Herein, we propose a spectral imaging method based on scanning transmission electron microscopy (STEM) and electron energy-loss spectroscopy to map polymer morphologies with nanometric resolution as an alternative to the conventional electron staining technique. In particular, the low-loss spectra of the 5–30 eV energy-loss region were measured to minimize electron irradiation damage rather than the core-loss spectra, such as carbon K-shell absorption spectra, which require significantly longer recording times. Medium-voltage (200 kV) and high-voltage (1000 kV) STEM was used at various temperatures to compare the degrees of electron-beam damage resulting from various electron energies and sample temperatures. A multivariate curve resolution technique was used to isolate the constituent spectra and visualize their distributions by distinguishing the characteristic peaks derived from various chemical species. High-voltage STEM was more useful than medium-voltage STEM for analyzing thicker samples while suppressing ionization damage. Hyperspectral imaging ; Scanning transmission electron microscopy ; Materials science ; Polymer ; Electron energy loss spectroscopy ; Spectral line ; Analytical Chemistry (journal) ; Spectroscopy ; Transmission electron microscopy ; Acceleration voltage ; High voltage ; Voltage ; Electron ; Optoelectronics ; Cathode ray ; Nanotechnology ; Chemistry ; Composite material ; Organic chemistry ; Physics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Integrated Circuits and Semiconductor Failure Analysis