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All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Solar flare discovery

This paper considers the discoveries that have appreciably changed our understanding of the physics of solar flares. A total of 42 discoveries from all disciplines, ranging from Galileo's initial observation of faculae to the recent discovery of strong limb brightening in 10-MeV gamma-radiation, are identified. The rate of discovery increased dramatically over the past four decades as new observational tools became available. The assessment of significance suggests that recent discoveries -though more numerous - are individually less significant; perhaps this is because the minor early discoveries tend to be taken for granted.

Hudson, Hugh S.↗
NASA NTRS2002

The Discovery of Extrasolar Planets by Backyard Astronomers

The discovery since 1995 of more than 80 planets around nearby solar-like stars and the photometric measurement of a transit of the jovian mass planet orbiting the solar-like star HD 209458 (producing a more than 1% drop in brightness that lasts 3 hours) has heralded a new era in astronomy. It has now been demonstrated that small telescopes equipped with sensitive and stable electronic detectors can produce fundamental scientific discoveries regarding the frequency and nature of planets outside the solar system. The modest equipment requirements for the discovery of extrasolar planetary transits of jovian mass planets in short period orbits around solar-like stars are fulfilled by commercial small aperture telescopes and CCD (charge coupled device) imagers common among amateur astronomers. With equipment already in hand and armed with target lists, observing techniques and software procedures developed by scientists at NASA's Ames Research Center and the University of California at Santa Cruz, non-professional astronomers can contribute significantly to the discovery and study of planets around others stars. In this way, we may resume (after a two century interruption!) the tradition of planet discoveries by amateur astronomers begun with William Herschel's 1787 discovery of the 'solar' planet Uranus.

Castellano, Tim↗
NASA NTRS2005

STS-114: Discovery Post MMT Briefing

On flight day 13, Leroy Cain, STS-114 Ascent/Entry Flight Director, discusses the condition of the Space Shuttle Discovery, and the weather outlook for landing. He answers questions from the news media about his feelings about re-entry since the Columbia tragedy, possible new information during re-entry, critical moments in the Mission Control Room during landing, and differences between night landing and day landing. Footage of the Mission Control Room and a talk with Soichi Noguchi in orbit is shown. Also, footage of the truss structure of the International Space Station, Destiny Laboratory, crew cabin of Discovery, and the Orbiter Docking System linked up to forward docking port on Discovery is shown. Eileen Collins and Wendy Lawrence are shown in the flight deck of Discovery. Charles Camarda is also shown in the mid-deck. Downlink television from Discovery shows spacewalk choreographer Andy Thomas with Stephen Robinson and Soichi Noguchi preparing for depressurization and pre-breathing activities that will lead to the opening of the hatch. The installation of a replacement GPS antenna, images of the port wing of Discovery and Canadarm moving with the Orbital Boom Sensor System (OBSS) extension is shown.

Source record↗
NASA NTRS2007

NASA's Discovery Program: Moving Toward the Edge (of the Solar System)

NASA's Planetary Science , Division sponsors a competitive program of small spacecraft missions with the goal of performing focused science investigations that complement NASA's larger planetary science explorations at relatively low cost. The goal of the Discovery program is to launch many smaller missions with fast development times to increase our understanding of the solar system by exploring the planets, dwarf planets, their moons, and small bodies such as comets and asteroids. Discovery missions are solicited from the broad planetary science community approximately every 2 years. Active missions within the Discovery program include several with direct scientific or engineering connections to potential future missions to the edge of the solar system and beyond. In addition to those in the Discovery program are the missions of the New Frontiers program. The first New Frontiers mission. is the New Horizons mission to Pluto, which will explore this 38-AU distant dwarf planet and potentially some Kuiper Belt objects beyond. The Discovery program's Dawn mission, when launched in mid-2007, will use ion drive as its primary propulsion system. Ion propulsion is one of only two technologies that appear feasible for early interstellar precursor missions with practical flight times. The Kepler mission will explore the structure and diversity of extrasolar planetary systems, with an emphasis on the detection of Earth-size planets around other stars. Kepler will survey nearby solar systems searching for planets that may fall within the habitable zone,' a region surrounding a star within which liquid water may exist on a planet's surface - an essential ingredient for life as we know it. With its open and competitive approach to mission selections, the Discovery program affords scientists the opportunity to propose missions to virtually any solar system destination. With its emphasis on science and proven openness to the use of new technologies such as ion propulsion, missions flown as part of the program will test out technologies needed for future very deep-space exploration and potentially take us to these difficult and distant destinations.

Johnson, Les↗
NASA NTRSDate not supplied

Investigating Neighbor Discovery for High-Rate Delay Tolerant Networking

A neighbor discovery protocol that enables the dynamic discovery of nodes for NASA Glenn Research Center’s High Rate Delay Tolerant Networking (HDTN) project is needed to satisfy mission scalability and interoperability objectives. The purpose of this study is to primarily examine a DTN IP neighbor discovery (IPND) implementation to understand the inner workings of the protocol before approaching a neighbor discovery implementation for the HDTN project. Using a rust-based DTN IPND implementation, this study analyzed the neighbor discovery process. DTN IPND was found to help meet both mission scalability and interoperability objectives by enabling dynamic routing capabilities. This study definitively answers the question regarding mission scalability and interoperability concerns. Further studies are needed to establish the most optimal neighbor discovery implementation for HDTN.

Prash Choksi↗
DOE OSTI2021

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Accelerating scientific discoveries through data-driven innovations

Developing artificial intelligence (AI) and machine learning (ML) methods that can accelerate scientific discoveries and advance science has become one of the important research directions for the AI/ML research community. It has been gaining increasing attention from researchers in diverse scientific areas, including biomedical science, materials science, climate science, physics, chemistry, and many others. Data-driven AI/ML innovations to enable reliable predictions and optimal decision making for scientific discoveries face several critical challenges, among which are high system complexity, large search space, incomplete knowledge, and small data, all of which demand novel strategies to effectively address them. Meeting these challenges and thereby accelerating scientific discoveries and industrial innovations, calls for research that can take full advantage of the latest advances in AI/ML to integrate data-driven techniques with scientific knowledge and is able to execute them in modern high-performance computing (HPC) environments at scale. This Patterns special collection "Accelerating scientific discoveries through data-driven innovations" features articles that showcase the promising roles of AI/ML and data-driven modeling in accelerating scientific discoveries and may inspire the next wave of data-driven innovations in various scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Computational catalyst discovery: Active classification through myopic multiscale sampling

We report the recent boom in computational chemistry has enabled several projects aimed at discovering useful materials or catalysts. We acknowledge and address two recurring issues in the field of computational catalyst discovery. First, calculating macro-scale catalyst properties is not straightforward when using ensembles of atomic-scale calculations [e.g., density functional theory (DFT)]. We attempt to address this issue by creating a multi-scale model that estimates bulk catalyst activity using adsorption energy predictions from both DFT and machine learning models. The second issue is that many catalyst discovery efforts seek to optimize catalyst properties, but optimization is an inherently exploitative objective that is in tension with the explorative nature of early-stage discovery projects. In other words, why invest so much time finding a “best” catalyst when it is likely to fail for some other, unforeseen problem? We address this issue by relaxing the catalyst discovery goal into a classification problem: “What is the set of catalysts that is worth testing experimentally?” Here, we present a catalyst discovery method called myopic multiscale sampling, which combines multiscale modeling with automated selection of DFT calculations. It is an active classification strategy that seeks to classify catalysts as “worth investigating” or “not worth investigating” experimentally. Our results show an ~7–16 times speedup in catalyst classification relative to random sampling. These results were based on offline simulations of our algorithm on two different datasets: a larger, synthesized dataset and a smaller, real dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Phase Mapper: Accelerating Materials Discovery with AI

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery

Computer Science↗
NASA NTRS1987

Discovery and problem solving: Triangulation as a weak heuristic

Recently the artificial intelligence community has turned its attention to the process of discovery and found that the history of science is a fertile source for what Darden has called compiled hindsight. Such hindsight generates weak heuristics for discovery that do not guarantee that discoveries will be made but do have proven worth in leading to discoveries. Triangulation is one such heuristic that is grounded in historical hindsight. This heuristic is explored within the general framework of the BACON, GLAUBER, STAHL, DALTON, and SUTTON programs. In triangulation different bases of information are compared in an effort to identify gaps between the bases. Thus, assuming that the bases of information are relevantly related, the gaps that are identified should be good locations for discovery and robust analysis.

Rochowiak, Daniel↗
NASA NTRS1994

Exobiology opportunities from Discovery-class missions

Discovery-class missions that are now planned, and those in the concept stage, have the potential to expand our knowledge of the origins and evolution of biogenic compounds, and ultimately, of the origins of life in the solar system. This class of missions, recently developed within NASA's Solar System Exploration Program, is designed to meet important scientific objectives within stringent guidelines--$150 million cap on development cost and a 3-year cap on the development schedule. The Discovery Program will effectively enable "faster, cheaper" missions to explore the inner solar system. The first two missions are Mars Environmental Survey (MESUR) Pathfinder and Near Earth Asteroid Rendezvous (NEAR). MESUR Pathfinder will be the first Discovery mission, with launch planned for November/December 1996. It will be primarily a technical demonstration and validation of the MESUR Program--a network of automated landers to study the internal structure, meteorology, and surface properties of Mars. Besides providing engineering data, Pathfinder will carry atmospheric instrumentation and imaging capabilities, and may deploy a microrover equipped with an alpha proton X-ray spectrometer to determine elemental composition, particularly the lighter elements of exobiological interest. NEAR is expected to be launched in 1998 and to rendezvous with a near-Earth asteroid for up to 1 year. During this time, the spacecraft will assess the asteroid's mass, size, density, map its surface topography and composition, determine its internal properties, and study its interaction with the interplanetary environment. A gamma ray or X-ray spectrometer will be used to determine elemental composition. An imaging spectrograph, with 0.35 to 2.5 micron spectral range, will be used to determine the asteroid's compositional disbribution. Of the 11 Discovery mission concepts that have been designated as warranting further study, several are promising in terms of determining the composition and chemical evolution of organic matter on small planetary bodies. The following mission concepts are of particular interest to the Exobiology Program: Cometary coma chemical composition, comet nucleus tour, near earth asteroid returned sample, small missions to asteroids and comets, and solar wind sample return. The following three Discovery mission concepts that have been targeted for further consideration are relevant to the study of the evolution of biogenic compounds: Comet nucleus penetrator, mainbelt asteroid rendezvous explorer, and the Mars polar Pathfinder.

Meyer, Michael A.↗
NASA NTRS2000

A Mars Exploration Discovery Program

The Mars Exploration Program should consider following the Discovery Program model. In the Discovery Program a team of scientists led by a PI develop the science goals of their mission, decide what payload achieves the necessary measurements most effectively, and then choose a spacecraft with the capabilities needed to carry the payload to the desired target body. The primary constraints associated with the Discovery missions are time and money. The proposer must convince reviewers that their mission has scientific merit and is feasible. Every Announcement of Opportunity has resulted in a collection of creative ideas that fit within advertised constraints. Following this model, a "Mars Discovery Program" would issue an Announcement of Opportunity for each launch opportunity with schedule constraints dictated by the launch window and fiscal constraints in accord with the program budget. All else would be left to the proposer to choose, based on the science the team wants to accomplish, consistent with the program theme of "Life, Climate and Resources". A proposer could propose a lander, an orbiter, a fleet of SCOUT vehicles or penetrators, an airplane, a balloon mission, a large rover, a small rover, etc. depending on what made the most sense for the science investigation and payload. As in the Discovery program, overall feasibility relative to cost, schedule and technology readiness would be evaluated and be part of the selection process.

Hansen, C. J.↗
NASA NTRSDate not supplied

Usage-Based Discovery of Earth Observations

Most providers of Earth Observation data enable search via dataset characteristics, (e.g., quantity being measured, instrument, location, and time). However, the Earth Science Information Partners Federation (ESIP) is attempting to improve dataset discovery by capturing information on how datasets are used. At a usage-based discovery hackfest at the July 2020 ESIP Summer Meeting, participants pooled their collective skills to implement a prototype based on the connections between datasets and data usage, which can reveal unanticipated patterns in dataset connectivity that are not apparent through traditional approaches. A survey of natural hazards websites, focusing on hydrology-related hazards such as floods and hydrology datasets such as rainfall, illustrated the difficulty of gleaning which dataset was actually used and how it was accessed. This difficulty highlights the need for greater transparency on dataset usage in website design. An application wireframe for a usage-based discovery database was conceptualized as a tool to help applications data specialists rapidly assemble a fit-for-purpose website as part of a disaster response, where customer feedback was integral to the manner in which the website would be developed and delivered. Capturing connections among datasets and instances of usage, either for research or applications, could help users identify datasets with a proven track record of utility or those that have been supplanted by more accurate and(or) precise representations. It may also help Earth Observation providers maximize returns on investments in data services. Social discovery, i.e, following discovery paths laid down by previous users of the data, could be a critical tool for improving the timeliness and applicability of dataset delivery at times and in circumstances where practitioners don’t have the capacity to evaluate the deluge of datasets that are returned by today’s search tools.

search↗
DOE OSTI2023

A New Drug Discovery Platform: Application to DNA Polymerase Eta and Apurinic/Apyrimidinic Endonuclease 1

The ability to quickly discover reliable hits from screening and rapidly convert them into lead compounds, which can be verified in functional assays, is central to drug discovery. The expedited validation of novel targets and the identification of modulators to advance to preclinical studies can significantly increase drug development success. Our SaXPyTM (“SAR by X-ray Poses Quickly”) platform, which is applicable to any X-ray crystallography-enabled drug target, couples the established methods of protein X-ray crystallography and fragment-based drug discovery (FBDD) with advanced computational and medicinal chemistry to deliver small molecule modulators or targeted protein degradation ligands in a short timeframe. Our approach, especially for elusive or “undruggable” targets, allows for (i) hit generation; (ii) the mapping of protein–ligand interactions; (iii) the assessment of target ligandability; (iv) the discovery of novel and potential allosteric binding sites; and (v) hit-to-lead execution. These advances inform chemical tractability and downstream biology and generate novel intellectual property. We describe here the application of SaXPy in the discovery and development of DNA damage response inhibitors against DNA polymerase eta (Pol η or POLH) and apurinic/apyrimidinic endonuclease 1 (APE1 or APEX1). Notably, our SaXPy platform allowed us to solve the first crystal structures of these proteins bound to small molecules and to discover novel binding sites for each target.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Hierarchical Gaussian process-based Bayesian optimization for materials discovery in high entropy alloy spaces

Bayesian optimization (BO) is a powerful and data-efficient method for iterative materials discovery and design, particularly valuable when prior knowledge is limited, underlying functional relationships are complex or unknown, and the cost of querying the materials space is significant. Traditional BO methodologies typically utilize conventional Gaussian Processes (cGPs) to model the relationships between material inputs and properties, as well as correlations within the input space. However, cGP-BO approaches often fall short in multi-objective optimization scenarios, where they are unable to fully exploit correlations between distinct material properties. Leveraging these correlations can significantly enhance the discovery process, as information about one property can inform and improve predictions about others. Here, this study addresses this limitation by employing advanced kernel structures to capture and model multi-dimensional property correlations through multi-task (MTGPs) or deep Gaussian Processes (DGPs), thus accelerating the discovery process. We demonstrate the effectiveness of MTGP-BO and DGP-BO in rapidly and robustly solving complex materials design challenges that occur within the context of complex multi-objective optimization over FCC FeCrNiCoCu high entropy alloy (HEA) spaces, where traditional cGP-BO approaches fail. Furthermore, we highlight how the differential costs associated with querying various material properties can be strategically leveraged to make the materials discovery process more cost-efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Mass Spectrometry-Based Approaches for Clinical Biomarker Discovery in Traumatic Brain Injury

Purpose of Review: Precision treatments to address the multifaceted pathophysiology of traumatic brain injury (TBI) are desperately needed, which has led to the intense study of fluid-based protein biomarkers in TBI. Mass Spectrometry (MS) is increasingly being applied to biomarker discovery and quantification in neurological disease to explore the proteome, allowing for more flexibility in biomarker discovery than commonly encountered antibody-based assays. In this narrative review, we will provide specific examples of how MS technology has advanced translational research in traumatic brain injury (TBI) focusing on clinical studies, and looking ahead to promising emerging applications of MS to the field of Neurocritical Care. Recent Findings: Proteomic biomarker discovery using MS technology in human subjects has included the full range of injury severity in TBI, though critically ill patients can offer more options to biofluids given the need for invasive monitoring. Blood, urine, cerebrospinal fluid, brain specimens, and cerebral extracellular fluid have all been sources for analysis. Emerging evidence suggests there are distinct proteomic profiles in radiographic TBI subtypes, and that biomarkers may be used to distinguish patients sustaining TBI from healthy controls. Metabolomics may offer a window into the perturbations of ongoing cerebral insults in critically ill patients after severe TBI. Summary: Emerging MS technologies may offer biomarker discovery and validation opportunities not afforded by conventional means due to its ability to handle the complexities associated with the proteome. In conclusion, while MS techniques are relatively early in development in the neurosciences space, the potential applications to TBI and neurocritical care are likely to accelerate in the coming decade.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of Multimolecular and Solvent-Inclusive Collective Variables

The typically rugged nature of molecular free energy landscapes can frustrate efficient sampling of the thermodynamically relevant phase space due to the presence of high free energy barriers. Enhanced sampling techniques can improve phase space exploration by accelerating sampling along particular collective variables (CVs). A number of techniques exist for data-driven discovery of CVs parameterizing the important large scale motions of the system. A challenge to CV discovery is learning CVs invariant to symmetries of the molecular system, frequently rigid translation, rigid rotation, and permutational relabeling of identical particles. Of these, permutational invariance has proved a persistent challenge in frustrating the data-driven discovery of multi-molecular CVs in systems of self-assembling particles and solvent-inclusive CVs for solvated systems. In this work, we integrate Permutation Invariant Vector (PIV) featurizations with autoencoding neural networks to learn nonlinear CVs invariant to translation, rotation, and permutation, and perform interleaved rounds of CV discovery and enhanced sampling to iteratively expand sampling of configurational phase space and obtain converged CVs and free energy landscapes. Here, we demonstrate the Permutationally Invariant Network for Enhanced Sampling (PINES) approach in applications to the self-assembly of a 13-atom Argon cluster, association/dissociation of a NaCl ion pair in water, and hydrophobic collapse of a C 45 H 92 n-pentatetracontane polymer chain. We make the approach freely available as a new module within the PLUMED2 enhanced sampling libraries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗