RESEARCH: FORCE-FIELD-PROTEIN-DYNAMICS
FOLDING PROJECT #18237 PROFILE
PROJECT TEAM
Manager(s): Justin MillerInstitution: University of Pennsylvania
WORK UNIT INFO
Atoms: 147,745Core: 0x24
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project tests different computer models used to simulate how proteins move. They use a well-studied protein called lysozyme to see which model is most accurate. The goal is to improve these simulations so scientists can better understand how proteins work.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
Force fields aren't only a thing in far off galaxies, but are also an integral part of molecular dynamics simulations.
Principally, molecular dynamics simulations are evaluating Newton's laws of motion iteratively.
Each atom in the simulation is given a position, velocity, and has some forces acting upon it.
We then take a short step forward in time (often 2-4 femtoseconds), update the positions of each atom based on the last known position, velocity, and acceleration, before re-evaluating the forces acting upon each atom.
Repeating this millions to trillions of times (or more), gives us a physics-based movie of atoms moving which we use to give insight into the behavior of our favorite proteins. One of the fundamental steps of this process is calculating the forces on each atom.
The collective model describing how to calculate these forces is called a force field.
Through the years, many force fields have been derived and refined, each one focusing on improving certain forces or behaviors of the simulation.
While tests are usually performed when force fields are redeveloped, it is difficult to achieve robust sampling (e.g.
many observations of rare events).
Here, we are continuing our efforts to catalog the performance and accuracy of these force fields.
In this project series, we use the well studied protein, T4 Lysozyme, as our test model.
Lysozyme is an antibacterial protein which destroys bacterial cell walls.
Lysozyme is an ideal system to use for evaluating force fields as many biophysical measurements have been performed on the system and several rare conformations (folds) of the protein have been observed.
We expect that our findings in this project series, along with the similar project series 18227-18230 and 18250-18255, will provide a strong benchmark to improve the accuracy in simulations, both on Folding@home as well as in the broader scientific community, to come. We are testing the following force field/water combinations in this project series.
If you are particularly excited about additional force field/water combinations, please reach out. 18235- Amber03 with TIP3P water 18236- Amber14sb with TIP3P water 18237- Amber19sb with OPC water 18238- Charmm36m with TIP3P water 18239- Amber99SB-star-ILDN with TIP4PD water 18240- Amber99SB-disp with TIP4PD-1.6 water.
RELATED TERMS GLOSSARY AI BETA
Force fields
A collective model describing how to calculate the forces acting on atoms in a molecular dynamics simulation.
Force fields are mathematical models used in computer simulations to represent the interactions between atoms. They are essential for accurately simulating the behavior of molecules and understanding their properties.
Molecular dynamics
A method for simulating the movement and interaction of atoms and molecules over time.
Molecular dynamics simulations use computer algorithms to model the behavior of molecules. By calculating the forces between atoms, these simulations can predict how molecules will move and interact, providing insights into chemical reactions, protein folding, and other biological processes.
Newton's laws of motion
A set of three fundamental principles that govern the motion of objects.
Newton's laws of motion describe how objects move and interact with forces. These laws are essential for understanding the behavior of everything from planets to projectiles.
Femtoseconds
A unit of time equal to 10^-15 seconds.
Femtoseconds are incredibly short units of time, used to measure the fastest events in physics and chemistry. They are often employed in studying ultrafast processes like light absorption and chemical reactions.
T4 Lysozyme
A well-studied bacteriolytic enzyme.
T4 Lysozyme is a type of protein that breaks down bacterial cell walls. It is widely used as a model system in biochemistry and molecular biology research due to its simplicity and well-characterized structure.
Folding@home
A distributed computing project that uses volunteer computer resources to perform simulations of protein folding.
Folding@home harnesses the power of thousands of computers to simulate the complex process of protein folding. This research helps scientists understand how proteins fold into their functional shapes and develop new drugs and therapies.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:31:05|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | GeForce RTX 5090 GB202 [GeForce RTX 5090] |
Nvidia | GB202 | 28,296,349 | 68,000 | 416.12 | 0 hrs 3 mins |
| 2 | GeForce RTX 4090 AD102 [GeForce RTX 4090] |
Nvidia | AD102 | 20,723,444 | 465,547 | 44.51 | 0 hrs 32 mins |
| 3 | GeForce RTX 5080 GB203 [GeForce RTX 5080] |
Nvidia | GB203 | 18,730,815 | 68,000 | 275.45 | 0 hrs 5 mins |
| 4 | GeForce RTX 4080 SUPER AD103 [GeForce RTX 4080 SUPER] |
Nvidia | AD103 | 16,332,403 | 323,171 | 50.54 | 0 hrs 28 mins |
| 5 | GeForce RTX 4080 AD103 [GeForce RTX 4080] |
Nvidia | AD103 | 15,131,579 | 524,226 | 28.86 | 0 hrs 50 mins |
| 6 | GeForce RTX 5070 Ti GB203 [GeForce RTX 5070 Ti] |
Nvidia | GB203 | 15,049,990 | 264,357 | 56.93 | 0 hrs 25 mins |
| 7 | GeForce RTX 4070 Ti AD104 [GeForce RTX 4070 Ti] |
Nvidia | AD104 | 13,219,484 | 513,615 | 25.74 | 0 hrs 56 mins |
| 8 | GeForce RTX 4070 SUPER AD104 [GeForce RTX 4070 SUPER] |
Nvidia | AD104 | 11,816,316 | 193,120 | 61.19 | 0 hrs 24 mins |
| 9 | GeForce RTX 4070 Ti SUPER AD103 [GeForce RTX 4070 Ti SUPER] |
Nvidia | AD103 | 11,296,049 | 301,818 | 37.43 | 0 hrs 38 mins |
| 10 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 8,859,857 | 127,016 | 69.75 | 0 hrs 21 mins |
| 11 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 8,716,966 | 252,295 | 34.55 | 0 hrs 42 mins |
| 12 | RTX 5000 Ada Generation Laptop GPU AD103GLM [RTX 5000 Ada Generation Laptop GPU] |
Nvidia | AD103GLM | 8,456,745 | 523,956 | 16.14 | 1 hrs 29 mins |
| 13 | GeForce RTX 4070 AD104 [GeForce RTX 4070] |
Nvidia | AD104 | 8,003,321 | 452,740 | 17.68 | 1 hrs 21 mins |
| 14 | GeForce RTX 3080 12GB GA102 [GeForce RTX 3080 12GB] |
Nvidia | GA102 | 7,144,852 | 495,732 | 14.41 | 1 hrs 40 mins |
| 15 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 6,956,423 | 410,902 | 16.93 | 1 hrs 25 mins |
| 16 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 6,498,519 | 407,433 | 15.95 | 1 hrs 30 mins |
| 17 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 6,403,853 | 165,191 | 38.77 | 0 hrs 37 mins |
| 18 | GeForce RTX 4060 Ti 16GB AD106 [GeForce RTX 4060 Ti 16GB] |
Nvidia | AD106 | 6,328,309 | 468,308 | 13.51 | 1 hrs 47 mins |
| 19 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 6,028,794 | 404,057 | 14.92 | 1 hrs 37 mins |
|
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| 20 | Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE] |
AMD | Navi 31 | 5,977,919 | 267,045 | 22.39 | 1 hrs 4 mins |
| 21 | TITAN V GV100 [TITAN V] M 12288 |
Nvidia | GV100 | 5,580,967 | 68,000 | 82.07 | 0 hrs 18 mins |
| 22 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 5,464,537 | 68,000 | 80.36 | 0 hrs 18 mins |
| 23 | Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT] |
AMD | Navi 21 | 5,296,463 | 173,632 | 30.50 | 0 hrs 47 mins |
| 24 | GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate] |
Nvidia | GA104 | 5,057,364 | 250,533 | 20.19 | 1 hrs 11 mins |
| 25 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 4,662,699 | 267,806 | 17.41 | 1 hrs 23 mins |
| 26 | GeForce RTX 4060 Ti AD106 [GeForce RTX 4060 Ti] |
Nvidia | AD106 | 4,656,035 | 419,229 | 11.11 | 2 hrs 10 mins |
| 27 | GeForce RTX 4070 Max-Q / Mobile AD106M [GeForce RTX 4070 Max-Q / Mobile] |
Nvidia | AD106M | 4,371,224 | 68,000 | 64.28 | 0 hrs 22 mins |
| 28 | Radeon RX 6800/6800XT/6900XT Navi 21 [Radeon RX 6800/6800XT/6900XT] |
AMD | Navi 21 | 3,874,913 | 277,441 | 13.97 | 1 hrs 43 mins |
| 29 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 3,720,672 | 377,744 | 9.85 | 2 hrs 26 mins |
| 30 | Radeon RX 7700XT/7800XT Navi 32 [Radeon RX 7700XT/7800XT] |
AMD | Navi 32 | 3,718,893 | 136,723 | 27.20 | 0 hrs 53 mins |
| 31 | GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti] |
Nvidia | GA104 | 3,673,629 | 126,005 | 29.15 | 0 hrs 49 mins |
| 32 | GeForce RTX 4060 AD107 [GeForce RTX 4060] |
Nvidia | AD107 | 3,432,021 | 287,839 | 11.92 | 2 hrs 1 mins |
| 33 | GeForce RTX 4060 AD106 [GeForce RTX 4060] |
Nvidia | AD106 | 3,289,634 | 381,594 | 8.62 | 2 hrs 47 mins |
| 34 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,251,296 | 358,334 | 9.07 | 2 hrs 39 mins |
| 35 | GeForce RTX 2070 TU106 [GeForce RTX 2070] |
Nvidia | TU106 | 2,935,839 | 293,875 | 9.99 | 2 hrs 24 mins |
| 36 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 2,743,163 | 275,469 | 9.96 | 2 hrs 25 mins |
| 37 | GeForce RTX 2080 Super TU104 [GeForce RTX 2080 Super] |
Nvidia | TU104 | 2,594,707 | 68,000 | 38.16 | 0 hrs 38 mins |
| 38 | GeForce RTX 3060 GA104 [GeForce RTX 3060] |
Nvidia | GA104 | 2,552,464 | 68,000 | 37.54 | 0 hrs 38 mins |
| 39 | GeForce RTX 4060 Max-Q / Mobile AD107M [GeForce RTX 4060 Max-Q / Mobile] |
Nvidia | AD107M | 2,548,823 | 351,116 | 7.26 | 3 hrs 18 mins |
|
|
|||||||
| 40 | Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT] |
AMD | Navi 21 | 2,522,837 | 250,477 | 10.07 | 2 hrs 23 mins |
| 41 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,451,102 | 326,773 | 7.50 | 3 hrs 12 mins |
| 42 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,415,780 | 345,313 | 7.00 | 3 hrs 26 mins |
| 43 | Radeon RX 6700/6700XT/6800M Navi 22 XT-XL [Radeon RX 6700/6700XT/6800M] |
AMD | Navi 22 XT-XL | 2,302,449 | 114,685 | 20.08 | 1 hrs 12 mins |
| 44 | GeForce RTX 2060 TU106 [Geforce RTX 2060] |
Nvidia | TU106 | 2,199,626 | 230,914 | 9.53 | 2 hrs 31 mins |
| 45 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,188,994 | 130,450 | 16.78 | 1 hrs 26 mins |
| 46 | GeForce RTX 3060 Mobile / Max-Q GA106M [GeForce RTX 3060 Mobile / Max-Q] |
Nvidia | GA106M | 2,188,414 | 306,721 | 7.13 | 3 hrs 22 mins |
| 47 | Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M] |
AMD | Navi 22 XT-XL | 2,089,840 | 68,000 | 30.73 | 0 hrs 47 mins |
| 48 | GeForce RTX 2070 Mobile / Max-Q Refresh TU106M [GeForce RTX 2070 Mobile / Max-Q Refresh] |
Nvidia | TU106M | 2,033,896 | 313,554 | 6.49 | 3 hrs 42 mins |
| 49 | Radeon RX 7700S/7600S Navi 33 [Radeon RX 7700S/7600S] |
AMD | Navi 33 | 1,833,441 | 68,000 | 26.96 | 0 hrs 53 mins |
| 50 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,760,661 | 215,863 | 8.16 | 2 hrs 57 mins |
| 51 | Radeon RX 6650XT Navi 23 [Radeon RX 6650XT] |
AMD | Navi 23 | 1,747,339 | 309,621 | 5.64 | 4 hrs 15 mins |
| 52 | GeForce GTX 1660 TU116 [GeForce GTX 1660] |
Nvidia | TU116 | 1,694,521 | 89,591 | 18.91 | 1 hrs 16 mins |
| 53 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,609,371 | 131,890 | 12.20 | 1 hrs 58 mins |
| 54 | GeForce GTX 1660 Ti TU116 [GeForce GTX 1660 Ti] |
Nvidia | TU116 | 1,580,554 | 68,000 | 23.24 | 1 hrs 2 mins |
| 55 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,574,991 | 300,925 | 5.23 | 4 hrs 35 mins |
| 56 | GeForce RTX 3050 8GB GA107 [GeForce RTX 3050 8GB] |
Nvidia | GA107 | 1,527,362 | 306,405 | 4.98 | 4 hrs 49 mins |
| 57 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,490,466 | 120,048 | 12.42 | 1 hrs 56 mins |
| 58 | P104-100 GP104 [P104-100] |
Nvidia | GP104 | 1,410,412 | 68,000 | 20.74 | 1 hrs 9 mins |
| 59 | GeForce GTX 1070 Ti GP104 [GeForce GTX 1070 Ti] 8186 |
Nvidia | GP104 | 1,251,063 | 248,524 | 5.03 | 4 hrs 46 mins |
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|
|||||||
| 60 | Geforce RTX 3050 GA106 [Geforce RTX 3050] |
Nvidia | GA106 | 1,202,592 | 68,000 | 17.69 | 1 hrs 21 mins |
| 61 | RTX A2000 12GB GA106 [RTX A2000 12GB] |
Nvidia | GA106 | 1,169,881 | 68,000 | 17.20 | 1 hrs 24 mins |
| 62 | Radeon Pro W5700 Navi 10 [Radeon Pro W5700] |
AMD | Navi 10 | 1,148,112 | 275,301 | 4.17 | 5 hrs 45 mins |
| 63 | Radeon RX 6600/6600 XT/6600M Navi 23 XT-XL [Radeon RX 6600/6600 XT/6600M] |
AMD | Navi 23 XT-XL | 1,114,932 | 186,180 | 5.99 | 4 hrs 0 mins |
| 64 | GeForce RTX 3050 Ti Mobile GA107M [GeForce RTX 3050 Ti Mobile] |
Nvidia | GA107M | 1,023,130 | 268,895 | 3.80 | 6 hrs 18 mins |
| 65 | P106-100 GP106 [P106-100] |
Nvidia | GP106 | 1,012,080 | 258,774 | 3.91 | 6 hrs 8 mins |
| 66 | RX 5600 OEM/5600XT/5700/5700XT Navi 10 [RX 5600 OEM/5600XT/5700/5700XT] |
AMD | Navi 10 | 996,008 | 200,993 | 4.96 | 4 hrs 51 mins |
| 67 | GeForce GTX 1060 6GB GP106 [GeForce GTX 1060 6GB] 4372 |
Nvidia | GP106 | 961,703 | 68,000 | 14.14 | 1 hrs 42 mins |
| 68 | GeForce RTX 3050 6GB GA107 [GeForce RTX 3050 6GB] |
Nvidia | GA107 | 930,320 | 68,000 | 13.68 | 1 hrs 45 mins |
| 69 | GeForce GTX 980 GM204 [GeForce GTX 980] 4612 |
Nvidia | GM204 | 883,745 | 68,000 | 13.00 | 1 hrs 51 mins |
| 70 | GeForce GTX 1660 Mobile TU116M [GeForce GTX 1660 Mobile] |
Nvidia | TU116M | 855,055 | 242,418 | 3.53 | 6 hrs 48 mins |
| 71 | GeForce GTX 1650 TU116 [GeForce GTX 1650] 3091 |
Nvidia | TU116 | 742,781 | 68,000 | 10.92 | 2 hrs 12 mins |
| 72 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 734,295 | 125,267 | 5.86 | 4 hrs 6 mins |
| 73 | Ryzen 7000 Series iGPU Raphael [Ryzen 7000 Series iGPU] |
AMD | Raphael | 689,325 | 73,786 | 9.34 | 2 hrs 34 mins |
| 74 | Quadro T1000 Mobile TU117GLM [Quadro T1000 Mobile] |
Nvidia | TU117GLM | 616,397 | 68,000 | 9.06 | 2 hrs 39 mins |
| 75 | RX 5500/5500M/Pro 5500M Navi 14 [RX 5500/5500M/Pro 5500M] |
AMD | Navi 14 | 603,226 | 219,001 | 2.75 | 8 hrs 43 mins |
| 76 | GeForce GTX 1650 Mobile / Max-Q TU117M [GeForce GTX 1650 Mobile / Max-Q] |
Nvidia | TU117M | 445,067 | 79,868 | 5.57 | 4 hrs 18 mins |
| 77 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 431,277 | 68,000 | 6.34 | 3 hrs 47 mins |
| 78 | GeForce GTX 1650 TU106 [GeForce GTX 1650] |
Nvidia | TU106 | 276,320 | 68,000 | 4.06 | 5 hrs 54 mins |
| 79 | Quadro P1000 GP107GL [Quadro P1000] |
Nvidia | GP107GL | 130,290 | 82,910 | 1.57 | 15 hrs 16 mins |
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| 80 | GeForce GT 1030 GP108 [GeForce GT 1030] |
Nvidia | GP108 | 106,695 | 68,000 | 1.57 | 15 hrs 18 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:31:05|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|---|---|---|---|---|
| 1 | RYZEN 9 5950X 16-CORE | 32 | AMD | ||
| 2 | CORE I9-14900K | 32 | Intel | ||
| 3 | CORE I9-14900KF | 24 | Intel |